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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Wczytanie potrzebnych bibliotek" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import os, re\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "import warnings\n", | |
| "from IPython.display import display" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Automatyczne wyszukiwanie dysku przenośnego potrzebnego do ścieżki do pliku z danymi.\n", | |
| "Wszystkich zainteresowanych zachęcam do wykorzystania udostępnionego na moim githubie zbioru danych, który możecie wykorzystać do zbudowania swojego własnego modelu." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "'L'" | |
| ] | |
| }, | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "discks_names = re.findall(r\"[A-Z]+:.*$\",os.popen(\"mountvol /\").read(),re.MULTILINE)\n", | |
| "\n", | |
| "a = []\n", | |
| "for i in range(len(discks_names)):\n", | |
| " b = discks_names[i].split(\":\")[0]\n", | |
| " a.append(b)\n", | |
| "\n", | |
| "dics = a['C' in a]\n", | |
| "dics = a[0]\n", | |
| "dics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Wybranie ligi z największym procentem remisów" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Na tą chwilę wyboru dokonuję wyłącznie między 5 analizowanych ligami na stronie www.analizadanychwpilce.com.\n", | |
| "Procent meczów zremisowanych w lidze na przestrzeni ostatnich 10 lat możecie podjrzeć na stronie: https://szydlinho.shinyapps.io/data_football/ w pierwszej zakładce aplikacji shiny.\n", | |
| "W ostatnich 10 latach procent zremisownych meczów w danej lidze przedstawia się nastepująco:\n", | |
| "Premier League: 24.72%,\n", | |
| "La Liga: 23.87%\n", | |
| "Serie A: 25.8%,\n", | |
| "Bundesliga: 24.69%,\n", | |
| "Ekstraklasa: 27.3% (od sezonu 2013/2014).\n", | |
| "\n", | |
| "Z racji na większą ilość danych wybieram ligę włoską, jednak w każdej z lig procent meczów zremisowanych jest względnie podobny i oscyluje w okolicach 25%.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Wczytanie oraz prezentacja zbioru danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HomeTeam</th>\n", | |
| " <th>AwayTeam</th>\n", | |
| " <th>FTHG</th>\n", | |
| " <th>FTAG</th>\n", | |
| " <th>FTR</th>\n", | |
| " <th>HTGS</th>\n", | |
| " <th>ATGS</th>\n", | |
| " <th>HTGC</th>\n", | |
| " <th>ATGC</th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>HM1</th>\n", | |
| " <th>HM2</th>\n", | |
| " <th>HM3</th>\n", | |
| " <th>HM4</th>\n", | |
| " <th>HM5</th>\n", | |
| " <th>AM1</th>\n", | |
| " <th>AM2</th>\n", | |
| " <th>AM3</th>\n", | |
| " <th>AM4</th>\n", | |
| " <th>AM5</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " <th>MW</th>\n", | |
| " <th>HomeTeamLP</th>\n", | |
| " <th>AwayTeamLP</th>\n", | |
| " <th>Sezon</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTFormPtsStr</th>\n", | |
| " <th>ATFormPtsStr</th>\n", | |
| " <th>HTFormPts</th>\n", | |
| " <th>ATFormPts</th>\n", | |
| " <th>HTWinStreak3</th>\n", | |
| " <th>HTWinStreak5</th>\n", | |
| " <th>HTLossStreak3</th>\n", | |
| " <th>HTLossStreak5</th>\n", | |
| " <th>HTDrawStreak3</th>\n", | |
| " <th>HTDrawStreak5</th>\n", | |
| " <th>ATWinStreak3</th>\n", | |
| " <th>ATWinStreak5</th>\n", | |
| " <th>ATLossStreak3</th>\n", | |
| " <th>ATLossStreak5</th>\n", | |
| " <th>ATDrawStreak3</th>\n", | |
| " <th>ATDrawStreak5</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>H2H_Home</th>\n", | |
| " <th>H2H_Away</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>temp</th>\n", | |
| " <th>Off_H</th>\n", | |
| " <th>Off_A</th>\n", | |
| " <th>Deff_H</th>\n", | |
| " <th>Deff_A</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Date</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2019-05-05</th>\n", | |
| " <td>Parma</td>\n", | |
| " <td>Sampdoria</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>D</td>\n", | |
| " <td>36.0</td>\n", | |
| " <td>57.0</td>\n", | |
| " <td>53.0</td>\n", | |
| " <td>48.0</td>\n", | |
| " <td>37</td>\n", | |
| " <td>48</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>9</td>\n", | |
| " <td>14</td>\n", | |
| " <td>15</td>\n", | |
| " <td>14</td>\n", | |
| " <td>10</td>\n", | |
| " <td>6</td>\n", | |
| " <td>35</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>10.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>15</td>\n", | |
| " <td>9</td>\n", | |
| " <td>DDDDL</td>\n", | |
| " <td>LLWLL</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-17.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>-11</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>LDL</td>\n", | |
| " <td>WDW</td>\n", | |
| " <td>1</td>\n", | |
| " <td>7</td>\n", | |
| " <td>377</td>\n", | |
| " <td>0.400000</td>\n", | |
| " <td>0.892857</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.776398</td>\n", | |
| " <td>0.465839</td>\n", | |
| " <td>1.044643</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-05</th>\n", | |
| " <td>Sassuolo</td>\n", | |
| " <td>Frosinone</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>D</td>\n", | |
| " <td>49.0</td>\n", | |
| " <td>28.0</td>\n", | |
| " <td>54.0</td>\n", | |
| " <td>60.0</td>\n", | |
| " <td>41</td>\n", | |
| " <td>23</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>W</td>\n", | |
| " <td>9</td>\n", | |
| " <td>5</td>\n", | |
| " <td>11</td>\n", | |
| " <td>21</td>\n", | |
| " <td>14</td>\n", | |
| " <td>8</td>\n", | |
| " <td>35</td>\n", | |
| " <td>11.0</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>10</td>\n", | |
| " <td>19</td>\n", | |
| " <td>WDDDW</td>\n", | |
| " <td>LLLWW</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-5.0</td>\n", | |
| " <td>-32.0</td>\n", | |
| " <td>18</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>-5.0</td>\n", | |
| " <td>DWW</td>\n", | |
| " <td>DLL</td>\n", | |
| " <td>7</td>\n", | |
| " <td>1</td>\n", | |
| " <td>377</td>\n", | |
| " <td>1.200000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.800000</td>\n", | |
| " <td>1.170000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-05</th>\n", | |
| " <td>Genoa</td>\n", | |
| " <td>Roma</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>D</td>\n", | |
| " <td>35.0</td>\n", | |
| " <td>61.0</td>\n", | |
| " <td>53.0</td>\n", | |
| " <td>47.0</td>\n", | |
| " <td>35</td>\n", | |
| " <td>58</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>8</td>\n", | |
| " <td>16</td>\n", | |
| " <td>15</td>\n", | |
| " <td>8</td>\n", | |
| " <td>11</td>\n", | |
| " <td>10</td>\n", | |
| " <td>35</td>\n", | |
| " <td>12.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>16</td>\n", | |
| " <td>5</td>\n", | |
| " <td>DLLDL</td>\n", | |
| " <td>WDWWD</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>11.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-18.0</td>\n", | |
| " <td>14.0</td>\n", | |
| " <td>-23</td>\n", | |
| " <td>-9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>DLL</td>\n", | |
| " <td>DWW</td>\n", | |
| " <td>1</td>\n", | |
| " <td>7</td>\n", | |
| " <td>377</td>\n", | |
| " <td>0.333333</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.232143</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.500000</td>\n", | |
| " <td>2.611607</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-05</th>\n", | |
| " <td>Napoli</td>\n", | |
| " <td>Cagliari</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>H</td>\n", | |
| " <td>64.0</td>\n", | |
| " <td>33.0</td>\n", | |
| " <td>30.0</td>\n", | |
| " <td>47.0</td>\n", | |
| " <td>70</td>\n", | |
| " <td>40</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>21</td>\n", | |
| " <td>10</td>\n", | |
| " <td>6</td>\n", | |
| " <td>14</td>\n", | |
| " <td>7</td>\n", | |
| " <td>10</td>\n", | |
| " <td>35</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>2</td>\n", | |
| " <td>12</td>\n", | |
| " <td>WLWDL</td>\n", | |
| " <td>LWDWL</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>34.0</td>\n", | |
| " <td>-14.0</td>\n", | |
| " <td>30</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>-14.0</td>\n", | |
| " <td>WWW</td>\n", | |
| " <td>LLL</td>\n", | |
| " <td>9</td>\n", | |
| " <td>0</td>\n", | |
| " <td>377</td>\n", | |
| " <td>1.166667</td>\n", | |
| " <td>1.041667</td>\n", | |
| " <td>1.607143</td>\n", | |
| " <td>1.449275</td>\n", | |
| " <td>2.536232</td>\n", | |
| " <td>1.958705</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019-05-06</th>\n", | |
| " <td>Milan</td>\n", | |
| " <td>Bologna</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>H</td>\n", | |
| " <td>47.0</td>\n", | |
| " <td>38.0</td>\n", | |
| " <td>31.0</td>\n", | |
| " <td>49.0</td>\n", | |
| " <td>56</td>\n", | |
| " <td>37</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>15</td>\n", | |
| " <td>9</td>\n", | |
| " <td>8</td>\n", | |
| " <td>15</td>\n", | |
| " <td>11</td>\n", | |
| " <td>10</td>\n", | |
| " <td>35</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>15.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>6</td>\n", | |
| " <td>14</td>\n", | |
| " <td>LDWLD</td>\n", | |
| " <td>WWDWL</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>10.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>-11.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>-5.0</td>\n", | |
| " <td>-9.0</td>\n", | |
| " <td>WWD</td>\n", | |
| " <td>LLD</td>\n", | |
| " <td>7</td>\n", | |
| " <td>1</td>\n", | |
| " <td>377</td>\n", | |
| " <td>1.066667</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.600000</td>\n", | |
| " <td>1.170000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HomeTeam AwayTeam FTHG FTAG FTR HTGS ATGS HTGC ATGC HTP \\\n", | |
| "Date \n", | |
| "2019-05-05 Parma Sampdoria 3.0 3.0 D 36.0 57.0 53.0 48.0 37 \n", | |
| "2019-05-05 Sassuolo Frosinone 2.0 2.0 D 49.0 28.0 54.0 60.0 41 \n", | |
| "2019-05-05 Genoa Roma 1.0 1.0 D 35.0 61.0 53.0 47.0 35 \n", | |
| "2019-05-05 Napoli Cagliari 2.0 1.0 H 64.0 33.0 30.0 47.0 70 \n", | |
| "2019-05-06 Milan Bologna 2.0 1.0 H 47.0 38.0 31.0 49.0 56 \n", | |
| "\n", | |
| " ATP HM1 HM2 HM3 HM4 HM5 AM1 AM2 AM3 AM4 AM5 HT_Wins AT_Wins \\\n", | |
| "Date \n", | |
| "2019-05-05 48 D D D D L L L W L L 9 14 \n", | |
| "2019-05-05 23 W D D D W L L L W W 9 5 \n", | |
| "2019-05-05 58 D L L D L W D W W D 8 16 \n", | |
| "2019-05-05 40 W L W D L L W D W L 21 10 \n", | |
| "2019-05-06 37 L D W L D W W D W L 15 9 \n", | |
| "\n", | |
| " HT_Loss AT_Loss HT_Draws AT_Draws MW HomeTeamLP AwayTeamLP \\\n", | |
| "Date \n", | |
| "2019-05-05 15 14 10 6 35 16.0 10.0 \n", | |
| "2019-05-05 11 21 14 8 35 11.0 16.0 \n", | |
| "2019-05-05 15 8 11 10 35 12.0 3.0 \n", | |
| "2019-05-05 6 14 7 10 35 2.0 16.0 \n", | |
| "2019-05-06 8 15 11 10 35 6.0 15.0 \n", | |
| "\n", | |
| " Sezon HT_LP AT_LP HTFormPtsStr ATFormPtsStr HTFormPts \\\n", | |
| "Date \n", | |
| "2019-05-05 19 15 9 DDDDL LLWLL 4.0 \n", | |
| "2019-05-05 19 10 19 WDDDW LLLWW 9.0 \n", | |
| "2019-05-05 19 16 5 DLLDL WDWWD 2.0 \n", | |
| "2019-05-05 19 2 12 WLWDL LWDWL 7.0 \n", | |
| "2019-05-06 19 6 14 LDWLD WWDWL 5.0 \n", | |
| "\n", | |
| " ATFormPts HTWinStreak3 HTWinStreak5 HTLossStreak3 \\\n", | |
| "Date \n", | |
| "2019-05-05 3.0 0 0 0 \n", | |
| "2019-05-05 6.0 0 0 0 \n", | |
| "2019-05-05 11.0 0 0 0 \n", | |
| "2019-05-05 7.0 0 0 0 \n", | |
| "2019-05-06 10.0 0 0 0 \n", | |
| "\n", | |
| " HTLossStreak5 HTDrawStreak3 HTDrawStreak5 ATWinStreak3 \\\n", | |
| "Date \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-06 0 0 0 0 \n", | |
| "\n", | |
| " ATWinStreak5 ATLossStreak3 ATLossStreak5 ATDrawStreak3 \\\n", | |
| "Date \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-05 0 0 0 0 \n", | |
| "2019-05-06 0 0 0 0 \n", | |
| "\n", | |
| " ATDrawStreak5 HTGD ATGD DiffPts DiffFormPts DiffLP H2H_Home \\\n", | |
| "Date \n", | |
| "2019-05-05 0 -17.0 9.0 -11 1.0 6.0 LDL \n", | |
| "2019-05-05 0 -5.0 -32.0 18 3.0 -5.0 DWW \n", | |
| "2019-05-05 0 -18.0 14.0 -23 -9.0 9.0 DLL \n", | |
| "2019-05-05 0 34.0 -14.0 30 0.0 -14.0 WWW \n", | |
| "2019-05-06 0 16.0 -11.0 19 -5.0 -9.0 WWD \n", | |
| "\n", | |
| " H2H_Away H2H_Home_pts H2H_Away_pts temp Off_H Off_A \\\n", | |
| "Date \n", | |
| "2019-05-05 WDW 1 7 377 0.400000 0.892857 \n", | |
| "2019-05-05 DLL 7 1 377 1.200000 1.000000 \n", | |
| "2019-05-05 DWW 1 7 377 0.333333 1.000000 \n", | |
| "2019-05-05 LLL 9 0 377 1.166667 1.041667 \n", | |
| "2019-05-06 LLD 7 1 377 1.066667 1.000000 \n", | |
| "\n", | |
| " Deff_H Deff_A Mean_home_goals Mean_away_goals \n", | |
| "Date \n", | |
| "2019-05-05 1.000000 0.776398 0.465839 1.044643 \n", | |
| "2019-05-05 1.000000 1.000000 1.800000 1.170000 \n", | |
| "2019-05-05 2.232143 1.000000 0.500000 2.611607 \n", | |
| "2019-05-05 1.607143 1.449275 2.536232 1.958705 \n", | |
| "2019-05-06 1.000000 1.000000 1.600000 1.170000 " | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pd.options.display.max_columns = None\n", | |
| "loc = dics + \":/data_football/seriaa/\"\n", | |
| "file = \"seriaa_final_log\"\n", | |
| "dataset = pd.read_csv(loc + file+ '.csv', sep = ';', index_col = 0)\n", | |
| "dataset = dataset[:-10]\n", | |
| "dataset.tail(5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Większość zmiennych wykorzystanych w zbiorze danych opisanych zostało w:\n", | |
| "\n", | |
| "https://analizadanychwpilce.com/2018/09/15/przewidywanie-wyniku-spotkania-z-wykorzystaniem-regresji-logistycznej/\n", | |
| "oraz\n", | |
| "\n", | |
| "https://analizadanychwpilce.com/2019/02/03/przewidywany-wynik-na-podstawie-rozkladu-poissona/\n", | |
| "\n", | |
| "Dodtkowymi zmiennymi są:\n", | |
| "HT_Wins - Ilość wygranych meczów drużyny gospodarzy w danym sezonie,\n", | |
| "AT_Wins - Ilość wygranych meczów drużyny gości w danym sezonie,\t\n", | |
| "HT_Loss - Ilość przegranych meczów drużyny gospodarzy w danym sezonie,\t\n", | |
| "AT_Loss - Ilość przegranych meczów drużyny gospodarzy w danym sezonie, \t\n", | |
| "HT_Draws - Ilość remisów meczów drużyny gospodarzy w danym sezonie, \t\n", | |
| "AT_Draws - Ilość remisów meczów drużyny gospodarzy w danym sezonie.\n", | |
| "\n", | |
| "Duże nadzieje pokładam szczególnie w dwóch ostatnich wypisanych zmiennych, gdyż na wielu stronach znalazłem zachcające artykuły dotyczące typowani remisów na drużyny, które mają największy procent remisó w sezonie. Przyznam, że taka hipotza wydaje się możliwa dlatego jestem bardzo ciekawy jak zinterpetuje to model." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Na etapie przygotowywania zbioru danych powstało kilka wierszy, które przyjmowały kategorię pomocniczą 'M'. \n", | |
| "W tym momencie powinniśmy pozbyć się tych obserwaci ze zbioru zbioru danych." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array(['L', 'W', 'D'], dtype=object)" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dataset = dataset.loc[(dataset[\"HM1\"] != \"M\") & \n", | |
| " (dataset[\"AM1\"] != \"M\") & \n", | |
| " (dataset[\"AM2\"] != \"M\") &\n", | |
| " (dataset[\"HM2\"] != \"M\") & \n", | |
| " (dataset[\"HM3\"] != \"M\") & \n", | |
| " (dataset[\"AM3\"] != \"M\") &\n", | |
| " (dataset[\"HM4\"] != \"M\") & \n", | |
| " (dataset[\"HM4\"] != \"M\") ]\n", | |
| "dataset.HM1.unique()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Każda z wymienionych powyżej zmiennych powinna posiadać tylko 3 kategorie: A, D, H." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Dodanie dodatkowych zmiennych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Jako że z wcześniejszych analiz doszliśmy do wniosku, że zmienna dotycząca wartości drużyny bardzo często jest zmienną istotną w modelu predykji wyniku spotkania piłkarskiego. Dane te zostaną złączone z bazowym zbiorem danych.\n", | |
| "Wartości zostały pobrane ze strony: https://www.transfermarkt.com/" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Dane dotyczące wartości drużyny" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HomeTeam</th>\n", | |
| " <th>AwayTeam</th>\n", | |
| " <th>FTHG</th>\n", | |
| " <th>FTAG</th>\n", | |
| " <th>FTR</th>\n", | |
| " <th>HTGS</th>\n", | |
| " <th>ATGS</th>\n", | |
| " <th>HTGC</th>\n", | |
| " <th>ATGC</th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>HM1</th>\n", | |
| " <th>HM2</th>\n", | |
| " <th>HM3</th>\n", | |
| " <th>HM4</th>\n", | |
| " <th>HM5</th>\n", | |
| " <th>AM1</th>\n", | |
| " <th>AM2</th>\n", | |
| " <th>AM3</th>\n", | |
| " <th>AM4</th>\n", | |
| " <th>AM5</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " <th>MW</th>\n", | |
| " <th>HomeTeamLP</th>\n", | |
| " <th>AwayTeamLP</th>\n", | |
| " <th>Sezon</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTFormPtsStr</th>\n", | |
| " <th>ATFormPtsStr</th>\n", | |
| " <th>HTFormPts</th>\n", | |
| " <th>ATFormPts</th>\n", | |
| " <th>HTWinStreak3</th>\n", | |
| " <th>HTWinStreak5</th>\n", | |
| " <th>HTLossStreak3</th>\n", | |
| " <th>HTLossStreak5</th>\n", | |
| " <th>HTDrawStreak3</th>\n", | |
| " <th>HTDrawStreak5</th>\n", | |
| " <th>ATWinStreak3</th>\n", | |
| " <th>ATWinStreak5</th>\n", | |
| " <th>ATLossStreak3</th>\n", | |
| " <th>ATLossStreak5</th>\n", | |
| " <th>ATDrawStreak3</th>\n", | |
| " <th>ATDrawStreak5</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>H2H_Home</th>\n", | |
| " <th>H2H_Away</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>temp</th>\n", | |
| " <th>Off_H</th>\n", | |
| " <th>Off_A</th>\n", | |
| " <th>Deff_H</th>\n", | |
| " <th>Deff_A</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>Club_x</th>\n", | |
| " <th>Age_H</th>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <th>Club_y</th>\n", | |
| " <th>Age_A</th>\n", | |
| " <th>Total_value_A</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>4045</th>\n", | |
| " <td>Juventus</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>H</td>\n", | |
| " <td>64.0</td>\n", | |
| " <td>41.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>40.0</td>\n", | |
| " <td>84</td>\n", | |
| " <td>40</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>W</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>27</td>\n", | |
| " <td>8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>8</td>\n", | |
| " <td>3</td>\n", | |
| " <td>16</td>\n", | |
| " <td>33</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>1</td>\n", | |
| " <td>10</td>\n", | |
| " <td>LWWWL</td>\n", | |
| " <td>DLDDL</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>43.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>44</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>-7.0</td>\n", | |
| " <td>WWW</td>\n", | |
| " <td>LLL</td>\n", | |
| " <td>9</td>\n", | |
| " <td>0</td>\n", | |
| " <td>375</td>\n", | |
| " <td>1.689189</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.040816</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.500000</td>\n", | |
| " <td>2.367347</td>\n", | |
| " <td>Juventus</td>\n", | |
| " <td>28.2</td>\n", | |
| " <td>768.50</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>24.1</td>\n", | |
| " <td>221.8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4046</th>\n", | |
| " <td>Roma</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>D</td>\n", | |
| " <td>54.0</td>\n", | |
| " <td>40.0</td>\n", | |
| " <td>45.0</td>\n", | |
| " <td>37.0</td>\n", | |
| " <td>47</td>\n", | |
| " <td>38</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>13</td>\n", | |
| " <td>8</td>\n", | |
| " <td>8</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>14</td>\n", | |
| " <td>30</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>7</td>\n", | |
| " <td>10</td>\n", | |
| " <td>LLWLW</td>\n", | |
| " <td>DLDLD</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>9</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>-5.0</td>\n", | |
| " <td>WLD</td>\n", | |
| " <td>LWD</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>372</td>\n", | |
| " <td>1.195973</td>\n", | |
| " <td>1.176471</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.184211</td>\n", | |
| " <td>2.131579</td>\n", | |
| " <td>1.580511</td>\n", | |
| " <td>Roma</td>\n", | |
| " <td>26.7</td>\n", | |
| " <td>368.80</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>24.1</td>\n", | |
| " <td>221.8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4047</th>\n", | |
| " <td>Spal</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>A</td>\n", | |
| " <td>20.0</td>\n", | |
| " <td>31.0</td>\n", | |
| " <td>36.0</td>\n", | |
| " <td>25.0</td>\n", | |
| " <td>22</td>\n", | |
| " <td>32</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>5</td>\n", | |
| " <td>7</td>\n", | |
| " <td>11</td>\n", | |
| " <td>5</td>\n", | |
| " <td>7</td>\n", | |
| " <td>11</td>\n", | |
| " <td>24</td>\n", | |
| " <td>17.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>14</td>\n", | |
| " <td>10</td>\n", | |
| " <td>LDWDL</td>\n", | |
| " <td>DDWDD</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-16.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>-10</td>\n", | |
| " <td>-2.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>DDL</td>\n", | |
| " <td>DDW</td>\n", | |
| " <td>2</td>\n", | |
| " <td>5</td>\n", | |
| " <td>366</td>\n", | |
| " <td>0.454545</td>\n", | |
| " <td>1.684211</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.125000</td>\n", | |
| " <td>0.675000</td>\n", | |
| " <td>2.054737</td>\n", | |
| " <td>Spal</td>\n", | |
| " <td>26.9</td>\n", | |
| " <td>57.05</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>24.1</td>\n", | |
| " <td>221.8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4048</th>\n", | |
| " <td>Empoli</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>H</td>\n", | |
| " <td>42.0</td>\n", | |
| " <td>41.0</td>\n", | |
| " <td>66.0</td>\n", | |
| " <td>42.0</td>\n", | |
| " <td>29</td>\n", | |
| " <td>40</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>W</td>\n", | |
| " <td>L</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>19</td>\n", | |
| " <td>10</td>\n", | |
| " <td>8</td>\n", | |
| " <td>16</td>\n", | |
| " <td>35</td>\n", | |
| " <td>16.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>18</td>\n", | |
| " <td>11</td>\n", | |
| " <td>LLDLW</td>\n", | |
| " <td>LLDLD</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-24.0</td>\n", | |
| " <td>-1.0</td>\n", | |
| " <td>-11</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>LWL</td>\n", | |
| " <td>WLW</td>\n", | |
| " <td>3</td>\n", | |
| " <td>6</td>\n", | |
| " <td>377</td>\n", | |
| " <td>1.500000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.250000</td>\n", | |
| " <td>1.170000</td>\n", | |
| " <td>Empoli</td>\n", | |
| " <td>26.5</td>\n", | |
| " <td>37.90</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>24.1</td>\n", | |
| " <td>221.8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4049</th>\n", | |
| " <td>Sassuolo</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>D</td>\n", | |
| " <td>23.0</td>\n", | |
| " <td>15.0</td>\n", | |
| " <td>22.0</td>\n", | |
| " <td>15.0</td>\n", | |
| " <td>20</td>\n", | |
| " <td>18</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>W</td>\n", | |
| " <td>D</td>\n", | |
| " <td>L</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>D</td>\n", | |
| " <td>5</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>5</td>\n", | |
| " <td>6</td>\n", | |
| " <td>15</td>\n", | |
| " <td>11.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>19</td>\n", | |
| " <td>8</td>\n", | |
| " <td>12</td>\n", | |
| " <td>DLDWD</td>\n", | |
| " <td>LDDDD</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>DLW</td>\n", | |
| " <td>DWL</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>357</td>\n", | |
| " <td>0.918182</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.247525</td>\n", | |
| " <td>Sassuolo</td>\n", | |
| " <td>25.2</td>\n", | |
| " <td>120.43</td>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>24.1</td>\n", | |
| " <td>221.8</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HomeTeam AwayTeam FTHG FTAG FTR HTGS ATGS HTGC ATGC HTP ATP \\\n", | |
| "4045 Juventus Fiorentina 2.0 1.0 H 64.0 41.0 21.0 40.0 84 40 \n", | |
| "4046 Roma Fiorentina 2.0 2.0 D 54.0 40.0 45.0 37.0 47 38 \n", | |
| "4047 Spal Fiorentina 1.0 4.0 A 20.0 31.0 36.0 25.0 22 32 \n", | |
| "4048 Empoli Fiorentina 1.0 0.0 H 42.0 41.0 66.0 42.0 29 40 \n", | |
| "4049 Sassuolo Fiorentina 3.0 3.0 D 23.0 15.0 22.0 15.0 20 18 \n", | |
| "\n", | |
| " HM1 HM2 HM3 HM4 HM5 AM1 AM2 AM3 AM4 AM5 HT_Wins AT_Wins HT_Loss \\\n", | |
| "4045 L W W W L D L D D L 27 8 2 \n", | |
| "4046 L L W L W D L D L D 13 8 8 \n", | |
| "4047 L D W D L D D W D D 5 7 11 \n", | |
| "4048 L L D L W L L D L D 7 8 19 \n", | |
| "4049 D L D W D L D D D D 5 4 4 \n", | |
| "\n", | |
| " AT_Loss HT_Draws AT_Draws MW HomeTeamLP AwayTeamLP Sezon HT_LP \\\n", | |
| "4045 8 3 16 33 1.0 8.0 19 1 \n", | |
| "4046 7 8 14 30 3.0 8.0 19 7 \n", | |
| "4047 5 7 11 24 17.0 8.0 19 14 \n", | |
| "4048 10 8 16 35 16.0 8.0 19 18 \n", | |
| "4049 4 5 6 15 11.0 8.0 19 8 \n", | |
| "\n", | |
| " AT_LP HTFormPtsStr ATFormPtsStr HTFormPts ATFormPts HTWinStreak3 \\\n", | |
| "4045 10 LWWWL DLDDL 9.0 3.0 0 \n", | |
| "4046 10 LLWLW DLDLD 6.0 3.0 0 \n", | |
| "4047 10 LDWDL DDWDD 5.0 7.0 0 \n", | |
| "4048 11 LLDLW LLDLD 4.0 2.0 0 \n", | |
| "4049 12 DLDWD LDDDD 6.0 4.0 0 \n", | |
| "\n", | |
| " HTWinStreak5 HTLossStreak3 HTLossStreak5 HTDrawStreak3 \\\n", | |
| "4045 0 0 0 0 \n", | |
| "4046 0 0 0 0 \n", | |
| "4047 0 0 0 0 \n", | |
| "4048 0 0 0 0 \n", | |
| "4049 0 0 0 0 \n", | |
| "\n", | |
| " HTDrawStreak5 ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 \\\n", | |
| "4045 0 0 0 0 0 \n", | |
| "4046 0 0 0 0 0 \n", | |
| "4047 0 0 0 0 0 \n", | |
| "4048 0 0 0 0 0 \n", | |
| "4049 0 0 0 0 0 \n", | |
| "\n", | |
| " ATDrawStreak3 ATDrawStreak5 HTGD ATGD DiffPts DiffFormPts DiffLP \\\n", | |
| "4045 0 0 43.0 1.0 44 6.0 -7.0 \n", | |
| "4046 0 0 9.0 3.0 9 3.0 -5.0 \n", | |
| "4047 0 0 -16.0 6.0 -10 -2.0 9.0 \n", | |
| "4048 0 0 -24.0 -1.0 -11 2.0 8.0 \n", | |
| "4049 1 0 1.0 0.0 2 2.0 3.0 \n", | |
| "\n", | |
| " H2H_Home H2H_Away H2H_Home_pts H2H_Away_pts temp Off_H Off_A \\\n", | |
| "4045 WWW LLL 9 0 375 1.689189 1.000000 \n", | |
| "4046 WLD LWD 4 4 372 1.195973 1.176471 \n", | |
| "4047 DDL DDW 2 5 366 0.454545 1.684211 \n", | |
| "4048 LWL WLW 3 6 377 1.500000 1.000000 \n", | |
| "4049 DLW DWL 4 4 357 0.918182 1.000000 \n", | |
| "\n", | |
| " Deff_H Deff_A Mean_home_goals Mean_away_goals Club_x Age_H \\\n", | |
| "4045 2.040816 1.000000 2.500000 2.367347 Juventus 28.2 \n", | |
| "4046 1.000000 1.184211 2.131579 1.580511 Roma 26.7 \n", | |
| "4047 1.000000 1.125000 0.675000 2.054737 Spal 26.9 \n", | |
| "4048 1.000000 1.000000 2.250000 1.170000 Empoli 26.5 \n", | |
| "4049 1.000000 1.000000 1.400000 1.247525 Sassuolo 25.2 \n", | |
| "\n", | |
| " Total_value_H Club_y Age_A Total_value_A \n", | |
| "4045 768.50 Fiorentina 24.1 221.8 \n", | |
| "4046 368.80 Fiorentina 24.1 221.8 \n", | |
| "4047 57.05 Fiorentina 24.1 221.8 \n", | |
| "4048 37.90 Fiorentina 24.1 221.8 \n", | |
| "4049 120.43 Fiorentina 24.1 221.8 " | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pd.options.display.max_columns = None\n", | |
| "marketv = pd.read_csv(loc + 'budget_seriaa_final.csv', index_col = 0)\n", | |
| "\n", | |
| "marketv.Sezon = marketv.Sezon.astype(str).str[2:]\n", | |
| "marketv[\"Sezon\"] = marketv[\"Sezon\"].astype(int) + 1\n", | |
| "\n", | |
| "dataset = pd.merge(dataset, marketv, how='inner', \n", | |
| " left_on=['HomeTeam','Sezon'], \n", | |
| " right_on = ['Club','Sezon'], sort=False)\n", | |
| "\n", | |
| "dataset = dataset.rename(columns={'Age': 'Age_H', \n", | |
| " 'Foreign': 'Foreign_H',\n", | |
| " 'Total_value': 'Total_value_H',\n", | |
| " 'Market_value': 'Market_value_H'})\n", | |
| "\n", | |
| "dataset = pd.merge(dataset, marketv, how='inner', \n", | |
| " left_on=['AwayTeam','Sezon'], \n", | |
| " right_on = ['Club','Sezon'], \n", | |
| " sort=False)\n", | |
| "\n", | |
| "dataset = dataset.rename(columns={'Age': 'Age_A', \n", | |
| " 'Foreign': 'Foreign_A',\n", | |
| " 'Total_value': 'Total_value_A',\n", | |
| " 'Market_value': 'Market_value_A'})\n", | |
| "\n", | |
| "dataset.tail()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Dodatkowe zmienne to Age_H ora Age_A, czyli średni wiek zawodników w drużynie odpowiednio gospdoarzy i gości.\n", | |
| "Foreign_H oraz Foreign_A to ilośc zawodników zagrancizncyh w drużynie odpowiednio gospdoarzy i gości.\n", | |
| "Total_value_H oraz Total_value_A czyli wartość pierwszego zespołu odpowiednio goposdarzy i gości.\n", | |
| "Market_value_H oraz Market_value_A czyli wartość rynkowa drużyny odpowiednio goposdarzy i gości.\n", | |
| "\n", | |
| "Wszystkie zmienne dotyczące wartości klubów podawane są milonach Euro." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Stworzenie zmiennej wynikowej 'draw'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df_final = dataset\n", | |
| "warnings.simplefilter('ignore')\n", | |
| "df_final['draw'] = np.where(df_final['FTR']=='D', 1, 0)\n", | |
| "df_final['draw']=(df_final['draw']==1).astype(int)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zmienna 'draw' przyjmuje wartość 1 w przypadku remisu, 0 w przeciwym wypadku.\n", | |
| "Przekonwertowana została również do typu integer. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Modyfikacja wbranych kolumn" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Konwersja wybranych kolumn do typu kategorycznego" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wybrane zmienne zostaną przekonwertowane do zmiennych typu kategorycznego.\n", | |
| "Wystarczy konwersja za pomoca .astype(), gdyż wszystkie wybrane zmienne przyjmują wyłącznie dwie kategorie (1/0)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "warnings.simplefilter('ignore')\n", | |
| "cols_cat = [ 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3',\n", | |
| " 'HTLossStreak5', 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3',\n", | |
| " 'ATLossStreak5', 'draw']\n", | |
| "df_final[cols_cat] = df_final[cols_cat].astype('category')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zmienne przyjmujące więcej niż dwie kategorie zostaną sprowadzone do zmiennych pomocniczych (dummy veriables) przyjmujących tylko dwie kategorie.\n", | |
| "Przykładowo zmienna 'HM1' przyjmująca kategorie: A, H oraz D, zostanie \"rozbita\" na 3 zmienne: HM1_A, HM1_H, HM1_D.Każda z nich przyjmuje wyłącznie dwie kategorie 0/1." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array(['HomeTeam', 'AwayTeam', 'FTHG', 'FTAG', 'FTR', 'HTGS', 'ATGS',\n", | |
| " 'HTGC', 'ATGC', 'HTP', 'ATP', 'HT_Wins', 'AT_Wins', 'HT_Loss',\n", | |
| " 'AT_Loss', 'HT_Draws', 'AT_Draws', 'MW', 'HomeTeamLP',\n", | |
| " 'AwayTeamLP', 'Sezon', 'HT_LP', 'AT_LP', 'HTFormPtsStr',\n", | |
| " 'ATFormPtsStr', 'HTFormPts', 'ATFormPts', 'HTWinStreak3',\n", | |
| " 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5', 'HTDrawStreak3',\n", | |
| " 'HTDrawStreak5', 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3',\n", | |
| " 'ATLossStreak5', 'ATDrawStreak3', 'ATDrawStreak5', 'HTGD', 'ATGD',\n", | |
| " 'DiffPts', 'DiffFormPts', 'DiffLP', 'H2H_Home', 'H2H_Away',\n", | |
| " 'H2H_Home_pts', 'H2H_Away_pts', 'temp', 'Off_H', 'Off_A', 'Deff_H',\n", | |
| " 'Deff_A', 'Mean_home_goals', 'Mean_away_goals', 'Club_x', 'Age_H',\n", | |
| " 'Total_value_H', 'Club_y', 'Age_A', 'Total_value_A', 'draw',\n", | |
| " 'HM1_D', 'HM1_L', 'HM1_W', 'HM2_D', 'HM2_L', 'HM2_W', 'AM1_D',\n", | |
| " 'AM1_L', 'AM1_W', 'AM2_D', 'AM2_L', 'AM2_W', 'HM3_D', 'HM3_L',\n", | |
| " 'HM3_W', 'HM4_D', 'HM4_L', 'HM4_W', 'HM5_D', 'HM5_L', 'HM5_M',\n", | |
| " 'HM5_W', 'AM3_D', 'AM3_L', 'AM3_W', 'AM4_D', 'AM4_L', 'AM4_W',\n", | |
| " 'AM5_D', 'AM5_L', 'AM5_M', 'AM5_W'], dtype=object)" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cat_vars=[\"HM1\", \"HM2\", \"AM1\", \"AM2\",\n", | |
| " \"HM3\", \"HM4\", \"HM5\", \"AM3\", \"AM4\", \"AM5\"]\n", | |
| "\n", | |
| "for var in cat_vars:\n", | |
| " cat_list='var'+'_'+var\n", | |
| " cat_list = pd.get_dummies(df_final[var], prefix=var)\n", | |
| " df_final1=df_final.join(cat_list)\n", | |
| " df_final=df_final1\n", | |
| "\n", | |
| "data_vars=df_final.columns.values.tolist()\n", | |
| "to_keep=[i for i in data_vars if i not in cat_vars]\n", | |
| "\n", | |
| "df_final=df_final[to_keep]\n", | |
| "df_final.columns.values" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Wstępne wyrzucenie zbędnych zmiennych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "cols_sel = ['HTGS', 'ATGS', 'HTGC', 'ATGC', 'HTP', 'ATP', 'MW', 'HomeTeamLP',\n", | |
| " 'HT_Wins', 'AT_Wins', 'HT_Loss', 'AT_Loss', 'HT_Draws', 'AT_Draws',\n", | |
| " 'AwayTeamLP', 'HT_LP', 'AT_LP', 'HTFormPtsStr', \n", | |
| " 'ATFormPtsStr', 'HTFormPts', 'ATFormPts', 'HTWinStreak3',\n", | |
| " 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5', 'ATWinStreak3',\n", | |
| " 'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5', 'HTGD', 'ATGD',\n", | |
| " 'DiffPts', 'DiffFormPts', 'DiffLP', 'H2H_Home', 'H2H_Away','Total_value_H', 'Total_value_A',\n", | |
| " 'H2H_Home_pts', 'H2H_Away_pts', 'Mean_home_goals', 'Mean_away_goals', 'Age_H', 'Age_A',\n", | |
| " 'draw', 'HM1_D', 'HM1_L', 'HM1_W', 'HM2_D', 'HM2_L', 'HM2_W', 'AM1_D', 'AM1_L',\n", | |
| " 'AM1_W', 'AM2_D', 'AM2_L', 'AM2_W', 'HM3_D', 'HM3_L', 'HM3_W',\n", | |
| " 'HM4_D', 'HM4_L', 'HM4_W', 'HM5_D', 'HM5_L', 'HM5_M', 'HM5_W',\n", | |
| " 'AM3_D', 'AM3_L', 'AM3_W', 'AM4_D', 'AM4_L', 'AM4_W', 'AM5_D',\n", | |
| " 'AM5_L', 'AM5_M', 'AM5_W']\n", | |
| "\n", | |
| "df_final = df_final[cols_sel]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Przegląd danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HTGS</th>\n", | |
| " <th>ATGS</th>\n", | |
| " <th>HTGC</th>\n", | |
| " <th>ATGC</th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>MW</th>\n", | |
| " <th>HomeTeamLP</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " <th>AwayTeamLP</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTFormPts</th>\n", | |
| " <th>ATFormPts</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <th>Total_value_A</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>Age_H</th>\n", | |
| " <th>Age_A</th>\n", | |
| " <th>HM1_D</th>\n", | |
| " <th>HM1_L</th>\n", | |
| " <th>HM1_W</th>\n", | |
| " <th>HM2_D</th>\n", | |
| " <th>HM2_L</th>\n", | |
| " <th>HM2_W</th>\n", | |
| " <th>AM1_D</th>\n", | |
| " <th>AM1_L</th>\n", | |
| " <th>AM1_W</th>\n", | |
| " <th>AM2_D</th>\n", | |
| " <th>AM2_L</th>\n", | |
| " <th>AM2_W</th>\n", | |
| " <th>HM3_D</th>\n", | |
| " <th>HM3_L</th>\n", | |
| " <th>HM3_W</th>\n", | |
| " <th>HM4_D</th>\n", | |
| " <th>HM4_L</th>\n", | |
| " <th>HM4_W</th>\n", | |
| " <th>HM5_D</th>\n", | |
| " <th>HM5_L</th>\n", | |
| " <th>HM5_M</th>\n", | |
| " <th>HM5_W</th>\n", | |
| " <th>AM3_D</th>\n", | |
| " <th>AM3_L</th>\n", | |
| " <th>AM3_W</th>\n", | |
| " <th>AM4_D</th>\n", | |
| " <th>AM4_L</th>\n", | |
| " <th>AM4_W</th>\n", | |
| " <th>AM5_D</th>\n", | |
| " <th>AM5_L</th>\n", | |
| " <th>AM5_M</th>\n", | |
| " <th>AM5_W</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>3930.000000</td>\n", | |
| " <td>3930.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>3930.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>26.138025</td>\n", | |
| " <td>26.193580</td>\n", | |
| " <td>26.242469</td>\n", | |
| " <td>26.099506</td>\n", | |
| " <td>27.857037</td>\n", | |
| " <td>28.010617</td>\n", | |
| " <td>21.388148</td>\n", | |
| " <td>10.053580</td>\n", | |
| " <td>7.515062</td>\n", | |
| " <td>7.561728</td>\n", | |
| " <td>7.561235</td>\n", | |
| " <td>7.500988</td>\n", | |
| " <td>5.311852</td>\n", | |
| " <td>5.325432</td>\n", | |
| " <td>10.058765</td>\n", | |
| " <td>10.090370</td>\n", | |
| " <td>9.894815</td>\n", | |
| " <td>6.743003</td>\n", | |
| " <td>6.954962</td>\n", | |
| " <td>-0.104444</td>\n", | |
| " <td>0.094074</td>\n", | |
| " <td>-0.153580</td>\n", | |
| " <td>-0.211959</td>\n", | |
| " <td>-0.005185</td>\n", | |
| " <td>149.781983</td>\n", | |
| " <td>149.493160</td>\n", | |
| " <td>3.167901</td>\n", | |
| " <td>3.504938</td>\n", | |
| " <td>1.455520</td>\n", | |
| " <td>1.146740</td>\n", | |
| " <td>24.483235</td>\n", | |
| " <td>24.483556</td>\n", | |
| " <td>0.251358</td>\n", | |
| " <td>0.427654</td>\n", | |
| " <td>0.320988</td>\n", | |
| " <td>0.267160</td>\n", | |
| " <td>0.331605</td>\n", | |
| " <td>0.401235</td>\n", | |
| " <td>0.263704</td>\n", | |
| " <td>0.315062</td>\n", | |
| " <td>0.421235</td>\n", | |
| " <td>0.251605</td>\n", | |
| " <td>0.413333</td>\n", | |
| " <td>0.335062</td>\n", | |
| " <td>0.243704</td>\n", | |
| " <td>0.405679</td>\n", | |
| " <td>0.350617</td>\n", | |
| " <td>0.266173</td>\n", | |
| " <td>0.355802</td>\n", | |
| " <td>0.378025</td>\n", | |
| " <td>0.247160</td>\n", | |
| " <td>0.365679</td>\n", | |
| " <td>0.029630</td>\n", | |
| " <td>0.357531</td>\n", | |
| " <td>0.269383</td>\n", | |
| " <td>0.333086</td>\n", | |
| " <td>0.397531</td>\n", | |
| " <td>0.251852</td>\n", | |
| " <td>0.384198</td>\n", | |
| " <td>0.363951</td>\n", | |
| " <td>0.256296</td>\n", | |
| " <td>0.357037</td>\n", | |
| " <td>0.029630</td>\n", | |
| " <td>0.357037</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>15.638481</td>\n", | |
| " <td>15.578482</td>\n", | |
| " <td>14.807832</td>\n", | |
| " <td>14.753234</td>\n", | |
| " <td>16.947030</td>\n", | |
| " <td>16.887235</td>\n", | |
| " <td>9.755973</td>\n", | |
| " <td>5.167612</td>\n", | |
| " <td>5.170293</td>\n", | |
| " <td>5.151665</td>\n", | |
| " <td>4.950865</td>\n", | |
| " <td>4.980984</td>\n", | |
| " <td>3.278508</td>\n", | |
| " <td>3.265674</td>\n", | |
| " <td>5.168153</td>\n", | |
| " <td>5.616476</td>\n", | |
| " <td>5.526943</td>\n", | |
| " <td>3.299908</td>\n", | |
| " <td>3.329799</td>\n", | |
| " <td>14.447449</td>\n", | |
| " <td>14.480390</td>\n", | |
| " <td>14.709943</td>\n", | |
| " <td>4.589276</td>\n", | |
| " <td>7.451181</td>\n", | |
| " <td>118.945594</td>\n", | |
| " <td>118.314136</td>\n", | |
| " <td>2.464960</td>\n", | |
| " <td>2.491695</td>\n", | |
| " <td>0.771242</td>\n", | |
| " <td>0.532545</td>\n", | |
| " <td>1.223340</td>\n", | |
| " <td>1.222632</td>\n", | |
| " <td>0.433847</td>\n", | |
| " <td>0.494800</td>\n", | |
| " <td>0.466914</td>\n", | |
| " <td>0.442531</td>\n", | |
| " <td>0.470848</td>\n", | |
| " <td>0.490209</td>\n", | |
| " <td>0.440695</td>\n", | |
| " <td>0.464598</td>\n", | |
| " <td>0.493818</td>\n", | |
| " <td>0.433989</td>\n", | |
| " <td>0.492492</td>\n", | |
| " <td>0.472070</td>\n", | |
| " <td>0.429369</td>\n", | |
| " <td>0.491084</td>\n", | |
| " <td>0.477222</td>\n", | |
| " <td>0.442010</td>\n", | |
| " <td>0.478815</td>\n", | |
| " <td>0.484954</td>\n", | |
| " <td>0.431414</td>\n", | |
| " <td>0.481680</td>\n", | |
| " <td>0.169584</td>\n", | |
| " <td>0.479332</td>\n", | |
| " <td>0.443694</td>\n", | |
| " <td>0.471375</td>\n", | |
| " <td>0.489448</td>\n", | |
| " <td>0.434130</td>\n", | |
| " <td>0.486465</td>\n", | |
| " <td>0.481194</td>\n", | |
| " <td>0.436641</td>\n", | |
| " <td>0.479185</td>\n", | |
| " <td>0.169584</td>\n", | |
| " <td>0.479185</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>-53.000000</td>\n", | |
| " <td>-52.000000</td>\n", | |
| " <td>-65.000000</td>\n", | |
| " <td>-15.000000</td>\n", | |
| " <td>-17.000000</td>\n", | |
| " <td>25.630000</td>\n", | |
| " <td>25.630000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>22.200000</td>\n", | |
| " <td>22.200000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>13.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>13.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>5.250000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>-8.000000</td>\n", | |
| " <td>-8.000000</td>\n", | |
| " <td>-8.000000</td>\n", | |
| " <td>-3.000000</td>\n", | |
| " <td>-6.000000</td>\n", | |
| " <td>63.730000</td>\n", | |
| " <td>63.730000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.932362</td>\n", | |
| " <td>0.880000</td>\n", | |
| " <td>23.500000</td>\n", | |
| " <td>23.500000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>24.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>21.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>10.500000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>-1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>104.025000</td>\n", | |
| " <td>103.650000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>1.389809</td>\n", | |
| " <td>1.140000</td>\n", | |
| " <td>24.400000</td>\n", | |
| " <td>24.400000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>36.000000</td>\n", | |
| " <td>36.000000</td>\n", | |
| " <td>37.000000</td>\n", | |
| " <td>37.000000</td>\n", | |
| " <td>39.000000</td>\n", | |
| " <td>39.000000</td>\n", | |
| " <td>30.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>6.000000</td>\n", | |
| " <td>207.730000</td>\n", | |
| " <td>207.730000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.750000</td>\n", | |
| " <td>1.890617</td>\n", | |
| " <td>1.323413</td>\n", | |
| " <td>25.275000</td>\n", | |
| " <td>25.300000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>87.000000</td>\n", | |
| " <td>90.000000</td>\n", | |
| " <td>83.000000</td>\n", | |
| " <td>83.000000</td>\n", | |
| " <td>99.000000</td>\n", | |
| " <td>96.000000</td>\n", | |
| " <td>38.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>32.000000</td>\n", | |
| " <td>31.000000</td>\n", | |
| " <td>28.000000</td>\n", | |
| " <td>28.000000</td>\n", | |
| " <td>17.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>67.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>17.000000</td>\n", | |
| " <td>768.500000</td>\n", | |
| " <td>768.500000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>7.448276</td>\n", | |
| " <td>5.015432</td>\n", | |
| " <td>28.600000</td>\n", | |
| " <td>28.600000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HTGS ATGS HTGC ATGC HTP \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 26.138025 26.193580 26.242469 26.099506 27.857037 \n", | |
| "std 15.638481 15.578482 14.807832 14.753234 16.947030 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 13.000000 14.000000 14.000000 14.000000 14.000000 \n", | |
| "50% 24.000000 24.000000 24.000000 24.000000 25.000000 \n", | |
| "75% 36.000000 36.000000 37.000000 37.000000 39.000000 \n", | |
| "max 87.000000 90.000000 83.000000 83.000000 99.000000 \n", | |
| "\n", | |
| " ATP MW HomeTeamLP HT_Wins AT_Wins \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 28.010617 21.388148 10.053580 7.515062 7.561728 \n", | |
| "std 16.887235 9.755973 5.167612 5.170293 5.151665 \n", | |
| "min 0.000000 5.000000 1.000000 0.000000 0.000000 \n", | |
| "25% 15.000000 13.000000 5.000000 4.000000 4.000000 \n", | |
| "50% 25.000000 21.000000 10.000000 7.000000 7.000000 \n", | |
| "75% 39.000000 30.000000 15.000000 10.000000 10.000000 \n", | |
| "max 96.000000 38.000000 18.000000 32.000000 31.000000 \n", | |
| "\n", | |
| " HT_Loss AT_Loss HT_Draws AT_Draws AwayTeamLP \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 7.561235 7.500988 5.311852 5.325432 10.058765 \n", | |
| "std 4.950865 4.980984 3.278508 3.265674 5.168153 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 1.000000 \n", | |
| "25% 4.000000 4.000000 3.000000 3.000000 5.250000 \n", | |
| "50% 7.000000 7.000000 5.000000 5.000000 10.500000 \n", | |
| "75% 11.000000 11.000000 8.000000 8.000000 15.000000 \n", | |
| "max 28.000000 28.000000 17.000000 18.000000 18.000000 \n", | |
| "\n", | |
| " HT_LP AT_LP HTFormPts ATFormPts HTGD \\\n", | |
| "count 4050.000000 4050.000000 3930.000000 3930.000000 4050.000000 \n", | |
| "mean 10.090370 9.894815 6.743003 6.954962 -0.104444 \n", | |
| "std 5.616476 5.526943 3.299908 3.329799 14.447449 \n", | |
| "min 1.000000 1.000000 0.000000 0.000000 -53.000000 \n", | |
| "25% 5.000000 5.000000 4.000000 4.000000 -8.000000 \n", | |
| "50% 10.000000 10.000000 7.000000 7.000000 -1.000000 \n", | |
| "75% 15.000000 14.000000 9.000000 9.000000 7.000000 \n", | |
| "max 20.000000 20.000000 15.000000 15.000000 61.000000 \n", | |
| "\n", | |
| " ATGD DiffPts DiffFormPts DiffLP Total_value_H \\\n", | |
| "count 4050.000000 4050.000000 3930.000000 4050.000000 4050.000000 \n", | |
| "mean 0.094074 -0.153580 -0.211959 -0.005185 149.781983 \n", | |
| "std 14.480390 14.709943 4.589276 7.451181 118.945594 \n", | |
| "min -52.000000 -65.000000 -15.000000 -17.000000 25.630000 \n", | |
| "25% -8.000000 -8.000000 -3.000000 -6.000000 63.730000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 104.025000 \n", | |
| "75% 7.000000 8.000000 3.000000 6.000000 207.730000 \n", | |
| "max 61.000000 67.000000 15.000000 17.000000 768.500000 \n", | |
| "\n", | |
| " Total_value_A H2H_Home_pts H2H_Away_pts Mean_home_goals \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 149.493160 3.167901 3.504938 1.455520 \n", | |
| "std 118.314136 2.464960 2.491695 0.771242 \n", | |
| "min 25.630000 0.000000 0.000000 0.000000 \n", | |
| "25% 63.730000 1.000000 1.000000 0.932362 \n", | |
| "50% 103.650000 3.000000 3.000000 1.389809 \n", | |
| "75% 207.730000 5.000000 5.750000 1.890617 \n", | |
| "max 768.500000 9.000000 9.000000 7.448276 \n", | |
| "\n", | |
| " Mean_away_goals Age_H Age_A HM1_D HM1_L \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 1.146740 24.483235 24.483556 0.251358 0.427654 \n", | |
| "std 0.532545 1.223340 1.222632 0.433847 0.494800 \n", | |
| "min 0.000000 22.200000 22.200000 0.000000 0.000000 \n", | |
| "25% 0.880000 23.500000 23.500000 0.000000 0.000000 \n", | |
| "50% 1.140000 24.400000 24.400000 0.000000 0.000000 \n", | |
| "75% 1.323413 25.275000 25.300000 1.000000 1.000000 \n", | |
| "max 5.015432 28.600000 28.600000 1.000000 1.000000 \n", | |
| "\n", | |
| " HM1_W HM2_D HM2_L HM2_W AM1_D \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.320988 0.267160 0.331605 0.401235 0.263704 \n", | |
| "std 0.466914 0.442531 0.470848 0.490209 0.440695 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " AM1_L AM1_W AM2_D AM2_L AM2_W \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.315062 0.421235 0.251605 0.413333 0.335062 \n", | |
| "std 0.464598 0.493818 0.433989 0.492492 0.472070 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " HM3_D HM3_L HM3_W HM4_D HM4_L \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.243704 0.405679 0.350617 0.266173 0.355802 \n", | |
| "std 0.429369 0.491084 0.477222 0.442010 0.478815 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 0.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " HM4_W HM5_D HM5_L HM5_M HM5_W \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.378025 0.247160 0.365679 0.029630 0.357531 \n", | |
| "std 0.484954 0.431414 0.481680 0.169584 0.479332 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 1.000000 0.000000 1.000000 0.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " AM3_D AM3_L AM3_W AM4_D AM4_L \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.269383 0.333086 0.397531 0.251852 0.384198 \n", | |
| "std 0.443694 0.471375 0.489448 0.434130 0.486465 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", | |
| "\n", | |
| " AM4_W AM5_D AM5_L AM5_M AM5_W \n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 0.363951 0.256296 0.357037 0.029630 0.357037 \n", | |
| "std 0.481194 0.436641 0.479185 0.169584 0.479185 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "75% 1.000000 1.000000 1.000000 0.000000 1.000000 \n", | |
| "max 1.000000 1.000000 1.000000 1.000000 1.000000 " | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_final.describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wiersz 'count' zlicza wszystkie wartości występujące w danej kolumnie. Jężli liczba ta jest mniejsza od ilości wszystkich obserwcji w zbiorze danych świadczyć może to o brakach danych dla danej zmiennej.\n", | |
| "\n", | |
| "Poza zliczeniem wartości, można prześledzić podstawowe statystki dla każdej ze zmienej.\n", | |
| "Należy w tym momemcie podkreślić, że wszystkie wartości w zbiorze danych nie uwzględniają końcowych dla danego sezonu. Dzieje się to z powodu celu wykorzystania danego zbioru. Dane te wykorzystywane są do celów predykcyjnych, dlatego wszystki statystki dla nowych zmiennych przyjmują wartości z poprzdniej kolejki, gdyż siła rzeczy nie mamy dostpnych statystyk dla danego meczu przed jego wydarzeniem." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wyszukanie braków danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
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| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HTFormPts</th>\n", | |
| " <th>ATFormPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>3930.000000</td>\n", | |
| " <td>3930.000000</td>\n", | |
| " <td>3930.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>6.743003</td>\n", | |
| " <td>6.954962</td>\n", | |
| " <td>-0.211959</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>3.299908</td>\n", | |
| " <td>3.329799</td>\n", | |
| " <td>4.589276</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>-15.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>-3.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HTFormPts ATFormPts DiffFormPts\n", | |
| "count 3930.000000 3930.000000 3930.000000\n", | |
| "mean 6.743003 6.954962 -0.211959\n", | |
| "std 3.299908 3.329799 4.589276\n", | |
| "min 0.000000 0.000000 -15.000000\n", | |
| "25% 4.000000 4.000000 -3.000000\n", | |
| "50% 7.000000 7.000000 0.000000\n", | |
| "75% 9.000000 9.000000 3.000000\n", | |
| "max 15.000000 15.000000 15.000000" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_final.describe()[[\"HTFormPts\",\"ATFormPts\", \"DiffFormPts\" ]]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zmienne HTFormPts oraz ATFormPts posiadają znacznie mniej wartości niż pozostałe kolumny. \n", | |
| "Z tego powodu należy uzupełnić te kolumny." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Uzupełnienie braków danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Uzupełnienie braków danych wartością mediany w wybranych kolumnach" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df_final.HTFormPts = df_final.HTFormPts.fillna(df_final.HTFormPts.median())\n", | |
| "df_final.ATFormPts = df_final.ATFormPts.fillna(df_final.ATFormPts.median())\n", | |
| "df_final.DiffFormPts = df_final.DiffFormPts.fillna(df_final.DiffFormPts.median())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Baki danych uzupełnione zotały medianą w danej kolumnie." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Stworzenie nowych zmiennych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Aby zmniejszyć liczbę zmiennych niektóre z nich sporwadzamy do jednej, która będzie preprezentowała dwie poprzednie." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Index(['HTGS', 'ATGS', 'HTGC', 'ATGC', 'HTP', 'ATP', 'MW', 'HomeTeamLP',\n", | |
| " 'HT_Wins', 'AT_Wins', 'HT_Loss', 'AT_Loss', 'HT_Draws', 'AT_Draws',\n", | |
| " 'AwayTeamLP', 'HT_LP', 'AT_LP', 'HTFormPtsStr', 'ATFormPtsStr',\n", | |
| " 'HTFormPts', 'ATFormPts', 'HTWinStreak3', 'HTWinStreak5',\n", | |
| " 'HTLossStreak3', 'HTLossStreak5', 'ATWinStreak3', 'ATWinStreak5',\n", | |
| " 'ATLossStreak3', 'ATLossStreak5', 'HTGD', 'ATGD', 'DiffPts',\n", | |
| " 'DiffFormPts', 'DiffLP', 'H2H_Home', 'H2H_Away', 'Total_value_H',\n", | |
| " 'Total_value_A', 'H2H_Home_pts', 'H2H_Away_pts', 'Mean_home_goals',\n", | |
| " 'Mean_away_goals', 'Age_H', 'Age_A', 'draw', 'HM1_D', 'HM1_L', 'HM1_W',\n", | |
| " 'HM2_D', 'HM2_L', 'HM2_W', 'AM1_D', 'AM1_L', 'AM1_W', 'AM2_D', 'AM2_L',\n", | |
| " 'AM2_W', 'HM3_D', 'HM3_L', 'HM3_W', 'HM4_D', 'HM4_L', 'HM4_W', 'HM5_D',\n", | |
| " 'HM5_L', 'HM5_M', 'HM5_W', 'AM3_D', 'AM3_L', 'AM3_W', 'AM4_D', 'AM4_L',\n", | |
| " 'AM4_W', 'AM5_D', 'AM5_L', 'AM5_M', 'AM5_W', 'H2H_Diff', 'Total_Diff',\n", | |
| " 'Age_diff', 'LP_Diff'],\n", | |
| " dtype='object')" | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_final[\"H2H_Diff\"] = df_final[\"H2H_Home_pts\"] - df_final[\"H2H_Away_pts\"]\n", | |
| "df_final[\"Total_Diff\"] = df_final[\"Total_value_H\"] / df_final[\"Total_value_A\"]\n", | |
| "df_final[\"Age_diff\"] = df_final[\"Age_H\"] - df_final[\"Age_A\"]\n", | |
| "df_final[\"LP_Diff\"] = df_final[\"HT_LP\"] - df_final[\"AT_LP\"]\n", | |
| "\n", | |
| "df_final.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Wybranie wybranych kolumn w zbiorze danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>HM1_D</th>\n", | |
| " <th>HM1_L</th>\n", | |
| " <th>HM1_W</th>\n", | |
| " <th>HM2_D</th>\n", | |
| " <th>HM2_L</th>\n", | |
| " <th>HM2_W</th>\n", | |
| " <th>AM1_D</th>\n", | |
| " <th>AM1_L</th>\n", | |
| " <th>AM1_W</th>\n", | |
| " <th>AM2_D</th>\n", | |
| " <th>AM2_L</th>\n", | |
| " <th>AM2_W</th>\n", | |
| " <th>HM3_D</th>\n", | |
| " <th>HM3_L</th>\n", | |
| " <th>HM3_W</th>\n", | |
| " <th>HM4_D</th>\n", | |
| " <th>HM4_L</th>\n", | |
| " <th>HM4_W</th>\n", | |
| " <th>HM5_D</th>\n", | |
| " <th>HM5_L</th>\n", | |
| " <th>HM5_M</th>\n", | |
| " <th>HM5_W</th>\n", | |
| " <th>AM3_D</th>\n", | |
| " <th>AM3_L</th>\n", | |
| " <th>AM3_W</th>\n", | |
| " <th>AM4_D</th>\n", | |
| " <th>AM4_L</th>\n", | |
| " <th>AM4_W</th>\n", | |
| " <th>AM5_D</th>\n", | |
| " <th>AM5_L</th>\n", | |
| " <th>AM5_M</th>\n", | |
| " <th>AM5_W</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " <th>MW</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTWinStreak3</th>\n", | |
| " <th>HTWinStreak5</th>\n", | |
| " <th>HTLossStreak3</th>\n", | |
| " <th>HTLossStreak5</th>\n", | |
| " <th>ATWinStreak3</th>\n", | |
| " <th>ATWinStreak5</th>\n", | |
| " <th>ATLossStreak3</th>\n", | |
| " <th>ATLossStreak5</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <th>Total_value_A</th>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <th>Age_diff</th>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <th>draw</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>4045</th>\n", | |
| " <td>84</td>\n", | |
| " <td>40</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>27</td>\n", | |
| " <td>8</td>\n", | |
| " <td>2</td>\n", | |
| " <td>8</td>\n", | |
| " <td>3</td>\n", | |
| " <td>16</td>\n", | |
| " <td>33</td>\n", | |
| " <td>1</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>43.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>44</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>-7.0</td>\n", | |
| " <td>9</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2.500000</td>\n", | |
| " <td>2.367347</td>\n", | |
| " <td>768.50</td>\n", | |
| " <td>221.8</td>\n", | |
| " <td>9</td>\n", | |
| " <td>3.464833</td>\n", | |
| " <td>4.1</td>\n", | |
| " <td>-9</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4046</th>\n", | |
| " <td>47</td>\n", | |
| " <td>38</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>13</td>\n", | |
| " <td>8</td>\n", | |
| " <td>8</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>14</td>\n", | |
| " <td>30</td>\n", | |
| " <td>7</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>9</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>-5.0</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>2.131579</td>\n", | |
| " <td>1.580511</td>\n", | |
| " <td>368.80</td>\n", | |
| " <td>221.8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.662759</td>\n", | |
| " <td>2.6</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4047</th>\n", | |
| " <td>22</td>\n", | |
| " <td>32</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>5</td>\n", | |
| " <td>7</td>\n", | |
| " <td>11</td>\n", | |
| " <td>5</td>\n", | |
| " <td>7</td>\n", | |
| " <td>11</td>\n", | |
| " <td>24</td>\n", | |
| " <td>14</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-16.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>-10</td>\n", | |
| " <td>-2.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>5</td>\n", | |
| " <td>0.675000</td>\n", | |
| " <td>2.054737</td>\n", | |
| " <td>57.05</td>\n", | |
| " <td>221.8</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>0.257214</td>\n", | |
| " <td>2.8</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4048</th>\n", | |
| " <td>29</td>\n", | |
| " <td>40</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>19</td>\n", | |
| " <td>10</td>\n", | |
| " <td>8</td>\n", | |
| " <td>16</td>\n", | |
| " <td>35</td>\n", | |
| " <td>18</td>\n", | |
| " <td>11</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-24.0</td>\n", | |
| " <td>-1.0</td>\n", | |
| " <td>-11</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>3</td>\n", | |
| " <td>6</td>\n", | |
| " <td>2.250000</td>\n", | |
| " <td>1.170000</td>\n", | |
| " <td>37.90</td>\n", | |
| " <td>221.8</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>0.170875</td>\n", | |
| " <td>2.4</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4049</th>\n", | |
| " <td>20</td>\n", | |
| " <td>18</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>5</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>5</td>\n", | |
| " <td>6</td>\n", | |
| " <td>15</td>\n", | |
| " <td>8</td>\n", | |
| " <td>12</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>4</td>\n", | |
| " <td>4</td>\n", | |
| " <td>1.400000</td>\n", | |
| " <td>1.247525</td>\n", | |
| " <td>120.43</td>\n", | |
| " <td>221.8</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.542967</td>\n", | |
| " <td>1.1</td>\n", | |
| " <td>-4</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HTP ATP HM1_D HM1_L HM1_W HM2_D HM2_L HM2_W AM1_D AM1_L AM1_W \\\n", | |
| "4045 84 40 0 1 0 0 0 1 1 0 0 \n", | |
| "4046 47 38 0 1 0 0 1 0 1 0 0 \n", | |
| "4047 22 32 0 1 0 1 0 0 1 0 0 \n", | |
| "4048 29 40 0 1 0 0 1 0 0 1 0 \n", | |
| "4049 20 18 1 0 0 0 1 0 0 1 0 \n", | |
| "\n", | |
| " AM2_D AM2_L AM2_W HM3_D HM3_L HM3_W HM4_D HM4_L HM4_W HM5_D \\\n", | |
| "4045 0 1 0 0 0 1 0 0 1 0 \n", | |
| "4046 0 1 0 0 0 1 0 1 0 0 \n", | |
| "4047 1 0 0 0 0 1 1 0 0 0 \n", | |
| "4048 0 1 0 1 0 0 0 1 0 0 \n", | |
| "4049 1 0 0 1 0 0 0 0 1 1 \n", | |
| "\n", | |
| " HM5_L HM5_M HM5_W AM3_D AM3_L AM3_W AM4_D AM4_L AM4_W AM5_D \\\n", | |
| "4045 1 0 0 1 0 0 1 0 0 0 \n", | |
| "4046 0 0 1 1 0 0 0 1 0 1 \n", | |
| "4047 1 0 0 0 0 1 1 0 0 1 \n", | |
| "4048 0 0 1 1 0 0 0 1 0 1 \n", | |
| "4049 0 0 0 1 0 0 1 0 0 1 \n", | |
| "\n", | |
| " AM5_L AM5_M AM5_W HT_Wins AT_Wins HT_Loss AT_Loss HT_Draws \\\n", | |
| "4045 1 0 0 27 8 2 8 3 \n", | |
| "4046 0 0 0 13 8 8 7 8 \n", | |
| "4047 0 0 0 5 7 11 5 7 \n", | |
| "4048 0 0 0 7 8 19 10 8 \n", | |
| "4049 0 0 0 5 4 4 4 5 \n", | |
| "\n", | |
| " AT_Draws MW HT_LP AT_LP HTWinStreak3 HTWinStreak5 HTLossStreak3 \\\n", | |
| "4045 16 33 1 10 0 0 0 \n", | |
| "4046 14 30 7 10 0 0 0 \n", | |
| "4047 11 24 14 10 0 0 0 \n", | |
| "4048 16 35 18 11 0 0 0 \n", | |
| "4049 6 15 8 12 0 0 0 \n", | |
| "\n", | |
| " HTLossStreak5 ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 \\\n", | |
| "4045 0 0 0 0 0 \n", | |
| "4046 0 0 0 0 0 \n", | |
| "4047 0 0 0 0 0 \n", | |
| "4048 0 0 0 0 0 \n", | |
| "4049 0 0 0 0 0 \n", | |
| "\n", | |
| " HTGD ATGD DiffPts DiffFormPts DiffLP H2H_Home_pts H2H_Away_pts \\\n", | |
| "4045 43.0 1.0 44 6.0 -7.0 9 0 \n", | |
| "4046 9.0 3.0 9 3.0 -5.0 4 4 \n", | |
| "4047 -16.0 6.0 -10 -2.0 9.0 2 5 \n", | |
| "4048 -24.0 -1.0 -11 2.0 8.0 3 6 \n", | |
| "4049 1.0 0.0 2 2.0 3.0 4 4 \n", | |
| "\n", | |
| " Mean_home_goals Mean_away_goals Total_value_H Total_value_A \\\n", | |
| "4045 2.500000 2.367347 768.50 221.8 \n", | |
| "4046 2.131579 1.580511 368.80 221.8 \n", | |
| "4047 0.675000 2.054737 57.05 221.8 \n", | |
| "4048 2.250000 1.170000 37.90 221.8 \n", | |
| "4049 1.400000 1.247525 120.43 221.8 \n", | |
| "\n", | |
| " H2H_Diff Total_Diff Age_diff LP_Diff draw \n", | |
| "4045 9 3.464833 4.1 -9 0 \n", | |
| "4046 0 1.662759 2.6 -3 1 \n", | |
| "4047 -3 0.257214 2.8 4 0 \n", | |
| "4048 -3 0.170875 2.4 7 0 \n", | |
| "4049 0 0.542967 1.1 -4 1 " | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_final = df_final[[ 'HTP', 'ATP','HM1_D', 'HM1_L', 'HM1_W',\n", | |
| " 'HM2_D', 'HM2_L', 'HM2_W', 'AM1_D', 'AM1_L', 'AM1_W', 'AM2_D', 'AM2_L',\n", | |
| " 'AM2_W', 'HM3_D', 'HM3_L', 'HM3_W', 'HM4_D', 'HM4_L', 'HM4_W', 'HM5_D',\n", | |
| " 'HM5_L', 'HM5_M', 'HM5_W', 'AM3_D', 'AM3_L', 'AM3_W', 'AM4_D', 'AM4_L',\n", | |
| " 'AM4_W', 'AM5_D', 'AM5_L', 'AM5_M', 'AM5_W', \n", | |
| " 'HT_Wins', 'AT_Wins', 'HT_Loss', 'AT_Loss', 'HT_Draws', 'AT_Draws',\n", | |
| " 'MW', 'HT_LP', 'AT_LP', 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3',\n", | |
| " 'HTLossStreak5', 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3',\n", | |
| " 'ATLossStreak5', 'HTGD', 'ATGD', 'DiffPts', 'DiffFormPts', 'DiffLP',\n", | |
| " 'H2H_Home_pts', 'H2H_Away_pts', 'Mean_home_goals', 'Mean_away_goals',\n", | |
| " 'Total_value_H', 'Total_value_A', 'H2H_Diff', \n", | |
| " 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw']]\n", | |
| "\n", | |
| "df_final.tail()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Konwersja wybranych kolumn do typu integer" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>MW</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " <td>4050.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>21.388148</td>\n", | |
| " <td>-0.205679</td>\n", | |
| " <td>-0.005185</td>\n", | |
| " <td>-0.104444</td>\n", | |
| " <td>0.094074</td>\n", | |
| " <td>10.090370</td>\n", | |
| " <td>9.894815</td>\n", | |
| " <td>27.857037</td>\n", | |
| " <td>28.010617</td>\n", | |
| " <td>3.167901</td>\n", | |
| " <td>3.504938</td>\n", | |
| " <td>7.515062</td>\n", | |
| " <td>7.561728</td>\n", | |
| " <td>7.561235</td>\n", | |
| " <td>7.500988</td>\n", | |
| " <td>5.311852</td>\n", | |
| " <td>5.325432</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>9.755973</td>\n", | |
| " <td>4.520901</td>\n", | |
| " <td>7.451181</td>\n", | |
| " <td>14.447449</td>\n", | |
| " <td>14.480390</td>\n", | |
| " <td>5.616476</td>\n", | |
| " <td>5.526943</td>\n", | |
| " <td>16.947030</td>\n", | |
| " <td>16.887235</td>\n", | |
| " <td>2.464960</td>\n", | |
| " <td>2.491695</td>\n", | |
| " <td>5.170293</td>\n", | |
| " <td>5.151665</td>\n", | |
| " <td>4.950865</td>\n", | |
| " <td>4.980984</td>\n", | |
| " <td>3.278508</td>\n", | |
| " <td>3.265674</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>-15.000000</td>\n", | |
| " <td>-17.000000</td>\n", | |
| " <td>-53.000000</td>\n", | |
| " <td>-52.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>13.000000</td>\n", | |
| " <td>-3.000000</td>\n", | |
| " <td>-6.000000</td>\n", | |
| " <td>-8.000000</td>\n", | |
| " <td>-8.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>21.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>-1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>30.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>6.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>14.000000</td>\n", | |
| " <td>39.000000</td>\n", | |
| " <td>39.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.750000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>38.000000</td>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>17.000000</td>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>61.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>99.000000</td>\n", | |
| " <td>96.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>32.000000</td>\n", | |
| " <td>31.000000</td>\n", | |
| " <td>28.000000</td>\n", | |
| " <td>28.000000</td>\n", | |
| " <td>17.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " MW DiffFormPts DiffLP HTGD ATGD \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 21.388148 -0.205679 -0.005185 -0.104444 0.094074 \n", | |
| "std 9.755973 4.520901 7.451181 14.447449 14.480390 \n", | |
| "min 5.000000 -15.000000 -17.000000 -53.000000 -52.000000 \n", | |
| "25% 13.000000 -3.000000 -6.000000 -8.000000 -8.000000 \n", | |
| "50% 21.000000 0.000000 0.000000 -1.000000 0.000000 \n", | |
| "75% 30.000000 3.000000 6.000000 7.000000 7.000000 \n", | |
| "max 38.000000 15.000000 17.000000 61.000000 61.000000 \n", | |
| "\n", | |
| " HT_LP AT_LP HTP ATP H2H_Home_pts \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 10.090370 9.894815 27.857037 28.010617 3.167901 \n", | |
| "std 5.616476 5.526943 16.947030 16.887235 2.464960 \n", | |
| "min 1.000000 1.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 5.000000 5.000000 14.000000 15.000000 1.000000 \n", | |
| "50% 10.000000 10.000000 25.000000 25.000000 3.000000 \n", | |
| "75% 15.000000 14.000000 39.000000 39.000000 5.000000 \n", | |
| "max 20.000000 20.000000 99.000000 96.000000 9.000000 \n", | |
| "\n", | |
| " H2H_Away_pts HT_Wins AT_Wins HT_Loss AT_Loss \\\n", | |
| "count 4050.000000 4050.000000 4050.000000 4050.000000 4050.000000 \n", | |
| "mean 3.504938 7.515062 7.561728 7.561235 7.500988 \n", | |
| "std 2.491695 5.170293 5.151665 4.950865 4.980984 \n", | |
| "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", | |
| "25% 1.000000 4.000000 4.000000 4.000000 4.000000 \n", | |
| "50% 3.000000 7.000000 7.000000 7.000000 7.000000 \n", | |
| "75% 5.750000 10.000000 10.000000 11.000000 11.000000 \n", | |
| "max 9.000000 32.000000 31.000000 28.000000 28.000000 \n", | |
| "\n", | |
| " HT_Draws AT_Draws \n", | |
| "count 4050.000000 4050.000000 \n", | |
| "mean 5.311852 5.325432 \n", | |
| "std 3.278508 3.265674 \n", | |
| "min 0.000000 0.000000 \n", | |
| "25% 3.000000 3.000000 \n", | |
| "50% 5.000000 5.000000 \n", | |
| "75% 8.000000 8.000000 \n", | |
| "max 17.000000 18.000000 " | |
| ] | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cols_to_int = [\"MW\", \"DiffFormPts\", \"DiffLP\", \"HTGD\", \"ATGD\", \n", | |
| " \"HT_LP\", \"AT_LP\", \"HTP\", \"ATP\", \"H2H_Home_pts\", \"H2H_Away_pts\",\n", | |
| " 'HT_Wins', 'AT_Wins', 'HT_Loss', 'AT_Loss', 'HT_Draws', 'AT_Draws']\n", | |
| "\n", | |
| "df_final.DiffFormPts = df_final.DiffFormPts.fillna(df_final.DiffFormPts.median())\n", | |
| "df_final[cols_to_int].describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df_final[cols_to_int] = df_final[cols_to_int].astype('int')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Eksploracja danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zmiennych na ten moment jest 67... Zdecydowanie za dużo.\n", | |
| "Sam fakt posiadania tak wielu zmiennych będzie znacznie utrudniał eksploracje, a następnie późniejsze tworzenie modelu." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Zmienna wynikowa 'draw'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "0 3016\n", | |
| "1 1034\n", | |
| "Name: draw, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df_final.draw.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEKCAYAAAAFJbKyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEYBJREFUeJzt3X2MpWdZx/Hvj5aCL0CLHWrd3bgNLIQStcWxNhIUAUtpggsETEmADTZZTFqFRKIF/2gFazABG4FaXNKVliCl8iKr2ViXChTEvkyxlm5r7ViQDtu0A4u8xpqtl3+ce+xpd2b23Ns5c2aY7yc5Oc9zPffznGuSzf7yvJz7pKqQJGlUj5t0A5Kk9cXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LU5dhJNzAOJ554Ym3dunXSbUjSunLLLbd8o6qmjjTuhzI4tm7dyszMzKTbkKR1Jcl/jjLOS1WSpC4GhySpi8EhSepicEiSuhgckqQuBockqcvYgiPJE5PclORfk+xP8oetfkqSG5PcneSjSY5r9Se09dm2fevQsd7a6nclecm4epYkHdk4zzgeBF5YVT8HnAacneRM4E+AS6tqG/At4Lw2/jzgW1X1DODSNo4kpwLnAs8Bzgb+PMkxY+xbkrSMsQVHDXyvrT6+vQp4IfCxVr8SeHlb3t7WadtflCStfnVVPVhVXwFmgTPG1bckaXlj/eZ4OzO4BXgGcBnwH8B/VdWhNmQO2NSWNwH3AlTVoSTfBn6i1W8YOuzwPmMz8zu/Ne6P0Do0/Z73T7oFaeLGenO8qh6qqtOAzQzOEp692LD2niW2LVV/hCQ7k8wkmZmfnz/aliVJR7AqT1VV1X8BnwXOBI5PsnCmsxk40JbngC0AbftTgIPD9UX2Gf6MXVU1XVXTU1NHnKNLknSUxvlU1VSS49vyjwAvBu4EPgO8qg3bAXyqLe9p67Tt/1hV1erntqeuTgG2ATeNq29J0vLGeY/jZODKdp/jccA1VfV3Se4Ark7yR8C/AFe08VcAH0oyy+BM41yAqtqf5BrgDuAQcH5VPTTGviVJyxhbcFTVbcDpi9TvYZGnoqrqv4FXL3GsS4BLVrpHSVI/vzkuSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSeoytuBIsiXJZ5LcmWR/kje1+sVJvp7k1vY6Z2iftyaZTXJXkpcM1c9utdkkF46rZ0nSkR07xmMfAn63qr6U5EnALUn2tW2XVtW7hgcnORU4F3gO8FPAp5M8s22+DPg1YA64OcmeqrpjjL1LkpYwtuCoqvuA+9ryd5PcCWxaZpftwNVV9SDwlSSzwBlt22xV3QOQ5Oo21uCQpAlYlXscSbYCpwM3ttIFSW5LsjvJCa22Cbh3aLe5VluqLkmagLEHR5IfBz4OvLmqvgNcDjwdOI3BGcm7F4YusnstU3/05+xMMpNkZn5+fkV6lyQdbqzBkeTxDELjw1X1CYCqur+qHqqq/wU+wMOXo+aALUO7bwYOLFN/hKraVVXTVTU9NTW18n+MJAkY71NVAa4A7qyqPx2qnzw07BXA7W15D3BukickOQXYBtwE3AxsS3JKkuMY3EDfM66+JUnLG+dTVc8DXgd8OcmtrfY24DVJTmNwuemrwBsBqmp/kmsY3PQ+BJxfVQ8BJLkAuBY4BthdVfvH2LckaRnjfKrqCyx+f2LvMvtcAlyySH3vcvtJklaP3xyXJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXcYWHEm2JPlMkjuT7E/yplZ/apJ9Se5u7ye0epK8J8lsktuSPHfoWDva+LuT7BhXz5KkIxvnGcch4Her6tnAmcD5SU4FLgSuq6ptwHVtHeClwLb22glcDoOgAS4CfhE4A7hoIWwkSatvbMFRVfdV1Zfa8neBO4FNwHbgyjbsSuDlbXk7cFUN3AAcn+Rk4CXAvqo6WFXfAvYBZ4+rb0nS8lblHkeSrcDpwI3ASVV1HwzCBXhaG7YJuHdot7lWW6r+6M/YmWQmycz8/PxK/wmSpGbswZHkx4GPA2+uqu8sN3SRWi1Tf2ShaldVTVfV9NTU1NE1K0k6orEGR5LHMwiND1fVJ1r5/nYJivb+QKvPAVuGdt8MHFimLkmagHE+VRXgCuDOqvrToU17gIUno3YAnxqqv749XXUm8O12Keta4KwkJ7Sb4me1miRpAo4d47GfB7wO+HKSW1vtbcA7gWuSnAd8DXh127YXOAeYBX4AvAGgqg4meQdwcxv39qo6OMa+JUnLGFtwVNUXWPz+BMCLFhlfwPlLHGs3sHvlupMkHS2/OS5J6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLiMFR5LrRqlJkn74LTvJYZInAj8KnNimNF+YtPDJwE+NuTdJ0hp0pNlx3wi8mUFI3MLDwfEd4LIx9iVJWqOWDY6q+jPgz5L8dlW9d5V6kiStYSP9HkdVvTfJLwFbh/epqqvG1JckaY0aKTiSfAh4OnAr8FArF2BwSNIGM+ovAE4Dp7Zf6ZMkbWCjfo/jduAnx9mIJGl9GPWM40TgjiQ3AQ8uFKvq18fSlSRpzRo1OC4eZxOSpPVj1KeqPjfuRiRJ68OoT1V9l8FTVADHAY8Hvl9VTx5XY5KktWnUM44nDa8neTlwxlg6kiStaUc1O25V/Q3wwhXuRZK0Dow6O+4rh16vSvJOHr50tdQ+u5M8kOT2odrFSb6e5Nb2Omdo21uTzCa5K8lLhupnt9pskguP4m+UJK2gUZ+qetnQ8iHgq8D2I+zzQeB9HP7t8kur6l3DhSSnAucCz2EwoeKnkzyzbb4M+DVgDrg5yZ6qumPEviVJK2zUexxv6D1wVV2fZOuIw7cDV1fVg8BXkszy8D2U2aq6ByDJ1W2swSFJEzLqparNST7ZLj3dn+TjSTYf5WdekOS2dinrhFbbBNw7NGau1ZaqS5ImZNSb438J7GFwGWkT8Let1utyBpMlngbcB7y71bPI2FqmfpgkO5PMJJmZn58/itYkSaMYNTimquovq+pQe30QmOr9sKq6v6oeqqr/BT7Aw5ej5oAtQ0M3AweWqS927F1VNV1V01NT3a1JkkY0anB8I8lrkxzTXq8Fvtn7YUlOHlp9BYPJE2FwNnNukickOQXYBtwE3AxsS3JKkuMY3EDf0/u5kqSVM+pTVb/J4AmpSxlcKvoisOwN8yQfAV7A4PfK54CLgBckOa0d46sMfpqWqtqf5BoGN70PAedX1UPtOBcA1wLHALuran/H3ydJWmGjBsc7gB1V9S2AJE8F3sUgUBZVVa9ZpHzFMuMvAS5ZpL4X2Dtin5KkMRv1UtXPLoQGQFUdBE4fT0uSpLVs1OB43NCjswtnHKOerUiSfoiM+p//u4EvJvkYg/sTv8Eil5UkST/8Rv3m+FVJZhhMbBjglU77IUkb08iXm1pQGBaStMEd1bTqkqSNy+CQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSF4NDktTF4JAkdTE4JEldDA5JUheDQ5LUxeCQJHUxOCRJXQwOSVIXg0OS1MXgkCR1MTgkSV0MDklSl7EFR5LdSR5IcvtQ7alJ9iW5u72f0OpJ8p4ks0luS/LcoX12tPF3J9kxrn4lSaMZ5xnHB4GzH1W7ELiuqrYB17V1gJcC29prJ3A5DIIGuAj4ReAM4KKFsJEkTcax4zpwVV2fZOujytuBF7TlK4HPAr/f6ldVVQE3JDk+yclt7L6qOgiQZB+DMPrIuPqW1oPf+uLMpFvQGvT+X5pelc9Z7XscJ1XVfQDt/Wmtvgm4d2jcXKstVZckTchauTmeRWq1TP3wAyQ7k8wkmZmfn1/R5iRJD1vt4Li/XYKivT/Q6nPAlqFxm4EDy9QPU1W7qmq6qqanpqZWvHFJ0sBqB8ceYOHJqB3Ap4bqr29PV50JfLtdyroWOCvJCe2m+FmtJkmakLHdHE/yEQY3t09MMsfg6ah3AtckOQ/4GvDqNnwvcA4wC/wAeANAVR1M8g7g5jbu7Qs3yiVJkzHOp6pes8SmFy0ytoDzlzjObmD3CrYmSXoM1srNcUnSOmFwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6TCQ4knw1yZeT3JpkptWemmRfkrvb+wmtniTvSTKb5LYkz51Ez5KkgUmecfxqVZ1WVdNt/ULguqraBlzX1gFeCmxrr53A5aveqSTp/62lS1XbgSvb8pXAy4fqV9XADcDxSU6eRIOSpMkFRwH/kOSWJDtb7aSqug+gvT+t1TcB9w7tO9dqj5BkZ5KZJDPz8/NjbF2SNrZjJ/S5z6uqA0meBuxL8m/LjM0itTqsULUL2AUwPT192HZJ0sqYyBlHVR1o7w8AnwTOAO5fuATV3h9ow+eALUO7bwYOrF63kqRhqx4cSX4syZMWloGzgNuBPcCONmwH8Km2vAd4fXu66kzg2wuXtCRJq28Sl6pOAj6ZZOHz/6qq/j7JzcA1Sc4Dvga8uo3fC5wDzAI/AN6w+i1LkhasenBU1T3Azy1S/ybwokXqBZy/Cq1Jkkawlh7HlSStAwaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpi8EhSepicEiSuhgckqQuBockqYvBIUnqYnBIkroYHJKkLusmOJKcneSuJLNJLpx0P5K0Ua2L4EhyDHAZ8FLgVOA1SU6dbFeStDGti+AAzgBmq+qeqvof4Gpg+4R7kqQNab0Exybg3qH1uVaTJK2yYyfdwIiySK0eMSDZCexsq99LctfYu9o4TgS+Mekm1oT3/sWkO9Dh/PfZrMC/zp8eZdB6CY45YMvQ+mbgwPCAqtoF7FrNpjaKJDNVNT3pPqTF+O9z9a2XS1U3A9uSnJLkOOBcYM+Ee5KkDWldnHFU1aEkFwDXAscAu6tq/4TbkqQNaV0EB0BV7QX2TrqPDcpLgFrL/Pe5ylJVRx4lSVKzXu5xSJLWCINDy3KqF61FSXYneSDJ7ZPuZSMyOLQkp3rRGvZB4OxJN7FRGRxajlO9aE2qquuBg5PuY6MyOLQcp3qRdBiDQ8s54lQvkjYeg0PLOeJUL5I2HoNDy3GqF0mHMTi0pKo6BCxM9XIncI1TvWgtSPIR4J+BZyWZS3LepHvaSPzmuCSpi2cckqQuBockqYvBIUnqYnBIkroYHJKkLgaHtMKSXJzkLZPuQxoXg0NaBUnWza9tSkdicEgrIMkftN8t+TTwrFb7bJI/TvI54E1JXpbkxiT/kuTTSU5q476c5PgMfDPJ61v9Q0lePLm/SlqcwSE9Rkl+nsF0LKcDrwR+YWjz8VX1K1X1buALwJlVdTqDKep/r435J+B5wHOAe4Dnt/qZwA3j/wukPp4+S4/d84FPVtUPAJIMz+f10aHlzcBHk5wMHAd8pdU/D/wy8J/A5cDOJJuAg1X1vXE3L/XyjENaGUvN3fP9oeX3Au+rqp8B3gg8sdWvZxA+zwc+C8wDr2IQKNKaY3BIj931wCuS/EiSJwEvW2LcU4Cvt+UdC8Wquhc4EdhWVfcwuKT1FgwOrVEGh/QYVdWXGFySuhX4OEv/h38x8NdJPg9841HbbgT+vS1/nsEvLX5hxZuVVoCz40qSunjGIUnqYnBIkroYHJKkLgaHJKmLwSFJ6mJwSJK6GBySpC4GhySpy/8BF0hwt6lTvlYAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sns.countplot(x='draw', data=df_final , palette = \"hls\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Percentage of no draws is 74.46913580246914\n", | |
| "Percentage of draws 25.53086419753086\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "count_no_draws = len(df_final[df_final['draw']==0])\n", | |
| "count_draws = len(df_final[df_final['draw']==1])\n", | |
| "pct_of_no_draws = count_no_draws/(count_draws+count_no_draws)\n", | |
| "print(\"Percentage of no draws is\", pct_of_no_draws*100)\n", | |
| "pct_of_draws = count_draws/(count_draws+count_no_draws)\n", | |
| "print(\"Percentage of draws\", pct_of_draws*100)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "##### Zbiór daych składa się z 4040 obserwacji. \n", | |
| "Ponad 25% z nich to zdarzenia w których zmienna wynikowa, w naszym przypadku 'draw', przyjmuje wartośc 1, co oznacza wystapienie remisu." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Tabela kontyngencji dla HM1/Draw" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>draw</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HM1_D</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0.752309</td>\n", | |
| " <td>0.247691</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>0.722004</td>\n", | |
| " <td>0.277996</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "draw 0 1\n", | |
| "HM1_D \n", | |
| "0 0.752309 0.247691\n", | |
| "1 0.722004 0.277996" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "contingency_table = pd.crosstab(df_final['HM1_D'], df_final['draw'])\n", | |
| "contingency_table.sum(axis=1)\n", | |
| "contingency_table.sum(axis=0)\n", | |
| "contingency_table.astype('float').div(contingency_table.sum(axis=1),axis=0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Co ciekawe procent występowania zdarzenia 'draw' jest większy w przypadku kiedy druyżyna gospdoarzy zremisowła swój ostatni mecz, aniżeli nie zremisowała go. Pozostałe zmienne możecie przeanalizować tabeli poniżej." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HTP</th>\n", | |
| " <th>ATP</th>\n", | |
| " <th>HM1_D</th>\n", | |
| " <th>HM1_L</th>\n", | |
| " <th>HM1_W</th>\n", | |
| " <th>HM2_D</th>\n", | |
| " <th>HM2_L</th>\n", | |
| " <th>HM2_W</th>\n", | |
| " <th>AM1_D</th>\n", | |
| " <th>AM1_L</th>\n", | |
| " <th>AM1_W</th>\n", | |
| " <th>AM2_D</th>\n", | |
| " <th>AM2_L</th>\n", | |
| " <th>AM2_W</th>\n", | |
| " <th>HM3_D</th>\n", | |
| " <th>HM3_L</th>\n", | |
| " <th>HM3_W</th>\n", | |
| " <th>HM4_D</th>\n", | |
| " <th>HM4_L</th>\n", | |
| " <th>HM4_W</th>\n", | |
| " <th>HM5_D</th>\n", | |
| " <th>HM5_L</th>\n", | |
| " <th>HM5_M</th>\n", | |
| " <th>HM5_W</th>\n", | |
| " <th>AM3_D</th>\n", | |
| " <th>AM3_L</th>\n", | |
| " <th>AM3_W</th>\n", | |
| " <th>AM4_D</th>\n", | |
| " <th>AM4_L</th>\n", | |
| " <th>AM4_W</th>\n", | |
| " <th>AM5_D</th>\n", | |
| " <th>AM5_L</th>\n", | |
| " <th>AM5_M</th>\n", | |
| " <th>AM5_W</th>\n", | |
| " <th>HT_Wins</th>\n", | |
| " <th>AT_Wins</th>\n", | |
| " <th>HT_Loss</th>\n", | |
| " <th>AT_Loss</th>\n", | |
| " <th>HT_Draws</th>\n", | |
| " <th>AT_Draws</th>\n", | |
| " <th>MW</th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTGD</th>\n", | |
| " <th>ATGD</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <th>Total_value_A</th>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <th>Age_diff</th>\n", | |
| " <th>LP_Diff</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>draw</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>28.208886</td>\n", | |
| " <td>28.179708</td>\n", | |
| " <td>0.243700</td>\n", | |
| " <td>0.430040</td>\n", | |
| " <td>0.326260</td>\n", | |
| " <td>0.263926</td>\n", | |
| " <td>0.323939</td>\n", | |
| " <td>0.412135</td>\n", | |
| " <td>0.254642</td>\n", | |
| " <td>0.321950</td>\n", | |
| " <td>0.423408</td>\n", | |
| " <td>0.256300</td>\n", | |
| " <td>0.408156</td>\n", | |
| " <td>0.335544</td>\n", | |
| " <td>0.237401</td>\n", | |
| " <td>0.409814</td>\n", | |
| " <td>0.352785</td>\n", | |
| " <td>0.257958</td>\n", | |
| " <td>0.349138</td>\n", | |
| " <td>0.392905</td>\n", | |
| " <td>0.246021</td>\n", | |
| " <td>0.358422</td>\n", | |
| " <td>0.028846</td>\n", | |
| " <td>0.366711</td>\n", | |
| " <td>0.263594</td>\n", | |
| " <td>0.335544</td>\n", | |
| " <td>0.400862</td>\n", | |
| " <td>0.248011</td>\n", | |
| " <td>0.380968</td>\n", | |
| " <td>0.371021</td>\n", | |
| " <td>0.254310</td>\n", | |
| " <td>0.359085</td>\n", | |
| " <td>0.028846</td>\n", | |
| " <td>0.357759</td>\n", | |
| " <td>7.63992</td>\n", | |
| " <td>7.624005</td>\n", | |
| " <td>7.538462</td>\n", | |
| " <td>7.535809</td>\n", | |
| " <td>5.289125</td>\n", | |
| " <td>5.307692</td>\n", | |
| " <td>21.467507</td>\n", | |
| " <td>9.996684</td>\n", | |
| " <td>9.824271</td>\n", | |
| " <td>0.237069</td>\n", | |
| " <td>0.121353</td>\n", | |
| " <td>0.029178</td>\n", | |
| " <td>-0.122679</td>\n", | |
| " <td>-0.151525</td>\n", | |
| " <td>3.219496</td>\n", | |
| " <td>3.454244</td>\n", | |
| " <td>1.471761</td>\n", | |
| " <td>1.148634</td>\n", | |
| " <td>154.453710</td>\n", | |
| " <td>148.772231</td>\n", | |
| " <td>-0.234748</td>\n", | |
| " <td>1.747164</td>\n", | |
| " <td>0.009118</td>\n", | |
| " <td>0.172414</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>26.830754</td>\n", | |
| " <td>27.517408</td>\n", | |
| " <td>0.273694</td>\n", | |
| " <td>0.420696</td>\n", | |
| " <td>0.305609</td>\n", | |
| " <td>0.276596</td>\n", | |
| " <td>0.353965</td>\n", | |
| " <td>0.369439</td>\n", | |
| " <td>0.290135</td>\n", | |
| " <td>0.294971</td>\n", | |
| " <td>0.414894</td>\n", | |
| " <td>0.237911</td>\n", | |
| " <td>0.428433</td>\n", | |
| " <td>0.333656</td>\n", | |
| " <td>0.262089</td>\n", | |
| " <td>0.393617</td>\n", | |
| " <td>0.344294</td>\n", | |
| " <td>0.290135</td>\n", | |
| " <td>0.375242</td>\n", | |
| " <td>0.334623</td>\n", | |
| " <td>0.250484</td>\n", | |
| " <td>0.386847</td>\n", | |
| " <td>0.031915</td>\n", | |
| " <td>0.330754</td>\n", | |
| " <td>0.286267</td>\n", | |
| " <td>0.325919</td>\n", | |
| " <td>0.387814</td>\n", | |
| " <td>0.263056</td>\n", | |
| " <td>0.393617</td>\n", | |
| " <td>0.343327</td>\n", | |
| " <td>0.262089</td>\n", | |
| " <td>0.351064</td>\n", | |
| " <td>0.031915</td>\n", | |
| " <td>0.354932</td>\n", | |
| " <td>7.15087</td>\n", | |
| " <td>7.380077</td>\n", | |
| " <td>7.627660</td>\n", | |
| " <td>7.399420</td>\n", | |
| " <td>5.378143</td>\n", | |
| " <td>5.377176</td>\n", | |
| " <td>21.156673</td>\n", | |
| " <td>10.363636</td>\n", | |
| " <td>10.100580</td>\n", | |
| " <td>-1.100580</td>\n", | |
| " <td>0.014507</td>\n", | |
| " <td>-0.686654</td>\n", | |
| " <td>-0.447776</td>\n", | |
| " <td>0.421663</td>\n", | |
| " <td>3.017408</td>\n", | |
| " <td>3.652805</td>\n", | |
| " <td>1.408149</td>\n", | |
| " <td>1.141215</td>\n", | |
| " <td>136.155358</td>\n", | |
| " <td>151.595986</td>\n", | |
| " <td>-0.635397</td>\n", | |
| " <td>1.441951</td>\n", | |
| " <td>-0.027853</td>\n", | |
| " <td>0.263056</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HTP ATP HM1_D HM1_L HM1_W HM2_D HM2_L \\\n", | |
| "draw \n", | |
| "0 28.208886 28.179708 0.243700 0.430040 0.326260 0.263926 0.323939 \n", | |
| "1 26.830754 27.517408 0.273694 0.420696 0.305609 0.276596 0.353965 \n", | |
| "\n", | |
| " HM2_W AM1_D AM1_L AM1_W AM2_D AM2_L AM2_W \\\n", | |
| "draw \n", | |
| "0 0.412135 0.254642 0.321950 0.423408 0.256300 0.408156 0.335544 \n", | |
| "1 0.369439 0.290135 0.294971 0.414894 0.237911 0.428433 0.333656 \n", | |
| "\n", | |
| " HM3_D HM3_L HM3_W HM4_D HM4_L HM4_W HM5_D \\\n", | |
| "draw \n", | |
| "0 0.237401 0.409814 0.352785 0.257958 0.349138 0.392905 0.246021 \n", | |
| "1 0.262089 0.393617 0.344294 0.290135 0.375242 0.334623 0.250484 \n", | |
| "\n", | |
| " HM5_L HM5_M HM5_W AM3_D AM3_L AM3_W AM4_D \\\n", | |
| "draw \n", | |
| "0 0.358422 0.028846 0.366711 0.263594 0.335544 0.400862 0.248011 \n", | |
| "1 0.386847 0.031915 0.330754 0.286267 0.325919 0.387814 0.263056 \n", | |
| "\n", | |
| " AM4_L AM4_W AM5_D AM5_L AM5_M AM5_W HT_Wins \\\n", | |
| "draw \n", | |
| "0 0.380968 0.371021 0.254310 0.359085 0.028846 0.357759 7.63992 \n", | |
| "1 0.393617 0.343327 0.262089 0.351064 0.031915 0.354932 7.15087 \n", | |
| "\n", | |
| " AT_Wins HT_Loss AT_Loss HT_Draws AT_Draws MW HT_LP \\\n", | |
| "draw \n", | |
| "0 7.624005 7.538462 7.535809 5.289125 5.307692 21.467507 9.996684 \n", | |
| "1 7.380077 7.627660 7.399420 5.378143 5.377176 21.156673 10.363636 \n", | |
| "\n", | |
| " AT_LP HTGD ATGD DiffPts DiffFormPts DiffLP \\\n", | |
| "draw \n", | |
| "0 9.824271 0.237069 0.121353 0.029178 -0.122679 -0.151525 \n", | |
| "1 10.100580 -1.100580 0.014507 -0.686654 -0.447776 0.421663 \n", | |
| "\n", | |
| " H2H_Home_pts H2H_Away_pts Mean_home_goals Mean_away_goals \\\n", | |
| "draw \n", | |
| "0 3.219496 3.454244 1.471761 1.148634 \n", | |
| "1 3.017408 3.652805 1.408149 1.141215 \n", | |
| "\n", | |
| " Total_value_H Total_value_A H2H_Diff Total_Diff Age_diff LP_Diff \n", | |
| "draw \n", | |
| "0 154.453710 148.772231 -0.234748 1.747164 0.009118 0.172414 \n", | |
| "1 136.155358 151.595986 -0.635397 1.441951 -0.027853 0.263056 " | |
| ] | |
| }, | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "warnings.simplefilter('ignore')\n", | |
| "df_final.groupby('draw').mean() " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zgrupowany zbiór danych według zminnej 'draw' w poszukiwaniu ciekawych wniosków.\n", | |
| "Większość zmiennych nie rózni się średnimi w danej kategorii.\n", | |
| "Poza nimi interesująca jest m.in. Zmienna HTGD (róznica bramek strzelonych i straconych drużyny gospodarzy w sezonie), która przyjmuje mniejszą wartość średniej dla grupy z draw = 1, aniżeli dla grupy z draw = 0.\n", | |
| "\n", | |
| "Zmienna HT_Draws w grupie z Draw = 0 przyjmuje średnią mniejszą aniżeli w grupie z Draw = 1. Taka sama sytuacja przedstawia się dla zmiennej AT_Draws. Co ciekawe różnic między tymi grupami są mniejsze aniżeli się tego spodziewałem. Mimo wszystko ciągle uważam, że któraś z tych zmiennych będzie wykorzystana przy budowie modelu." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Hxd4BshfrnCv9Yu4q2evtaudumYvVuMypndpb3jC+r25iaHe7RlEd76IkSYPSixZJbwCuBx5Vpt8JnJ6ZayLin4HjqJphHwfcnZlPiYgjS7pX9mD9kjTWmsejkDT66nJXyYXu8LjYO0DCwmPoDOKukr3ernbulrlYxzZ1ATuije+sH9/XYi0mhna3axTV8S5KkjQoXVUkRcSewIuBdwBvKgNCvgB4dUlyDnAKVUXS4eU1wAXAByIiSlNtSdIAWTklDU+/bq+tehmFQalHIUZJUv102yLpPcCbgUeW6d2AezJztk3rbPNraGianZlbImJTSX9H4wI7bZrdL3Vrtlq3eOChpuaz6hBf3fZT3eKBesYkSePOu0pKkqRR13FFUkQcBtyemddExPTs7BZJs433HprRYdPsfqlbs9W6xQPw/vMu4rS1Dx1K/WhGvVh12091iwfqGZMmm1fGNSG8q6TUwLxfkkZPNy2SDgReEhGHAg+nGiPpPcDSiFhSWiU1Nr+ebZq9ISKWAI8G7upi/ZKkNtmVTaoH7yopSZJG3XadfjAz35KZe2bmcuBI4MuZeRRwBfDykqy5afYx5fXLS3rHR5IkSZIkSRoRHVckzeMkqoG311GNgXRWmX8WsFuZ/yZgdR/WLUmSJEmSpD7pdrBtADJzBpgpr28A9m+R5uc81N9fkiRJkiRJI6YnFUmSpPHi4KeSJEmSWrEiSZLGQB0H07YySpIkqb9alQEtc6nfrEiSJA2EFUuSJEnS6LMiSZIkSZIk1YYXIOvNiiRJUkf8g5ckSZImz3bDDkCSJEmSJEmjwRZJkqSeqOOA35KkyWOLWUnqL1skSZIkSZIkqS22SJIkSZI0MmxxJEnDZYskSWMpIj4SEbdHxHcb5p0SETdHxLfK49CG994SEesi4gcR8aLhRC1JkjQ8lp8ktcOKJEnj6mzg4BbzT8/M/crjUoCI2Bc4Evit8pl/iojtBxapJElSPZyN5SdJC7AiSdJYyswrgbvaTH44sCYzf5GZPwbWAfv3LThJkqQasvwkqR2OkSRp0pwYEa8BvgGsysy7gWXAVQ1pNpR5W4mI44HjAaamppiZmWlrhZs3b247bSurVmzp+LOLNbVT6/W1ir/buJqX2by8hfZZt/t1kIy1P0YpVkkacR2Xn6DzMlS7RvX/oBdxt1tuW+gz82le3lzlxW6W2Wyx5cJ2TPJx0mtWJGnRmgc4XLViSIFIi3cG8A9AlufTgD8DokXa3GZG5pnAmQArV67M6enptlY6MzNDu2lbObbpN9dPq1Zs4bS12/41rD9qept53cbVvMzm5bVaZ6Nu9+sgGWt/jFKskjTCuio/QedlqHaN6v9BL+JuVR5bqAy12DJc8/Lef95FLcuL3Syz2WLLhe2Y5OOk1+zaJmliZOZtmflAZv4a+BAPNb/eAOzVkHRP4JZBxydpMjiYraRRYvlJUjNbJEmaGBGxR2ZuLJMvA2ZP4i4GPhER7waeAOwDfG0IIUqaDGcDHwDObZp/emb+Y+OMpsFsnwD8W0Q8NTMfGESgkmT5SXXQ3CsGYP2pLx5CJIIuKpIi4uHAlcCOZTkXZObJEbE3sAbYFbgWODozfxkRO1IVmJ4D3Am8MjPXdxm/JLUUEZ8EpoHdI2IDcDIwHRH7UTW7Xg+8HiAzr4uI84HvAVuAEzxJk9QvmXllRCxvM/mDg9kCP46I2cFsv9qn8CRNMMtPktrRTYukXwAvyMzNEbED8JWI+DzwJqoramsi4p+B46j61R4H3J2ZT4mII4F3Aq/sMn4NQKvaX6nuMvNVLWafNU/6dwDv6F9EkrSggd8MYKHBTDsZ7LTXy5xNPzu46zBj6OYz88UwtdNw92276RcbQzvbVYdjrBN1HPy2Fyw/1Z/nZg9p3he2UBqcjiuSMjOBzWVyh/JI4AXAq8v8c4BTqCqSDi+vAS4APhARUZYjSSosIEgTaSg3A1hoMNNOBjvt9TJn08/eDGCYMXTzmfliWLViC0e08Z31e7t6vW/b2a46HGOdqOPgtxpPlgtVR12NkRQR2wPXAE8BPgj8CLgnM2cvAzReNVsG3ASQmVsiYhOwG3BH0zL7emvIxarb1YZhxLPQrR2bb/9Yh/3l97awOsYkSZMqM2+bfR0RHwI+VyYdzFaSJNVKVxVJpQ/sfhGxFLgQeHqrZOW5r1fU+qVuVxuGEc9Ct4dsvl14P676LJbf28LqGJPULZs4a1Q5mK0kSRoVPblrW2beExEzwAHA0ohYUlolNV41m72itiEilgCPBu7qxfolSZNheVOXienhhSJ1zMFsJUnSKOvmrm2PBX5VKpF2Al5INYD2FcDLqe7cdgxwUfnIxWX6q+X9Lzs+kiRJmjQOZitJkkZZNy2S9gDOKeMkbQecn5mfi4jvAWsi4u3AN3moYHQW8LFy29q7gCO7WLckacQ5eKQkSdJkcjiC0dbNXdu+AzyrxfwbgP1bzP858IpO1ydJkiRJkqTh6skYSZIkSZI0rppbT5x98M5DikRaPFuBq9esSJpwNimUJEmSJEntsiJJkiRJ0sRq1VrDi6uSNLfthh2AJEmSJEmSRoMVSZIkSZIkSWqLXdskacgcAFGSJEmTzPLwaLEiSQPnAN+SJEmSJI0mK5IkSZIkSRoTtu5Rv1mRJEmqJQtBkiRJUv042LYkSZIkSZLaYoskSdLIstWSJEmSNFi2SJI0liLiIxFxe0R8t2HerhFxWUT8sDw/psyPiHhfRKyLiO9ExLOHF7kkSdJwWH6S1A4rktRzy1dfstVDGpKzgYOb5q0GLs/MfYDLyzTAIcA+5XE8cMaAYpQkSaqTs7H8JGkBdm2TNJYy88qIWN40+3Bgurw+B5gBTirzz83MBK6KiKURsUdmbhxMtJIkScNn+UmtNDcOWLViSIGoNqxI0lZsQaQxNzVbuMnMjRHxuDJ/GXBTQ7oNZd5WBaGIOJ7qihtTU1PMzMy0tdLNmzfPm3bVii3tRT8AUzu1jqdV/MOOe65YG7X7HfXbQsdAnRhr/0XER4DDgNsz8xll3q7Ap4DlwHrgiMy8OyICeC9wKHA/cGxmXjuMuCVNrK7KT9B5Gapdo/p/0E7cwy5vtdJOGWwYFtqX43ycDJoVSZIE0WJebjMj80zgTICVK1fm9PR0WwufmZlhvrTH1qgCd9WKLZy2dtu/hvVHTW8zb9hxzxVro1ZxD8NCx0CdGOtAnA18ADi3Yd5s15FTI2J1mT6JrbuOPI+q68jzBhqtJLXWVvkJOi9DtWtU/w/aiXvY5a1W2imDDcNC5b5xPk4GreNvPyL2oioAPR74NXBmZr7XK2pqZisn1chts02uI2IP4PYyfwOwV0O6PYFbBh6dpIlg1xFJI8byk6StdDPY9hZgVWY+HTgAOCEi9sXB2CTV18XAMeX1McBFDfNfU+4+cgCwyZM0SQO2VdcRYKGuI5I0KJafJG2l4xZJJZOYLfDcGxHXUxVsvKImaegi4pNUedHuEbEBOBk4FTg/Io4DbgReUZJfStVach1Vi8nXDjxgSWqtra4jnY4/0jzGRfPnFnp/EMucTT87JscwY+jmM/PFMLXTcPdtu+kXG0M721WnY2wxy6zjmCW9YPlJUjt60rGxNM9+FnA1QxrMtl/q9ifR63h6MUhat4Ot9WP/jvv31gt1jKmXMvNVc7x1UIu0CZzQ34gkaV5ddR3pdPyR5rE3mseXWOj9QSxzNv3smBzDjKGbz8wXw6oVWziije+s39vV633bznbV6RhbzDLPPnjn2o1Z0guWnyS1o+uKpIjYBfg08MbM/Fk1FFLrpC3m9Www236p28BWvY6nF4O3dTvYWj8Gwx33760X6hiTJE2w2a4jp7Jt15ETI2IN1SDbdh2RJElD1VVFUkTsQFWJdF5mfqbMdjA2SZKkOdh1RJI0q/nGROtPffGQIpHa181d2wI4C7g+M9/d8JZX1CRJkuZg1xFJkvqvuZLu7IN3HlIk46ebFkkHAkcDayPiW2XeW/GKmiRJkiRJ0ljq5q5tX6H1uEfgFTVJGivNV3QkSZK0eMubBqTv0f2v1Ia1N2/aaoxguxF2zqNWkqSiVYWZhQxJkiTpIdsNOwBJkiRJkiSNBlskSZIkSZJUAw4n0Dn33eBYkSRJmlgWOCRJkqTFsSJpzHmSJEmSJEmSesWKpBHXXFHkoLCSJEmSJKlfrEiSJEmSJKkP7CFSXzbK6Jx3bZMkSZIkSVJbbJGkoWtVS29tsCRJkiRJ9WOLJEmSJEmSJLXFiiRJkiRJkiS1xa5tkqSx5SCKkiRJUm9ZkSRJkiRJUpe8Q5smhRVJkiZORKwH7gUeALZk5sqI2BX4FLAcWA8ckZl3DytGjQ5bPUmSJoHlJ0mzrEhSLXlipgF4fmbe0TC9Grg8M0+NiNVl+qThhCZJklRLlp80tjwHbZ+DbUtS5XDgnPL6HOClQ4xF0oSKiPURsTYivhUR3yjzdo2IyyLih+X5McOOU5IKy0/SBOqqIikiPhIRt0fEdxvmtSzsROV9EbEuIr4TEc/uNnhJ6lACX4qIayLi+DJvKjM3ApTnxw0tOtXK8tWXbPWQBuD5mblfZq4s07NX/PcBLi/TkjRolp8kAd13bTsb+ABwbsO8uZo3HgLsUx7PA84oz9Ki2exQXTowM2+JiMcBl0XE99v5UCk0HQ8wNTXFzMxMWyvbvHnzvGlXrdjS1nIGYWqnesUzn05ibf4eOtnWhZbR6rte6BioE2OtpcOB6fL6HGAGu45IGryOyk/QeRmqXXX5P1hsuWKUyl2NJiXuOhxTUJ/ju1FXFUmZeWVELG+aPVdh53Dg3MxM4KqIWBoRe8zWYEvSoGTmLeX59oi4ENgfuG02T4qIPYDbW3zuTOBMgJUrV+b09HRb65uZmWG+tMfWqJXLqhVbOG3taAyf10ms64+a3mq6k32/0DKa34eFj4E6Mdahm73in8D/LvnOVlf8y0ncVjo9SVuoIrSditJ+L3M2/ewJwDBj6OYz88UwtdNw92276RcbQzvbVadjbDHLrOOJXb91Wn4qn+moDNWuuvwfLLZcMUrlrkaTEnerMt0w1OX4btSPb3+uws4y4KaGdBvKvK0qkvpdW71YdfuTaI6n+U/t/eddtNX0qhX9j2kQNdKLLSDU/XurgzrGNAgRsTOwXWbeW17/IfD3wMXAMcCp5fmiuZciSX3T0RX/Tk/SFqoIbaeitN/LnE0/ewIwzBi6+cx8MaxasYUj2vjO+r1dvd637WxXnY6xxSzz7IN3rt2JXT9ZfpLUaJDViNFiXm4zo8+11YtVt9q/5njq0JJhEDXSiy0g1P17q4M6xjQgU8CFEQFVHviJzPxCRHwdOD8ijgNuBF4xxBglTahurvhLUh9ZfpL0oH6c/c9V2NkA7NWQbk/glj6sX5LmlJk3AM9sMf9O4KDBR6RR44Db6hev+EuqK8tPkhr1oyJprsLOxcCJEbGGapDtTY6PpHZ54iZJmgBe8ZekGvEGP1JrXVUkRcQnqQbW3j0iNgAnU1UgtSrsXAocCqwD7gde2826JWlUWTE63ix0qlNe8ZckabRMarmv27u2vWqOt7Yp7JS7tZ3QzfokSZIkSZI0PKN3z74Jt/bmTbUYYFuS1J7lqy9h1YotD+bdk3KlSpIkSePJiiRJkgZoUptAS5IkaTxYkaSx0HxidvbBOw8pEkmSJEmSxpcVSZKkieFA55IkSVJ3tht2AJIkSZIkSRoNtkiSpD5zkHxJkqTxY0vnyeN3XrEiaYgccLV/mk/c3beSJEmS5mMlgdQeK5IkSRqiVoVWK78lSZJGz6Q0FrEiqeaaD8RVK4YUyIiblB+0pPFgniVJkqS6siJJE8kWAJJGmRVNkiRJGhYrkmrEPrmSJEmSVE+er00Wv++5WZHUR14xliT1Qq8LMrbKlCRJUqesSBogazQlSZIkqR48P5M6Y0WSNIeFWpTZ4kySJEmSNJdxPWe0IqmHrNEebQt9f4t9v51MYlwzFkmSJEnS1sbl/M+KJEmSRtxCFd2dFFL6sUxJkobJC/+qm1GtWBp4RVJEHAy8F9ge+HBmnjroGKRBaMwUVq3YwrEd/HGNasYyqsyfNK76UXA2fxos8ydJdVWX/Mn/JWlwBlqRFBFrvgeWAAAgAElEQVTbAx8E/gDYAHw9Ii7OzO8NMg7ozZVWa7TVax5Tw1On/EkaRRbg+8f8SVJd9TN/Wsx4patWbKH51NZytUZRq+P27IN3XtRnBlEGG3SLpP2BdZl5A0BErAEOB3qe0fRqeZ22JJH6oZcVoLPHtid7D+pb/iSNgl7/77Xzv+xNDNpm/iSprgaWP1kxpEm19uZNtauTiMwc3MoiXg4cnJmvK9NHA8/LzBMb0hwPHF8mnwb8YGABtrY7cMeQY2hUt3jAmNpRt3hg8TE9MTMf269ghq3P+VMdv/+5GGt/GGt/zMZq/lS/8lM/jNKxuVjjum3jul3Q/rZNfP5U5vc7jxrVY824B8u4t9Zx/jToFknRYt5WNVmZeSZw5mDCWVhEfCMzVw47jll1iweMqR11iwfqGdOQ9S1/GqV9baz9Yaz9MUqxdmnkyk/9MM7f97hu27huF4z3ti3SgvkT9D+PGtXvw7gHy7h7Z7sBr28DsFfD9J7ALQOOQZJaMX+SVFfmT5LqyvxJmkCDrkj6OrBPROwdEQ8DjgQuHnAMktSK+ZOkujJ/klRX5k/SBBpo17bM3BIRJwJfpLo95Ecy87pBxtCBujUTr1s8YEztqFs8UM+YhqbP+dMo7Wtj7Q9j7Y9RirVjI1p+6odx/r7HddvGdbtgvLetbTXKn0b1+zDuwTLuHhnoYNuSJEmSJEkaXYPu2iZJkiRJkqQRZUWSJEmSJEmS2mJF0jwi4q8jIiNi9zIdEfG+iFgXEd+JiGcPMJb/NyK+X9Z7YUQsbXjvLSWmH0TEiwYY08FlnesiYvWg1tsUw14RcUVEXB8R10XEG8r8XSPisoj4YXl+zIDj2j4ivhkRnyvTe0fE1SWeT5XBCAcZz9KIuKAcQ9dHxO8Mex9Ngjr8RuZS19/OfOr2u5rPKP3mIuKvyjHw3Yj4ZEQ8vC77NiI+EhG3R8R3G+a13I/D/I9W/0XE+ohYGxHfiohvDDuebizmuB4lc2zXKRFxc/nevhURhw4zxk6M4v/lpIumc7i6i3nO8+qozuXbucz1Ox4VzWXgOrAiaQ4RsRfwB8CNDbMPAfYpj+OBMwYY0mXAMzLzt4H/BN5S4tyX6u4IvwUcDPxTRGzf72DKOj5ItU/2BV5VYhm0LcCqzHw6cABwQoljNXB5Zu4DXF6mB+kNwPUN0+8ETi/x3A0cN+B43gt8ITN/E3hmiW3Y+2is1eg3Mpe6/nbmU7ff1XxG4jcXEcuAvwRWZuYzqAZKPZL67Nuzqf7bGs21H4f5H63BeH5m7peZK4cdSJfOpv3jepSczbbbBVVesl95XDrgmHphFP8vJ9Yc53B11/I8r45GoHw7l7l+x6OiuQw8dFYkze104M1A42jkhwPnZuUqYGlE7DGIYDLzS5m5pUxeBezZENOazPxFZv4YWAfsP4CQ9gfWZeYNmflLYE2JZaAyc2NmXlte30v1A1tWYjmnJDsHeOmgYoqIPYEXAx8u0wG8ALhgSPE8Cvh94CyAzPxlZt7DEPfRhKjFb2QudfztzKduv6v5jOBvbgmwU0QsAR4BbKQm+zYzrwTuapo9134c2n+0tBiLPK5HxhzbNfJG7f9SLc/ham2e87w6qnX5di7z/I5rr7kMXBdWJLUQES8Bbs7Mbze9tQy4qWF6A8M5AP8M+Hx5PayY6rIvHhQRy4FnAVcDU5m5EaqMA3jcAEN5D9Uf2K/L9G7APQ1/EIPeV08Cfgp8tDSJ/HBE7Mxw99EkqN1vZC41+u3Mp26/q/mMzG8uM28G/pHqyu1GYBNwDfXdtzD3fhyZ35w6ksCXIuKaiDh+2MH0Qe3yhx46sXTZ+ciod/8akf/LiTXPOdwoaTzPq6OR/69t+h2PguYycC1MbEVSRPxbGQ+i+XE48DfA21p9rMW8ntV2LxDTbJq/oWqad94gYpov3CGtt6WI2AX4NPDGzPzZEOM4DLg9M69pnN0i6SD31RLg2cAZmfks4D5sfj0Iw/7e21KX3858avq7ms/I/ObKSd3hwN7AE4CdqZqrN6vLvp1PnY8Jde/AzHw21fF5QkT8/rADUlvOAJ4M7EdVWX3acMPp3Cj8X06CDs/hhq7D87w6Gun/2lH7Hc9RBq6FJcMOYFgy84Wt5kfECqoC9bernhPsCVwbEftT1bju1ZB8T+CWfsfUENsxwGHAQZk5+4Pta0zzGNZ6txERO1BlCOdl5mfK7NsiYo/M3Fi6Ntw+oHAOBF5SBpN8OPAoqlrkpRGxpFzhH/S+2gBsyMzZWvcLqE5qh7WPJkVtfiNzqdlvZz51/F3NZ5R+cy8EfpyZPwWIiM8Av0t99y3MvR9r/5tT5zLzlvJ8e0RcSNW94srhRtVTdcwfupaZt82+jogPAbUZKHYxRuj/cux1cg6XmbcOMMSWOjzPq6OR/a+d43dcd9uUgSPi45n5p0OOa3JbJM0lM9dm5uMyc3lmLqf6sTy7ZEAXA6+JygHAptkmrf0WEQcDJwEvycz7G966GDgyInaMiL2pBhn92gBC+jqwT1R39nkY1eCsFw9gvVsp46ScBVyfme9ueOti4Jjy+hjgokHEk5lvycw9y7FzJPDlzDwKuAJ4+aDjKTHdCtwUEU8rsw4CvseQ9tEEqcVvZC51++3Mp46/q/mM2G/uRuCAiHhEOSZmY63lvi3m2o9D+49Wf0XEzhHxyNnXwB8C353/UyOnjvlD15rGKXsZI/i9jdL/5SRb4Byu1uY5z6ujWpdv5zLP77jW5igDD70SCSDqXeE5fBGxnupuNneUA/ADVHekuB94bWYO5Ba0EbEO2BG4s8y6KjP/orz3N1T9abdQNdMbSL/aUjP6Hqq7/HwkM98xiPU2xfB7wH8Aa3mo3+hbqfq8ng/8BtWJ0isyc6ADQEbENPDXmXlYRDyJajC6XYFvAn+amb8YYCz7UQ3Q9jDgBuC1VBXJQ91H464Ov5G51Pm3M586/a7mM0q/uYj4O+CVVP8h3wReRzXewdD3bUR8EpgGdgduA04GPkuL/TjM/2j1V/mtX1gmlwCfqFN+uliLOa6HFWMn5tiuaapubQmsB14/ahW8o/p/Oekaz+GGHctC5jvPq6M6l2/nMtfvOEfoTpKNZeBhxwJWJEmSJEmSJKlNdm2TJEmSJElSW6xIkiRJkiRJUlusSJIkSZIkSVJbrEiSJEmSJElSW6xIkiRJkiRJUlusSJIkSZIkSVJbrEiSJEmSJElSW6xImmAR8c8R8b8apv9HRNwWEZsjYreIODAiflimXzrMWCWpE+ZzkurGfEmSNOqsSBpjEbE+Iv4rIu6NiHsi4v9ExF9ExHYAmfkXmfkPJe0OwLuBP8zMXTLzTuDvgQ+U6c9GxExE/LwUbGYfvzOgbcmIuK+s8+aIeHdEbN+wnS8cRByS6qVP+dzrWqxnecmHZvO+9RGxepDbKmk09Ln8dUdEfCYi9iifPzsi3j6sbZXUO63OaSLi2Ij4SkTsGBFnRcRPSt7yzYg4pCHddERsaLHMluWaOda/d0T8OiL+qfutGQzPA4fHiqTx90eZ+UjgicCpwEnAWS3STQEPB65rmPfEpmmAE0vBZvbx1cUEE5VOj7tnZuYuwEHAq4E/73A5ksZLr/O5+Swt+dCrgLdFxMGdhSxpzPWl/AU8FVgKnN7ziCXV2RLgJuC/A48G/hdwfkQs7+E6XgPcDRwZETv2cLkaQ1YkTYjM3JSZFwOvBI6JiGfMXsWKiKcCPyhJ74mIL0fEj4AnAf9aroDNm5lExO9GxNcjYlN5/t2G92Yi4h0R8f8B9wNPKvPeXq7SbY6Ify3Nuc+LiJ+VZSyfY1u+D/wH8IyI+BjwGw1xvjkiHh4RH4+IO8uVwK9HxFR3e1BS3fU7n2ta11epTvSe0evtkDQ+ep0vZeZdwKepykDHA0cBb54tSwFExEml9fa9EfGDiDhoUNsrqT8y877MPCUz12fmrzPzc8CPgef0cDWvAf4W+BXwR7MzI+LvIuL95fUOUfUSeVeZ3qm0mHxMmf6XiLi1nBNeGRG/VeY/N6ouvEsalvsnEfGt+QKKiFMi4oKI+FTJ066NiGeW9zwPHCIrkiZMZn4N2AD8t4Z5/wn8VplcmpkvyMwnAzdSXVHbJTN/MdcyI2JX4BLgfcBuVE20L4mI3RqSHQ0cDzwS+EmZd2SZvwx4MvBV4KPArsD1wMlzrG/fEv83M/PopjjfBRxDVVO/V4nnL4D/WnjvSBoH/cjnGpWWlQeW5X2zt9FLGke9ypciYnfgT6jKQGcC5wHvKmn/KCKeBpwIPLe0iHoRsL7PmydpwErlyFNZXKvq+Zb334A9gTXA+VSVSrP+HZgur58L3ErVMgrgd4AfZObdZfrzwD7A44BrqfIoMvPrwJ3AHzQs90+Bj7UR3uHAv1CdI34C+GxE7OB54HBZkTSZbqH6IXbifaV2956IuLbMezHww8z8WGZuycxPAt+noSYbODszryvv/6rM+2hm/igzN1FlOj/KzH/LzC1UmcWzmtZ9bUTcDfwr8GGqSqdWfkWVcTwlMx/IzGsy82cdbq+k0dRNPjefO4C7qPKg1Zl5eR/WIWk8dV3+Ar4NbATeNEe6B4AdgX3Lidb6zPxRh+uUNHifbTjXugfYZryiqMZWOw84p/TUmPWExs+Wz/9em+s9Bvh8qRD6BHBIRDyuvPdVYJ/SSOD3qbrpLouIXagqlP59diGZ+ZHMvLdUgp8CPDMiHl3ePoeq8mi2IcKLyroWck1mXlDOId9N1R34gDnSeh44IFYkTaZlVCdCnfjLzFxaHs8u857AQ62MZv2krGfWTS2WdVvD6/9qMb1LU/pnZ+ZjMvPJmfm3mfnrOWL8GPBFYE1E3BIR7yoZrqTJ0U0+N5/dSz709Mx8Xx+WL2l89aL8tSwzj8rMn7ZKlJnrgDdSncDdHhFrIuIJHa5T0uC9tOFcaynwfzW+GdVYsx8DfknV+rDRLY2fLZ//ykIrjIidgFfwUOuhr1K19Hl1mf4v4BtUlUa/T1Vx9H+AA2moSIqI7SPi1Ij4UUT8jIdaQ+5enj8O/FGpgDoC+I/M3NjGPnnwPLKc/22gOv9sxfPAAbEiacJExHOpCjILZiqLcAvVwJCNfgO4uWE6e7i+ZlstOzN/lZl/l5n7Ar8LHMbWzTMljbE+5XOS1LE+5kvblK8y8xOZ+XtUZbME3tnjdUoagogIqtZAU8CfNPTy6NbLgEcB/1TGN7qVKr9q7t72AqoeI18v0y8C9geuLGleTdUN7YVU3cuWz4YOkJk3U7VuehnV8CbtdGuDqptataCqIm1PqvNP8DxwaKxImhAR8aiIOIyq3+vHM3NtDxd/KfDUiHh1RCyJiFcC+wKf6+E65nMb1cCUAETE8yNiRURsD/yMqonjAwOKRdKQ9DCfW1IGa5x9eCVLUkf6XP6CbctAT4uIF5RBun9O1cLbMpA0Hs4Ank41JlAvx/05BvgIsALYrzwOBPaLiBUlzb9TVch8LzN/CcwArwN+3NBC8pHAL6jGQnoE8H+3WNe5wJvLui5sM77nRMQfl4G631jWcVV5z/PAIbEiafz9a0TcS9Uk8G+o+pW+tpcryMw7qWp7V1FlHG8GDsvMO3q5nnn8P8Dflr7Afw08HriAKvO4nirj+/iAYpE0eL3O586gOvmafcw1HpskzaXv5a/iLKrxkO6JiM9SjY90KtV4brdSDXj71j6sV9IARcQTgddTVfLcWu5StjkijupyucuAg4D3ZOatDY9rgC9QVTJB1ZVtJx5qffQ9qsrqKxsWdy7V8CY3l/evYlsXUrWWvDAz72szzIuo7nx5N1VLpj9uaI3leeCQRGY/exxJkiRJkiRBRPwIeH1m/lsbaU+hGjj7T/semBbFFkmSJEmSJKmvIuJPqMY1+vKwY1F3rEiSJEmSJGlMRcRbG7rDNT4+P8AYZqiGDzih8e7bEfH5OWKzW26N2bVNkiRJkiRJbbFFkiRJkiRJktqyZNgBzGf33XfP5cuXt5X2vvvuY+edd+5vQCMUB9QnlrrEAfWJpS5xQPuxXHPNNXdk5mMHENJIWEz+NCrqdFwOits8HsyftlaX/Knux1qd4zO2ztQxNvOnbbWbR9Xx+2yHcQ+WcXeum/yp1hVJy5cv5xvf+EZbaWdmZpienu5vQCMUB9QnlrrEAfWJpS5xQPuxRMRP+h/N6FhM/jQq6nRcDorbPB7Mn7ZWl/yp7sdaneMzts7UMTbzp221m0fV8ftsh3EPlnF3rpv8ya5tkiRJkiRJaosVSZIkSZIkSWqLFUmSJEmSJElqixVJkiRJkiRJaosVSZIkSZIkSWpLre/apvGw9uZNHLv6kgen15/64iFGI0lzW96QV80yz5LUD835jXmN1H/+7qTesCJJkjS2LDBKkiRJvWXXNkmSJEmSJLXFiiRJkiRJkiS1xYokSZIkSZIktcWKJEmSJEmSJLXFiiRJkiRJkiS1xYokSZIkSRIR8VcRcV1EfDciPhkRD4+IvSPi6oj4YUR8KiIeVtLuWKbXlfeXDzd6SYNiRZIkSZIkTbiIWAb8JbAyM58BbA8cCbwTOD0z9wHuBo4rHzkOuDsznwKcXtJJmgBWJEmSJEmSAJYAO0XEEuARwEbgBcAF5f1zgJeW14eXacr7B0VEDDBWSUNiRZIkSZIkTbjMvBn4R+BGqgqkTcA1wD2ZuaUk2wAsK6+XATeVz24p6XcbZMyShmPJsAOQJEmSJA1XRDyGqpXR3sA9wL8Ah7RImrMfmee95mUfDxwPMDU1xczMzILxbN68ua10i7FqxZatpnu9fOhP3INg3IM1qnHPsiJJkiRJqpnlqy/ZZt76U188hEg0QV4I/DgzfwoQEZ8BfhdYGhFLSqujPYFbSvoNwF7AhtIV7tHAXa0WnJlnAmcCrFy5MqenpxcMZmZmhnbSLcaxTb+r9Uf1dvnQn7gHwbgHa1TjnmXXNkmSJEnSjcABEfGIMtbRQcD3gCuAl5c0xwAXldcXl2nK+1/OzJYtkiSNFyuSJEmSJGnCZebVVINmXwuspTpXPBM4CXhTRKyjGgPprPKRs4Ddyvw3AasHHrSkobBrmyRJkiSJzDwZOLlp9g3A/i3S/hx4xSDiklQvXbdIiojtI+KbEfG5Mr13RFwdET+MiE9FxMPK/B3L9Lry/vJu1y1JrUTEXhFxRURcHxHXRcQbyvxdI+Kykj9dVgaVJCrvK/nTdyLi2cPdAknjzvKTJEkaVb3o2vYG4PqG6XcCp2fmPsDdwHFl/nHA3Zn5FOD0kk6S+mELsCoznw4cAJwQEftSNbm+vORPl/NQE+xDgH3K43jgjMGHLGnCWH6SJEkjqauKpIjYE3gx8OEyHcALqPrWApwDvLS8PrxMU94/qKSXpJ7KzI2ZeW15fS/Vydoyts6HmvOnc7NyFdXdSfYYcNiSJoTlJ0mSNMq6HSPpPcCbgUeW6d2Ae8qtIaG6JeSy8noZcBNAZm6JiE0l/R2NC4yI46laBDA1NcXMzExbgWzevLnttP1UlzigPrFM7QSrVmx5cHqYMdVln9QlDqhXLP1QuoE8C7gamMrMjVBVNkXE40qyB/OnYjbv2ti0rI7yp1ExjsdCY94D2+Y/zdvcnL7VZ0bdOH7PI6g25ad+qvuxNuz45sufNm/ezKoVD2zzmTrsz2Hvt/nUOTZJGicdVyRFxGHA7Zl5TURMz85ukTTbeO+hGZlnUt0dgJUrV+b09HRzkpZmZmZoN20/1SUOqE8s7z/vIk5b+9Chtv6o6aHFUpd9Upc4oF6x9FpE7AJ8GnhjZv5snov4fc2fRsU4HgvHrr5kq+nm/Kd5m5vTt/rMqBvH73mU1K381E91P9aGHd98+dPMzAynfeW+bT5Th/xo2PttPnWOTZLGSTctkg4EXhIRhwIPBx5FdYVtaUQsKVfV9gRuKek3AHsBGyJiCfBo4K4u1i9Jc4qIHagqkc7LzM+U2bdFxB6lNdIewO1l/mz+NKsx75KkXrL8JEmSRlrHYyRl5lsyc8/MXA4cCXw5M48CrgBeXpIdA1xUXl9cpinvfzkzt7miJkndKuOHnAVcn5nvbnirMR9qzp9eU+7edgCwabYLnCT1kuUnSZI06rodI6mVk4A1EfF24JtUJ3OU549FxDqqK2lH9mHdkgTVFf+jgbUR8a0y763AqcD5EXEccCPwivLepcChwDrgfuC1gw1Xkiw/SZKk0dCTiqTMnAFmyusbgP1bpPk5D520SVLfZOZXaD2uCMBBLdIncEJfg9LIWt48jsmpLx5SJBo3lp8kSQuxHKI66keLJEmShqK5sDWMdVrAkyRJ0jjreIwkSZIkSZIkTRZbJEmSRoatfyRJkqThskWSJEmSJEmS2mJFkiRJkiRJktpi1zZJknrI7neSJEkaZ7ZIkiRJkiRJUlusSJIkSZIkSVJbrEiSJEmSJElSWxwjSQtyvA9JkiRJkgRWJEmSaqq5EluSJp0X9yT1w/LVl7BqxRaOLXmMeYsWYkWSBs5CkCRJklQ/EbEU+DDwDCCBPwN+AHwKWA6sB47IzLsjIoD3AocC9wPHZua1Qwhb0oBZkaSuWTEkSZIkjYX3Al/IzJdHxMOARwBvBS7PzFMjYjWwGjgJOATYpzyeB5xRniWNOQfbliRJkqQJFxGPAn4fOAsgM3+ZmfcAhwPnlGTnAC8trw8Hzs3KVcDSiNhjwGFLGgJbJEmSJEmSngT8FPhoRDwTuAZ4AzCVmRsBMnNjRDyupF8G3NTw+Q1l3sbmBUfE8cDxAFNTU8zMzCwYzObNm9tKtxirVmzZarrXy4fexz2ImFet2MLUTg+tqx/r6Jd+HCeDMKpxz7IiSZIkSZK0BHg28D8z8+qIeC9VN7a5RIt52SphZp4JnAmwcuXKnJ6eXjCYmZkZ2km3GMc2D8lxVG+XD72PexAxH1sG2z5t7ZK+raNf+nGcDMKoxj3Lrm2SJEmSpA3Ahsy8ukxfQFWxdNtsl7XyfHtD+r0aPr8ncMuAYpU0RB23SIqIhwNXAjuW5VyQmSdHxN7AGmBX4Frg6Mz8ZUTsCJwLPAe4E3hlZq7vMn71mLfb1riIiI8AhwG3Z+YzyrxTgD+narYN8NbMvLS89xbgOOAB4C8z84sDD1oTqVW+600LxpflJ0l1lZm3RsRNEfG0zPwBcBDwvfI4Bji1PF9UPnIxcGJErKEaZHvTbBc4SeOtm65tvwBekJmbI2IH4CsR8XngTcDpmbkmIv6Z6sTsjPJ8d2Y+JSKOBN4JvLLL+CVpLmcDH6A6AWt0emb+Y+OMiNgXOBL4LeAJwL9FxFMz84FBBKrJYoX9xLP8JKnO/idwXrlj2w3Aa6l6sZwfEccBNwKvKGkvBQ4F1gH3l7SSJkDHXdvK6Pyby+QO5ZHAC6iaQcK2o/rPjvZ/AXBQRLTqVytJXcvMK4G72kx+OLAmM3+RmT+mKhDt37fgJE0sy0+S6iwzv5WZKzPztzPzpZl5d2bemZkHZeY+5fmukjYz84TMfHJmrsjMbww7fkmD0dVg2xGxPdVo/k8BPgj8CLgnM2eHlp8duR8aRvXPzC0RsQnYDbijmxgkaZFOjIjXAN8AVmXm3VT501UNaRrzrgd1cseRUdLt3SPW3rxpq+kVyx7dVfrmu5S00hzvQp9pTt+8zYNYZyfr6KVRv0vIOOhH+amO+VPdj7VhxzffnZg2b97MqhULN4odRvzD3m/zqXNskjROuqpIKt0+9ouIpcCFwNNbJSvPbY3q32lBqC5/HHWJAzqLpRcnUc3vN95Ksp3l9VNdvp+6xAH1imUAzgD+gSrv+QfgNODPaDN/6uSOI6Ok27tHLHRXkW27dG39F9Scvnl5rSz2M83pm7d5EOvsZB29NOp3CRkH/Sg/1TF/qvuxNuz45sszZ2ZmOO0r9y24jGHcWWnY+20+dY5NksZJVxVJszLznoiYAQ4AlkbEknJVrXHk/tlR/TdExBLg0bTodtJpQagufxx1iQM6i6UXJ1HN77//vIsevJVkO8vrp7p8P3WJA+oVS79l5m2zryPiQ8DnyqR3HVGtNVfCORj3eOhl+Un14+9WkjSuOh4jKSIeW66kERE7AS8ErgeuAF5ekjWP6n9Mef1y4MuZuc0VNUnql9lb1xYvA75bXl8MHBkRO5Y7J+0DfG3Q8Ukaf5afNJflqy958NHc9VeSpDrppkXSHsA5pZ//dsD5mfm5iPgesCYi3g58EzirpD8L+FhErKO6knZkF+uWpHlFxCeBaWD3iNgAnAxMR8R+VN1C1gOvB8jM6yLifKrb224BTvCObZL6xPKTJEkaaR1XJGXmd4BntZh/Ay3udpSZP+ehW0VKUl9l5qtazD6rxbzZ9O8A3tG/iCTJ8pMkSRp9HXdtkyRJkiRJ0mTpyWDb0qA5gKUkSZIkSYNniyRJkiRJkiS1xYokSZIkSZIktcWubZKkibX25k3/P3v3Hi9ZWd/5/vPV9ooXRLSDQNImkkyMHS/pg2SYyXTEKKJHzBw1OMTQhoTMa2Cik55XbJyZ6ESdgzPxQjQxIULEBEGCeuCIUQnax3EmEASJiGjsYEcaOqBy0dZEs/V3/qi1obqo3bt27apaq/b+vF+v/dq1nlqXX13WU0/96nmexbaBobKSJEmSlmaPJEmSJEmSJI3ERJIkSZIkSZJGYiJJkiRJkiRJI3GOJHXSpoE5S3af9fyWIpEkSZIkSYtMJEmStMaYjJckSdK0mEiSJI1lMFkhSVqadaYkaa0wkbTO+Cu1pFFYV3Sbr48kSZLa4mTbkiRJkiRJGomJJEmSJEkSAEkemOQzST7ULD8xydVJvpTkfUke3JQ/pFne1dy/qc24Jc2OQ9skSZKkNcBhr5qQVwI3AY9qlt8EvLWqLkryB8CpwDub/3dV1ZOSnNSs9wttBCxptuyRJEmSJEkiyRHA84F3NcsBngVc0qxyPvCi5vaJzTLN/cc160szsWnH5dxw6z1s2rSiUQwAACAASURBVHG5FzSYMXskaU3wFzhJkiRp1d4G/CbwyGb5scDdVbXQLO8BDm9uHw7cAlBVC0nuadb/2uzCldQGE0mSJEnSKvlruOZdkhcAd1TVtUm2LhYPWbVGuG9w36cBpwFs3LiRnTt3LhvPvn37RlpvJbZvXthvedL7h8nHPYuYt29eYOPD7jvWNI4xDfMaN0zn/T1LYyeSkhwJvAf4AeD7wDlVdXaSQ4D3AZuA3cBLq+quppvj2cAJwLeBbVV13erCl6ThkpwHLDaIntKUWT8tYdOOy9m+eYFtfhGSpsr2k6QOOxZ4YZITgIfSmyPpbcDBSTY0vZKOAG5r1t8DHAnsSbIBeDRw57AdV9U5wDkAW7Zsqa1bty4bzM6dOxllvZUYbOfsPnmy+4fJxz2LmLc17cA337BhaseYhknHPctRLtN4f8/SauZIWgC2V9WPA8cApyd5MrADuLKqjgKubJYBngcc1fydRm+CNkmalncDxw+UWT9pXVqcO2BxLgG1yvaTpE6qqjOr6oiq2gScBHy8qk4GPgG8uFntFODS5vZlzTLN/R+vqqE9kiStLWMnkqpq7+IvYlX1TXoz+x/O/pOuDU7G9p7quYpeZvuwsSOXpAOoqk9y/1/FrJ8ktcr2k6Q59GrgN5LsojcH0rlN+bnAY5vy3+C+BLikNW4icyQl2QQ8Hbga2FhVe6HXWEry+Ga1eydjayxO1LZ3YF8rHj8L3Rlj2JU4YHgsy42xHbx/mJXus3/c6ij7m0ScS70GXXl9uhIHdCuWGWilfuqCwV4omw9/9H7Lg2PMB41yni63zaTXn8QxlqufpnHMWTyuA62/8WHzNYfAWtaF9tM0df3zZdrzmKzGqHXTSttkk9Dl17XLsc2bqtoJ7Gxu3wwcPWSdfwReMq0Ybrj1nv2GdXkxHak7Vp1ISvII4P3Aq6rqGwe44uNIk7GNM34WujPGsCtxwPBYlhtjO8r8KMttM3j/2y+49N5xq6PsbxJxLjU+tiuvT1figG7F0qKp1k9dMMo51T/GfNAo5+ly20x6/UkcY7n6aRrHnMXjOtD62zcv8NI5eu+uVV1pP01T1z9fpj2PyWocqD7ut9I22SR0+XXtcmyStJasZo4kkjyIXiPogqr6QFN8+2KX6+b/HU354mRsi/onapOkWbB+ktQ620+SJGmejZ1Iaq4ici5wU1W9pe+u/knXBidj+6X0HAPcs9iFW5JmxPpJUqtsP0mSpHm3mqFtxwIvB25Icn1T9hrgLODiJKcCX+G+cbMfpnfp2l30Ll/7ilUcW0vov2Th9s0LbG0vFKlVSS4EtgKHJtkDvBbrJ0nts/2kmZnlpawlSevH2ImkqvoUw8ftAxw3ZP0CTh/3eJK0ElX1siXusn6ShvAL52zYfpIkSfNuIldtk7pu8QvS9s0LbNtxuV+QtOYNJgUkSZIkaRJMJM05vyxKkqbBHkqSJEkaxkSSJEmSNIf8QVGS1Iaxr9omSZIkSZKk9cVEkiRJkiRJkkbi0DZJkiTpAIYNIXPeMEnSemWPJEmSJEmSJI3ERJIkSZIkSZJGYiJJkiRJkiRJI3GOJEmStGKDc8Y4X4wkSdL6YCJJavilSJIkSZKkAzORJElzxqsHSZIkSWqLcyRJkiRJkiRpJCaSJEmSJEmSNBKHtkmSJEmStEqbdlzO9s0LbOubhsDpB7QWmUiSpI4bNieSJEnSJCU5EngP8APA94FzqursJIcA7wM2AbuBl1bVXUkCnA2cAHwb2FZV17URu7rHCxmtbSaSJEnSqtlg1FpiAl/r1AKwvaquS/JI4NokVwDbgCur6qwkO4AdwKuB5wFHNX/PBN7Z/Je0xq0qkZTkPOAFwB1V9ZSmzIy1pE5Lshv4JvA9YKGqtixVd7UVo7TWeLXB+9h+ktRFVbUX2Nvc/maSm4DDgROBrc1q5wM76SWSTgTeU1UFXJXk4CSHNfuRNCNt/Ji32h5J7wbeQa8L5KIdmLGW1H0/W1Vf61tequ6SpEl7N7afJHVYkk3A04GrgY2LyaGq2pvk8c1qhwO39G22pym7XyIpyWnAaQAbN25k586dy8aw8WGwffPCvcujbLOc/v1Nap+D+5903NOOefEY/XHPw3O9eIxJxj2LmBft27dvYvufZdyLVpVIqqpPNpVMPzPWkubRUnXX1DkkSFpfbD9J6rIkjwDeD7yqqr7R6xg5fNUhZTVsxao6BzgHYMuWLbV169Zl43j7BZfy5hvu+7q6++Tlt1nOtsE21wT2Obj/7ZsXJhr3tGNePEZ/3PPwXC8eY5JxzyLmRTt37mSU82AUs4x70TTmSFpVxnqcbDVMNqO3GrOOYzD72G/jw+6fjVwuW3mg/Y26zeD9g1n55fY3iTiXWn8xlnGOOUldeb9Ct2KZoQI+lqSAP2waN0vVXfcat35azizOy1HWP9C5Oso5M42YlrPaYyxXP03jmLN6PZcy7LNh0scYdf11WPccSCvtp2nq+ufLgeKbxnm6EuPUTaOYxOvR5de1y7HNiyQPopdEuqCqPtAU376YwE5yGHBHU74HOLJv8yOA22YXraS2zHKy7ZEy1uNkq2GyGb3VmHUcg9nHfts3L/DSgViWy1YeaH+jbjN4/+CvCcvtbxJxLrX+YsZ6lGNyw7f23+cEe4l05f0K3Yplho6tqtuaL2pXJPnCKBuNWz8tZxbn5SjrD/6CdqD1xz3GJNefxDGWq5+mccxZvZ5LGfbZMOljjLr+LH4xWwOm2n6apq5/vhwovlHO08F2wiSb1Qeqj1djEudcl1/XLsc2D5o52c4Fbqqqt/TddRlwCnBW8//SvvIzklxEb8jtPfaWlNaHaSSSzFhL6rSquq35f0eSDwJHs3TdJUmzYPtJU7fSq9E51HrdORZ4OXBDkuubstfQSyBdnORU4CvAS5r7PkzvQgC76F0M4BWzDVdSWx4whX0uZqzh/hnrX0rPMZixltSCJAc1l7QlyUHAc4DPsXTdJUmzYPtJUquq6lNVlar6yap6WvP34ar6elUdV1VHNf/vbNavqjq9qn6kqjZX1afbfgySZmNVPZKSXEhvYshDk+wBXosZa0ndthH4YDNx5AbgvVX1kSTXMLzukqSJsv0kSZLm2Wqv2vayJe46bsi6BZy+muOtR17NSZqsqroZeOqQ8q8zpO6SpEmz/SRJkubZNIa2SZIkSZIkaQ2a5VXbNMDeRpIkSZIkaZ7YI0mSJEmSJEkjsUeSJEmS1pVNOy5n++YFtjW9w+0VPtxg73mAdx9/UAuRSJK6xESSNCaHJkqSJEmS1huHtkmSJEmSJGkk9kiaoWHdg7V22WNJkiRJkrTWmEiSJEnSuuaPfZIkjc5EkiRJkqSx2ANbktYf50iSJEmSJEnSSOyRJEmSpDXNoWuTc8Ot97DN51OS1jUTSStg111JkiRJkrSemUiaoE07Lmf75oV7f6Ux0SRJkjR9/tgnSdLsmEiSZqS/kbuYcBxs6NoQliRJ88y2jCStfU62LUmSJEmSpJHYI0mSZsxJXyVJkiTNKxNJkiRJWlNM2HeHQ90kae0xkSRJklrhF0yNwqSQJEndMvNEUpLjgbOBBwLvqqqzZh3DKGy0SOvPvNRPktaftVQ/LZdAtA22to2TQF7uPWESul1rqX6SNJqZJpKSPBD4PeDngD3ANUkuq6rPzzIO8FdQSfvrUv0kSf3mvX4yMaQD8f0x3+a9fpI0nln3SDoa2FVVNwMkuQg4EbCikUaw0l/khq1v0nRJU6ufbCRLo/FHniW1Vj8t97ky7DVaXGf75gW2jVD/WUdqtVb6nrNumSi/30nrUKpqdgdLXgwcX1W/0iy/HHhmVZ3Rt85pwGnN4o8BXxxx94cCX5tguOPqShzQnVi6Egd0J5auxAGjx/JDVfW4aQfTlinXT/OiS+/LWfExrw3WT92sn7r+XutyfMY2ni7Gtu7rp6Z8nDqqi6/nKIx7tox7fGPXT7PukZQhZftlsqrqHOCcFe84+XRVbRk3sEnpShzQnVi6Egd0J5auxAHdiqVlU6uf5sV6fC/4mDUn5rJ+6vp7rcvxGdt4uhzbGrZs/QTj1VHz+noa92wZdzseMOPj7QGO7Fs+ArhtxjFI0jDWT5K6yvpJUldZP0nr0KwTSdcARyV5YpIHAycBl804BkkaxvpJUldZP0nqKusnaR2a6dC2qlpIcgbwUXqXhzyvqm6c0O670p27K3FAd2LpShzQnVi6Egd0K5bWTLl+mhfr8b3gY1bnzXH91PX3WpfjM7bxdDm2NWmdfL9bKeOeLeNuwUwn25YkSZIkSdL8mvXQNkmSJEmSJM0pE0mSJEmSJEkayVwnkpK8JMmNSb6fZEtf+aYk/5Dk+ubvD9qKpbnvzCS7knwxyXOnHUvfcV+X5Na+5+GEWR27L4bjm8e9K8mOWR+/L47dSW5onodPz/jY5yW5I8nn+soOSXJFki81/x/TUhytv0fUvi7WX7PQlfppmrpS/2h9OdBnS9t1SpL/keQLST6b5INJDm7KZ952XCK+ztRLSY5M8okkNzWfEa9syjvTdhjWvrOOm39dOg9GtdT5Mi+SPDDJZ5J8qO1YViLJwUkuaer1m5L8dNsxLSfJf2jeI59LcmGSh7Yd0zjmOpEEfA7418Anh9z3t1X1tObv37YVS5In07t6wU8AxwO/n+SBM4hn0Vv7nocPz/C4NI/z94DnAU8GXtY8H2352eZ52LL8qhP1bnqvfb8dwJVVdRRwZbPcRhzQ4ntEndHV+mtqOlg/Tcu76Ub9o/Xnfp8tHalTrgCeUlU/CfwNcGbffbNuO+6ng/XSArC9qn4cOAY4vS+eLrUdBtt31nFzrIPnwagOdL7Mg1cCN7UdxBjOBj5SVf8MeCodfwxJDgd+HdhSVU+hN0H9Se1GNZ65TiRV1U1V9cW244ADxnIicFFVfaeqvgzsAo6ebXStORrYVVU3V9V3gYvoPR/rSlV9ErhzoPhE4Pzm9vnAi1qKQ1qv9de6qJ+6Uv9IjdbrlKr6WFUtNItXAUfM8vjL6FS9VFV7q+q65vY36X1BO7yteFbAOm6+deo8GNUcny8kOQJ4PvCutmNZiSSPAn4GOBegqr5bVXe3G9VINgAPS7IBeDhwW8vxjGWuE0nLeGLTPe//S/IvW4zjcOCWvuU9zLZSOaPpvn1eC117237s/Qr4WJJrk5zWUgz9NlbVXuh98ACPbzGWNt8j6rYuncOTtpYf23K6VP9o7Rr22dK18+6XgT/vW2677di15+deSTYBTweuboq60nYY1r6zjptvnT0PRjXkfOm6twG/CXy/7UBW6IeBrwJ/3NTd70pyUNtBHUhV3Qr8DvAVYC9wT1V9rN2oxtP5RFKSv2jGDw7+HSgzvRf4wap6OvAbwHubjGUbsWRIWa02lhFjeifwI8DT6D0nb57UcUcNb0jZxB77Ch1bVc+g10329CQ/01IcXdP2e0Qz0sX6q2Vr+bFJUzdm+2Mm590o9V2S/0RvKMoFTdFU2o4rDX1IWev1UpJHAO8HXlVV36BbbQfbd2tPJ8+DUQ05XzotyQuAO6rq2rZjGcMG4BnAO5u6+1t0fChrk3g/EXgi8ATgoCS/2G5U49nQdgDLqapnj7HNd4DvNLevTfK3wI8Cq5pkeZxY6GXRj+xbPoIJdl8bNaYkfwTMevK0qT72laiq25r/dyT5IL1us8Pm1pqV25McVlV7kxwG3NFGEFV1++Ltlt4jmpEu1l8tW8uPbTmdqH8038Zsf8zkvFsutiSnAC8AjquqaraZSttxhTpXLyV5EL0vxRdU1QegW22HJdp31nHzrXPnwaiGnS9z4FjghelNmv9Q4FFJ/rSq5iG5sQfYU1WLPb8uoeOJJODZwJer6qsAST4A/HPgT1uNagyd75E0jiSPW5y8MckPA0cBN7cUzmXASUkekuSJTSx/NYsDNx+ei36e3oS6s3QNcFSSJyZ5ML2JxC6bcQwkOSjJIxdvA89h9s/FoMuAU5rbpwCXthFEB94j6rbW6q8Z6ET91JJO1D9auw7w2dJ6nZLkeODVwAur6tt95V1oO3aqXkoSenOP3FRVb+kr70Tb4QDtO+u4+dap82BUS50vXVdVZ1bVEVW1id5z/fE5SSJRVX8P3JLkx5qi44DPtxjSKL4CHJPk4c175jg6PkH4UjrfI+lAkvw88HbgccDlSa6vqufSm3Trt5MsAN8D/m1VTXWS4aViqaobk1xM7029AJxeVd+bZix9/nuSp9HrDrob+LUZHReAqlpIcgbwUXoz0p9XVTfOMobGRuCDvXOVDcB7q+ojszp4kguBrcChSfYArwXOAi5Ociq9CuUlLcWxtc33iLqho/XXVHWofpqqrtQ/WneGtj86Uqe8A3gIcEXTLriquULbzNuOgzpYLx0LvBy4Icn1Tdlr6F1Fqwtth6HtuyTXYB03tzp4Hoxq6PnSgasarnX/HrigSTreDLyi5XgOqKquTnIJcB29z8HPAOe0G9V40vTolSRJkiRJkg5oTQ5tkyRJkiRJ0uSZSJIkSZIkSdJITCRJkiRJkiRpJCaSJEmSJEmSNBITSZIkSZIkSRqJiSRJkiRJkiSNxESSJEmSJEmSRmIiSZIkSZIkSSMxkaROS7IvyQ83tx+W5P9Nck+SP2vK3pDka0n+vt1IJQEk2Z3k2QNl25J8KslDkpyb5O+SfDPJZ5I8r2+9rUn2DNnnziS/ssxxx962K5Z6DJLm0zL14b6+v+8n+Ye+5ZObdY9KclGSryb5RpIvJXl7kiOa+7c22y5utyfJxUn+jzYer6T5t5p6K8nrkvxTs3x3kv+d5KfbeiyaLhNJ61zLX/qWbfxU1SOq6uZm8cXARuCxVfWSJEcC24EnV9UPjPkUSJqdDcAtwL8CHg38F+DiJJtajEmSZq5p3zyiqh4BfAX4P/vKLkjyJOBq4Dbg6VX1KOBY4G+Bf9G3q9uafTwSOAb4AvA/kxw30wckac1brt5qVntfc//jgE8BH0iStmLW9JhI0oFM+0vfShs/PwT8TVUt9C1/varumFA8kqaoqr5VVa+rqt1V9f2q+hDwZeCnZhVDkl9NsivJnUkuS/KEvvsqyb9rfvX/ZpLXJ/mRJH/Z9Aa4OMmD+9Z/QZLr+351+8kRjr87yZlJPp/kriR/nOShSQ4C/hx4Ql+C/QlJjk7y6eb4tyd5y3SeGUkd8zrgf1XVb1TVHoCquqOq3lZVFw2uXD17quq3gHcBb5ptuJJ0n6r6J+B84AeAx7YcjqbARJKWNKsvfQdq/DRf7J6U5L8CvwX8QvMF69eAK7jvS9e7JxmTpOlLshH4UeDGGR3vWcD/DbwUOAz4O2DwC9nx9Oq4Y4DfBM4BTgaOBJ4CvKzZ1zOA84Bfo9dA+kPgsiQPGSGUk4HnAj9C7/H/56r6FvA8mgR783cbcDZwdtMb4UeAi8d79JLmzLOB94+57QeAZzQJakmauaY9tA3YU1VfazkcTYGJJI1sRl/6hjZ+quq1wH+j6S5ZVX/I/l+6tk0xJkkr8/80vXTuTnI38PuDKyR5EHABcH5VfaHvrif0b9ts/y8Gt1/CctueDJxXVddV1XeAM4GfHuhl+aaq+kZV3Qh8DvhYVd1cVffQ6zH09Ga9XwX+sKqurqrvVdX5wHfoJaCW846quqWq7gTeSJOcWsI/AU9KcmhV7auqq0bYv6TuWLY+XMKhwL3zPyY5o9nHviR/tMy2twEBDh4vZEnr3Lj1FsBLm21uoffD3IumEqFaZyJJ0N6XvmFs/Ejz70VVdfDiH/Dv+u9M8gDgT4DvAmcMbHtb/7bN9p8a8bjLbfsEer2QAKiqfcDXgcP71rm97/Y/DFl+RHP7h4DtA3Xfkc0xlnNL3+2/W2abU+kl8L+Q5JokLxhh/5K644D14QF8nV7PSQCq6h3N9m8DHrTMtocDBdw9TsCS1r1x6y2Ai5vtHl9Vz6qqa6cVpNplIknQ3pe+YWz8SGtYM+HiufQmzv+/mjH0s3IbvQTQYiwH0RuWdusY+7oFeONA/ffwqrpwhG2P7Lv9g01c0Kv79lNVX6qqlwGPpzfs9xKHq0jrwpXAvx5z258HrmuGzEqSNHEmknRALXzps/EjrW3vBH6c3pU+/mHGx34v8IokT2vG7v834Oqq2j3Gvv4I+LdJnpmeg5I8P8kjR9j29CRHJDkEeA3wvqb8duCxSR69uGKSX0zyuKr6Pvcl2L83RryS5svrgH+Z5C1JDgdIcii9+vN+mnro8CSvBX6FXt0iSdJUmEjScqb+pc/Gj7Q+JPkhepNTPw34+76rk508i+NX1ZX0rj75fmAvvcmrTxpzX5+mN0/SO4C7gF30JpUcxXuBjwE3N39vaPb5BeBC4OZmuNwT6E3+fWOSffQm3j6pqv5xnJglzY+q+ht6c64dAfx1km8C/4teD8b/0rfqE5r6YR9wDbAZ2FpVH5txyJKkdSRV9+tJr3UkyW7gV6rqL/rKttFL6JwM7KY3gexC32a/VlUXJNkK/GlVHTGwz51N+bsOcNytwMeBb9ObE+ke4H8Dv9M/mWySAo6qql1JXgc8qap+sW8f9zu+JHXVsDpXkiRJmicmkiRJmhETSZIkSZp3Dm2TJM2FJK/pGw7X//fnbce2KMkPLhHjviQ/2HZ8kiRJ0mrZI0lTk+Q1DJ/v6H9W1fNmHY8kSZIkSVodE0mSJEmSJEkayYa2AziQQw89tDZt2tR2GFP1rW99i4MOOqjtMKZqPTxGWPuP89prr/1aVT2u7Tj6JTkPeAFwR1U9pSk7hN7l1DfRmyz+pVV1V5LQu+rVCfQmed9WVdc125wC/Odmt2+oqvOXO/ZK6qd5fW8Y92wZ9/i6WD+1qSvtpy68NwZ1LSbjWV7XYlppPNZP9zdqHdW1134pxjk58xAjrJ04V1U/VVVn/37qp36q1rpPfOITbYcwdevhMVat/ccJfLo6UC/0/wE/AzwD+Fxf2X8HdjS3dwBvam6fAPw5vasEHgNc3ZQfQu8S7IcAj2luP2a5Y6+kfprX94Zxz5Zxj6+L9VObf11pP3XhvTGoazEZz/K6FtNK47F+Gr+O6tprvxTjnJx5iLFq7cS5mvrJybYlza2q+iRw50DxicBij6LzgRf1lb+nqTevAg5OchjwXOCKqrqzqu4CrgCOn370kiRJkjR/Oj20TZLGsLGq9gJU1d4kj2/KDwdu6VtvT1O2VPn9JDkNOA1g48aN7Ny5c6SA9u3bN/K6XWLcs2XckiRJmgcmkiStFxlSVgcov39h1TnAOQBbtmyprVu3jnTgnTt3Muq6XWLcs2XckiRJmgcObZO01tzeDFmj+X9HU74HOLJvvSOA2w5QLkmSJEkaYCJJ0lpzGXBKc/sU4NK+8l9KzzHAPc0QuI8Cz0nymCSPAZ7TlEmSJEmSBji0TVN3w633sG3H5fcu7z7r+S1Go7UkyYXAVuDQJHuA1wJnARcnORX4CvCSZvUP07ty2y7g28ArAKrqziSvB65p1vvtqhqcwLtTNvWdT4s8rySpWwbrautpqfs8b6XRmEiSNLeq6mVL3HXckHULOH2J/ZwHnDfB0CRJkiRpTXJomyRJkiRJkkZiIkmSJEmSJEkjMZEkSZIkSZKkkZhIkiRJkiRJ0khMJEmSJEmSJGkkJpIkSZJmLMkDk3wmyYea5ScmuTrJl5K8L8mDm/KHNMu7mvs3tRm3JEmSiSRJkqTZeyVwU9/ym4C3VtVRwF3AqU35qcBdVfUk4K3NepKkDti043JuuPUeNu24nE07Lm87HGlmTCRJkiTNUJIjgOcD72qWAzwLuKRZ5XzgRc3tE5tlmvuPa9aXpIlKcmSSTyS5KcmNSV7ZlB+S5Iqmx+QVSR7TlCfJ7zY9Jj+b5BntPgJJs7Kh7QAkSZLWmbcBvwk8sll+LHB3VS00y3uAw5vbhwO3AFTVQpJ7mvW/1r/DJKcBpwFs3LiRnTt3TjP+kezbt68TcfSbZUzbNy/stzzsuF17jroWD3Qvpq7FM2ELwPaqui7JI4Frk1wBbAOurKqzkuwAdgCvBp4HHNX8PRN4Z/Nf0hpnIkmSJGlGkrwAuKOqrk2ydbF4yKo1wn33FVSdA5wDsGXLltq6devgKjO3c+dOuhBHv1nGtG1gmMvuk+9/3K49R12LB7oXU9fimaSq2gvsbW5/M8lN9JLZJwJbm9XOB3bSSySdCLynqgq4KsnBSQ5r9tO6YUPNdp/1/BYikdYeE0mSJEmzcyzwwiQnAA8FHkWvh9LBSTY0vZKOAG5r1t8DHAnsSbIBeDRw5+zDlrSeNBP7Px24Gti4mByqqr1JHt+sdm+PycZib8pOJJIkTc+yiaQk5wGLv549pSk7BHgfsAnYDby0qu5qxuyfDZwAfBvYVlXXNducAvznZrdvqKrzkSRJWkeq6kzgTICmR9J/rKqTk/wZ8GLgIuAU4NJmk8ua5b9s7v948+u/JE1FkkcA7wdeVVXfOMC0bCP1mGz2ueLht6sdRjg4vBSGDzE90DajrL/xYfdt1+Vhj/MwLHMeYgTjhNF6JL0beAfwnr6yHaxgnGyTeHotsIVe5XJtksuq6q5JPRBJkqQ59mrgoiRvAD4DnNuUnwv8SZJd9HoindRSfJLWgSQPopdEuqCqPtAU3744ZC3JYcAdTflij8lF/b0p9zPO8NvVDiMcHF4Kw4eYHmibUdbfvnmBN9+wYaT12zQPwzLnIUYwThjhqm1V9Unu34W6/woig1cWeU/1XEWvm/ZhwHOBK6rqziZ5dAVw/CQegCRJ0jyqqp1V9YLm9s1VdXRVPamqXlJV32nK/7FZflJz/83tRi1prWpGl5wL3FRVb+m7a7FnJNy/x+QvNVdvOwa4pyvzI0marnHnSFrpONmlyu+ni1cdmaZ56Ra3Gv3dPaHbXT5XYz28lpIkSVqzjgVeDtyQ5Pqm7DXAWcDFSU4FvgK8pLnvw/SmNNlFb1qTIK22DwAAIABJREFUV8w2XEltmfRk20uNkx15/GwXrzoyTfPSLW413n7Bpfd294Rud/lcjfXwWkqSJGltqqpPMfx7G8BxQ9Yv4PSpBiWpk5Yd2raE25sha4w4Tnbk8bOSJEmSJEnqpnETSSsdJ/tR4DlJHpPkMcBzmjJJmook/yHJjUk+l+TCJA9N8sQkVyf5UpL3JXlws+5DmuVdzf2b2o1ekiRJkrpp2aFtSS4EtgKHJtlD7+prKxonW1V3Jnk9cE2z3m9X1eAE3pI0EUkOB34deHJV/UOSi+ld6egE4K1VdVGSPwBOpXd1yVOBu6rqSUlOAt4E/EJL4UuSJGkObRp2pbiznt9CJNJ0LZtIqqqXLXHXisbJVtV5wHkrik6SxrcBeFiSfwIeDuwFngX8m+b+84HX0UskndjcBrgEeEeSNHWaJEmSJKkx6cm2Jal1VXVrkt+h12PyH4CPAdcCd1fV4iUE+68eee+VJatqIck9wGOBr/Xvd9yrSk76in79V0FcNI0rBs7rlQiNe7bmNW5J0voz2GPI3kLSeEwkSVpzmrnYTgSeCNwN/BnwvCGrLvY4GunKkuNeVXLSV/TbNqzb9BSuhjivVyI07tma17glSZI0HhNJa4xZdgmAZwNfrqqvAiT5APDPgYOTbGh6JfVfPXLxypJ7kmwAHg04j5skSZJmyu9zmgfjXrVNkrrsK8AxSR6eJPTmdPs88Angxc06g1ecXLwS5YuBjzs/kiRJkiTdnz2SJK05VXV1kkuA64AF4DP0hqRdDlyU5A1N2bnNJucCf5JkF72eSCfNPmpJkiTN0rCrrElanokkSWtSVb0WeO1A8c3A0UPW/UfgJbOIS5IkSZLmmYkkSZIkSZKWYQ8mqcc5kiRJkiRJkjQSE0mSJEmSJEkaiUPbJEmSJElqgcPlNI9MJEmSJElTNvhlcfdZz28pEknzxLpDXWQiSZIkSZI01+zZI82OcyRJkiRJkiRpJPZIkiRJkmZs047L2b55gW1NL4rB4SrDelc4pEWaLXs5ScPZI0mSJGlGkjw0yV8l+eskNyb5r035E5NcneRLSd6X5MFN+UOa5V3N/ZvajF+SJMlEkiRJ0ux8B3hWVT0VeBpwfJJjgDcBb62qo4C7gFOb9U8F7qqqJwFvbdaTpKlIcl6SO5J8rq/sdUluTXJ983dC331nNonuLyZ5bjtRS5o1E0mSJEkzUj37msUHNX8FPAu4pCk/H3hRc/vEZpnm/uOSZEbhSlp/3g0cP6T8rVX1tObvwwBJngycBPxEs83vJ3ngzCKV1BrnSJIkSZqh5ovWtcCTgN8D/ha4u6oWmlX2AIc3tw8HbgGoqoUk9wCPBb42sM/TgNMANm7cyM6dO6f8KJa3b9++TsTRb5Yxbd+8sOw6Gx9233qDcQ3bftqxr/fXbBRdi2fSquqTKxhCeyJwUVV9B/hykl3A0cBfTim8uTTpeZYG9+fcaWrD2ImkJD8GvK+v6IeB3wIOBn4V+GpT/pq+rPWZ9Lpofw/49ar66LjHlyRJmkdV9T3gaUkOBj4I/Piw1Zr/w3of1f0Kqs4BzgHYsmVLbd26dTLBrsLOnTvpQhz9ZhnTthG+PG7fvMCbb+g1x3efvHXZ7QfXmbT1/pqNomvxzNAZSX4J+DSwvaruopfovqpvnf4kuKQ1bOxEUlV9kd7Y/sVf1m6l1xh6Bb2uj7/Tv/5A18cnAH+R5EebxpQkSdK6UlV3J9kJHAMcnGRD0yvpCOC2ZrU9wJHAniQbgEcDd7YRr6R1653A6+klsV8PvBn4ZUZMdMN4vSZX2vtrlF6A09Dfs7ANoz5H89Cbbh5iBOOEyQ1tOw7426r6uwMM27froyRJWteSPA74pyaJ9DDg2fQm0P4E8GLgIuAU4NJmk8ua5b9s7v94VQ39oiZJ01BVty/eTvJHwIeaxcVE96L+JPjgPlbca3Klvb9G6QU4Df09C9swak/FeehNNw8xgnHC5BJJJwEX9i2P3fWxi2P8p2nSWcLBbHgXnr/BLH0XYpqGeclMS5JadRhwftOb+wHAxVX1oSSfBy5K8gbgM8C5zfrnAn/S/AB3J702lyTNTJLDqmpvs/jzwOIV3S4D3pvkLfRGnBwF/FULIUqasVUnkpI8GHghcGZTtKquj10c4z9Nk84SDmbipz2WfhRvv+DS/bL0XYhpGuYlMy21xckhJaiqzwJPH1J+M72e2oPl/wi8ZAahSRJJLgS2Aocm2QO8Ftia5Gn0vrvtBn4NoKpuTHIx8HlgATjdaUuk9WESPZKeB1y32OVxEl0fpUnzC+z600xi+y7gKfQaPr8MfJHeRQI20WsIvbSq7moupX02cALwbWBbVV3XQtiSJEmtqaqXDSk+d0jZ4vpvBN44vYgkddEDJrCPl9E3rC3JYX33DXZ9PCnJQ5I8Ebs+Spqus4GPVNU/A54K3ATsAK6sqqOAK5tl6CXEj2r+TqPXs1JzaNOOy7nh1nvYtOPyiV9uV5IkSdIqeyQleTjwczTdGxv/3a6PktqU5FHAzwDbAKrqu8B3k5xIr7s2wPnATuDV9C4G8J5mAturkhw8MB+AJEmSJIlVJpKq6tvAYwfKXn6A9e36KGkWfhj4KvDHSZ4KXAu8Eti4mByqqr1JHt+sfzhwS9/2ixcD2C+RNO7FAKY9qT5MZxL7ebwYwPbNC/tN8D9PE+DP64T98xq3JEmSxtPedQolaXo2AM8A/n1VXZ3kbO4bxjbMVC8GMO1J9WE6k9jP48UAtu24fL/L8M7T5P7zOmH/vMYtSZKk8UxijiRJ6po9wJ6qurpZvoReYun2xXncmv939K3vxQAkSZIkaRn2SJK05lTV3ye5JcmPVdUXgePozc/2eeAU4Kzm/6XNJpcBZyS5CHgmcI/zI0mSZskLBEiS5oWJJElr1b8HLkjyYOBm4BX0emFenORU4CvAS5p1PwycAOwCvt2sK0mSJEkaYCJJ0ppUVdcDW4bcddyQdQs4fepBSZIkSVM22MNx91nPbykSrVUmkiRJkqRVcmiapK7a1FyMZNgFW8BEk1bOybYlSZIkSZI0EnskSZIkSZI0h+wNqTaYSJIkSZIkzRUTKFJ7HNomSZIkSZKkkZhIkiRJkiRJ0kgc2iZJU3bDrffsd5UMr4whSZIkaV7ZI0mSJEmSJEkjMZEkSZIkSZKkkZhIkiRJkiRJ0khMJEmSJM1IkiOTfCLJTUluTPLKpvyQJFck+VLz/zFNeZL8bpJdST6b5BntPgJJkrTeOdm2JEnS7CwA26vquiSPBK5NcgWwDbiyqs5KsgPYAbwaeB5wVPP3TOCdzX+tQ5v6LtwAXrxBk5fkPOAFwB1V9ZSm7BDgfcAmYDfw0qq6K0mAs4ETgG8D26rqujbiljRb9kiSJEmakarau/hFq6q+CdwEHA6cCJzfrHY+8KLm9onAe6rnKuDgJIfNOGxJ68e7geMHynbQS3QfBVzZLMP+ie7T6CW6Ja0Dq0okJdmd5IYk1yf5dFNm12xJkqRlJNkEPB24GthYVXuhl2wCHt+sdjhwS99me5oySZq4qvokcOdAsYluSfuZxNC2n62qr/UtL2as7ZotSZI0RJJHAO8HXlVV3+iNEBm+6pCyGrK/0+j1CGDjxo3s3LlzQpGOb9++fZ2Io980Y9q+eWHF22x82HjbLZr0Y1lvr9k4uhbPjOyX6E6yXKJ774zjkzRj05gj6URga3P7fGAnvUTSvRlr4KokByc5bLFSkiRJWg+SPIheEumCqvpAU3z7Yruo+UX/jqZ8D3Bk3+ZHALcN7rOqzgHOAdiyZUtt3bp1WuGPbOfOnXQhjn7TjGnbwPxFo9i+eYE33zB+c3z3yVvH3naY9faajaNr8bRspEQ3jJfsXi5pt5ok7CStNiE8KweKsyvJ0XlJ1Brn6hNJBXwsSQF/2DRizFhL6oQkDwQ+DdxaVS9I8kTgIuAQ4Drg5VX13SQPAd4D/BTwdeAXqmp3S2FLWsOayWnPBW6qqrf03XUZcApwVvP/0r7yM5JcRK8n9z3+CCdpxlaV6Ibxkt3LJe3GSd5Ow2oTwrNyoDgnnZQe17wkao1z9YmkY6vqtiZZdEWSLxxg3bntmj1Nk84SDmaZu/D8DWa/24hpFs/LvGSm15lX0pvI9lHN8puAt1bVRUn+ADiV3jDbU4G7qupJSU5q1vuFNgKWtOYdC7wcuCHJ9U3Za+glkC5OcirwFeAlzX0fpndFpF30ror0itmGK0kmuiXtb1WJpKq6rfl/R5IPAkezBrtmT9Oks4SDmfkuZJfffsGl+2W/24hpFs/LvGSm14skRwDPB94I/EbTC+BZwL9pVjkfeB29RNKJzW2AS4B3JEkzFFeSJqaqPsXwH9cAjhuyfgGnTzUo3c+mwXbDWc9vKRJptpJcSG+akkOT7AFei4luSQPGTiQlOQh4QFV9s7n9HOC3MWMtqRveBvwm8Mhm+bHA3VW12D2t/8pH9w69raqFJPc06/dfSGDsHpOT7pU3bHz7PPSym0XPwO2bF/Z7vuepl+C89mqc17glSfdXVS9b4i4T3ZLutZoeSRuBDzZXGdkAvLeqPpLkGsxYS2pRkhcAd1TVtUm2LhYPWbVGuO++gjF7TE66V96wOQHmoZfdLHoGbttx+X5zAHShV+ao5rVX47zGLUmSpPGMnUiqqpuBpw4p/zrrNGNtN2ipM44FXpjkBOCh9OZIehtwcJINTa+k/uG1i0Nv9yTZADwauHP2YWs9Wvzs2L554d5km58fkiRpVvweq5Xq/vTykrRCVXUmcCZA0yPpP1bVyUn+DHgxvSu3DQ69PQX4y+b+jzs/kiTpQAa/eEmStF48oO0AJGmGXk1v4u1d9OZAOrcpPxd4bFP+G8COluKTJEmSpE6zR5KkNa2qdgI7m9s307u65OA6/8h987lJkjQXHI4iSWqDiSRJkiTpABzGJknSfUwkSZIkSWuAPZQkSbPgHEmSJEmSJEkaiT2SJEmSJEnSUPZ21CATSdKMDJtfwUpYkiRJUpc4L5yW49A2SZIkSZIkjcREkiRJkiRJkkbi0DZJktYY5zKQJEmzZNtjfbFHkiRJkiRJkkZiIkmSJEmSJEkjMZEkSZIkSZKkkZhIkiRJkiRJ0kicbFuSdD+bdlzO9s0LbGsmTnTCRElr2eAksZIkaWkmkiRJkrSumDiSJGl8Dm2TJEmaoSTnJbkjyef6yg5JckWSLzX/H9OUJ8nvJtmV5LNJntFe5JIkSSaSJEmSZu3dwPEDZTuAK6vqKODKZhngecBRzd9pwDtnFKMk7SfJ7iQ3JLk+yaebsqFJcElr29hD25IcCbwH+AHg+8A5VXV2ktcBvwp8tVn1NVX14WabM4FTge8Bv15VH11F7JIkSXOnqj6ZZNNA8YnA1ub2+cBO4NVN+XuqqoCrkhyc5LCq2jubaCVpPz9bVV/rW15Mgp+VZEez/Op2QtOsODxYq5kjaQHYXlXXJXkkcG2SK5r73lpVv9O/cpInAycBPwE8AfiLJD9aVd9bRQySdD8HSHQfArwP2ATsBl5aVXclCXA2cALwbWBbVV3XRuyS1q2Ni8mhqtqb5PFN+eHALX3r7WnK9kskJTmNXo8lNm7cyM6dO6ce8HL27dvXiTj6Lca0ffNC26EAsPFhTDWWlT7/XX7NuqJr8XTAUknwiTN5IXXH2ImkprGz2OD5ZpKb6DVslnIicFFVfQf4cpJdwNHAX44bgyQtYalE9zaG/2rWP3TkmfSGjjyzlcglaX8ZUlb3K6g6BzgHYMuWLbV169Yph7W8nTt30oU4+i3GtK0jX0i3b17gzTdM79o3u0/euqL1u/yadUXX4pmxAj6WpIA/bOqdpZLg+xkn2T2YtOtKAnjQtBPCkzLrOMdJuM5LotY4J3TVtqZ79tOBq4FjgTOS/BLwaXpf5u6il2S6qm+zxV/UBvfVuV/URjV4Yo5TQbYRw7QNVlptxDSL52W513JYxd2F12ctOkCi26Ejkrrq9sV6J8lhwB1N+R7gyL71jgBum3l0kgTHVtVtTbLoiiRfGHXDcZLdg0m7riSAB007ITwps45zMJE92KNs91nPv98285KoNc4JJJKSPAJ4P/CqqvpGkncCr6eXsX498Gbgl5njX9RGNVi5jfIr0KRf3HFimLa3X3DpfpVWGzHN4nlZ7rUc9uHXhddnrRtIdLcydGTSydRZJCW3b17YL+5J7H8WCd1pxD1ti7FOMu5Z/qgwL7/KzYHLgFOAs5r/l/aVn5HkIno9Je8xyb1yi19gtm9e6OyXUanrquq25v8dST5Ib3TJUklw6YBGSSypu1aVSEryIHpJpAuq6gMAVXV73/1/BHyoWfQXNUkzNSTRveSqQ8omluiedDJ1FknJbTsu3++Xq0nsfxYJ3WnEPW3b+r7gTiruWf6oMC+/ynVJkgvp9Y48NMke4LX0EkgXJzkV+Arwkmb1D9Obv20XvTncXjHzgCWte0kOAh7Q9PQ+CHgO8NssnQSXVmRT04ZbbMOYWOq21Vy1LcC5wE1V9Za+8v7hID8PfK65fRnw3iRvoTfZ9lHAX417fEk6kGGJbhw6ojH4i5kmrapetsRdxw1Zt4DTpxuR1gvrM63CRuCDzY9yG4D3VtVHklzD8CS41jknR1/bVtMj6Vjg5cANSa5vyl4DvCzJ0+j9mr8b+DWAqroxycXA5+lNhHv6JK/Y5gejpEVLJbpx6IgkSfcabD+/+/iDWopEXVdVNwNPHVL+dYYkwSWtbau5atunGD4c5MMH2OaNwBvHPaYkjWipRLdDR6QO80chqV033HrPfkNjPQclScN0f3p5SVqhAyS6waEjkiRJkjS2B7QdgCRJkiRJkuaDPZKkdcahI5IkSZKkcZlIkiRJktYgr5okSZoGh7ZJkiRJkiRpJPZIkiRJUmc5JFtanwavIiipO0wkSZIkSZKkzho2VNcfFtpjIkmSJEnS/fjFTdI8sQfr7DhHkiRJkiRJkkZijyRJkiStKV6tTJLmm/V4t9kjSZIkSZIkSSOxR5IkSZLmhvP2dMtq5yRxThNJmj8mkiRJkjTXHAIxOz7XktYKE9njc2ibJEmSJEmSRmKPJEmSJHWGPV4kSeo2E0mSJEmSpmIaQ0ccjiIJlv/hYdJzuI2zj7XKRJIkSZKkibBHmaSumkX9tF4S3SaSJEmSJM2EiSZJ82zTjsvZvnmBbU1dtlYTRcuZeSIpyfHA2cADgXdV1VmzjkGShrF+ktRVa6l+Wi+/1mo8g++P7ZsX2LrCbXxPzdZaqp8kjWamiaQkDwR+D/g5YA9wTZLLqurzs4xDkgZZP0nqqi7VT6P0JpnEHBRSv5W+R5Zb30TT5HSpfpJWy6Fvo5t1j6SjgV1VdTNAkouAEwErGklts36S1FVTq5+Wa9CO06he3Ka/67/UJeN8kRt8X8/rl78psP2kdW2lE36Ps7/l6ps2klOpqqkf5N6DJS8Gjq+qX2mWXw48s6rO6FvnNOC0ZvHHgC/OLMB2HAp8re0gpmw9PEZY+4/zh6rqcW0HMS1Trp/m9b1h3LNl3OOzfupm+6kL741BXYvJeJbXtZhWGs+6r5+a8nHqqK699ksxzsmZhxhh7cQ5dv006x5JGVK2Xyarqs4BzplNOO1L8umq2tJ2HNO0Hh4jrJ/HuYZNrX6a1/eGcc+WcesA5rL91MX3RtdiMp7ldS2mrsXTAcvWTzBeHTUvz7VxTs48xAjGCfCAaez0APYAR/YtHwHcNuMYJGkY6ydJXWX9JKmrrJ+kdWjWiaRrgKOSPDHJg4GTgMtmHIMkDWP9JKmrrJ8kdZX1k7QOzXRoW1UtJDkD+Ci9y0OeV1U3zjKGDupUN/QpWQ+PEdbP41yTplw/zet7w7hny7g11By3n7r43uhaTMazvK7F1LV4WmX7CTDOSZqHGME4ZzvZtiRJkiRJkubXrIe2SZIkSZIkaU6ZSJIkSZIkSdJITCS1LMnrktya5Prm74S2Y5qkJMcn+WKSXUl2tB3PNCTZneSG5vX7dNvxqDvm8f2f5Mgkn0hyU5Ibk7yy7ZhWIskDk3wmyYfajmVUSQ5OckmSLzTP+0+3HdMokvyH5j3yuSQXJnlo2zGpW5L8xySV5NBmOUl+t6kTP5vkGTOK438059dnk3wwycF9953ZxPPFJM+dRTx9x271M2Kp+j7JIUmuSPKl5v9jZhzXfvV4M4nz1U0872smdJ5lPPero9t+jta6ts+NpXT1nFlK186lJWKci/NrWJunC89nkvOS3JHkc31lQ5+/SX8Gm0jqhrdW1dOavw+3HcykJHkg8HvA84AnAy9L8uR2o5qan21evy1tB6JumOP3/wKwvap+HDgGOH1O4l70SuCmtoNYobOBj1TVPwOeyhzEn+Rw4NeBLVX1FHoTrJ7UblTqkiRHAj8HfKWv+HnAUc3facA7ZxTOFcBTquongb8BzmxifDK99+1PAMcDv9/U3VPXkc+Iper7HcCVVXUUcGWzPEuD9fib6LWVjwLuAk6dcTzD6ui2n6M1qyPnxlK6es4spWvn0jCdP78O0ObpwvP5bnqfX/2Wev4m+hlsIknTdDSwq6purqrvAhcBJ7YckzQrc/n+r6q9VXVdc/ub9D7QD283qtEkOQJ4PvCutmMZVZJHAT8DnAtQVd+tqrvbjWpkG4CHJdkAPBy4reV41C1vBX4T6L+qy4nAe6rnKuDgJIdNO5Cq+lhVLTSLVwFH9MVzUVV9p6q+DOyiV3fPQuufEQeo708Ezm9WOx940axiGqzHkwR4FnBJS/EsVUe39hytA62fG0vp4jmzlK6dS8PM2fk12ObZSweez6r6JHDnQPFSz99EP4NNJHXDGU33svO60HVvgg4Hbulb3sOcfCFdoQI+luTaJKe1HYw6Y+7f/0k2AU8Hrm43kpG9jd4X1++3HcgK/DDwVeCPm+7n70pyUNtBLaeqbgV+h15vk73APVX1sXajUlckeSFwa1X99cBdXagXfxn48w7E04Xn4l4D9f3GqtoLvS/OwONnGMpgPf5Y4O6+ROCsn6el6ug2n6O1rlPnxlI6dM4spWvn0jBzcX4Na/MA19K953PRUs/fRM8tE0kzkOQvmvGUg38n0utS9iPA0+i9Md/carCTlSFlNaRs3h1bVc+g113w9CQ/03ZA6oS5fv8neQTwfuBVVfWNtuNZTpIXAHdU1bVtx7JCG4BnAO+sqqcD36I7XeKX1PzocSLwROAJwEFJfrHdqDRLy7Rt/hPwW8M2G1I2kXpxmXgW1/lP9IamXDDteEbQmc+IrtT3S9TjbT9Pc1lHz7m2X/NldeWcWUpHz6Vh5uL8Gtbmofe9b1Dbz+dyJvoe2LCKQDSiqnr2KOsl+SNgbiaIHcEe4Mi+5SNYg0Mfquq25v8dST5Ir0vuJ9uNSh0wt+//JA+i10C6oKo+0HY8IzoWeGF6Fyx4KPCoJH9aVV1PbuwB9lTVYq+vS+hgI2qIZwNfrqqvAiT5APDPgT9tNSrNzFJtmySb6TW2/7o3koIjgOuSHM0U68Xl2lpJTgFeABxXVYsN5zbr6U58RixR39+e5LCq2tsMe7hjRuHcrx6n16vi4CQbml/+Z/08LVVHt/UcrQedODeW0rFzZildPJeGmZfza6k2T9eez0VLPX8TPbfskdSygXGJPw98bql159A1wFHpzWj/YHqTkl3WckwTleSgJI9cvA08h7X1Gmp8c/n+b8bQnwvcVFVvaTueUVXVmVV1RFVtovdcf3wOkkhU1d8DtyT5saboOODzLYY0qq8AxyR5ePOeOY45mCRc01dVN1TV46tqU3M+7uH/Z+/ew20t63r/vz+AIoKKiM4QqGVbcntYibZS9g+z6TFEDf0lBqGCUeRvq+nea1+B2k7T6MISD50sDAIKRbaHJDWTqAnRDhSQQkQTdSkLlqAIyAKTFn5/fzzPlLEGY8455pxjjtN8v65rXnM85+89Dvd4xve57/uBJ7fv9fOBV6RxCE2XyG1rHVOSw4ATgZ+vqrs6Fp0PHJVk9ySPohmA9DNrHU9r5N8Ri9T35wPHto+PBT42jHgWqMePAf4ReMmw42ljWqiOHslztE6M/LOxkHH7zCxkHD9LvUzQ56vXOc8XGLPns8NCz99Av4NtkTR6v5fkYJpmZVuAXxttOINTVTuSvAb4O5rR7c+oqmtGHNagzQAfba+67ga8v6o+NdqQNA4m+P1/KPBy4OokV7Xz3lhTdEfJMfRa4Jz2hPmrwCtHHM+SquqyJB8CrqTpKvQ54LTRRqUJ8EngcJpBre9ieO/1PwJ2By5ov68vrapXVdU1Sc6j+UGwA3h1Vd0zjIDG5DuiZ30PnAKcl+R4mh9QRw45rm4nAucm+R2auub0IR+/Vx29C+P1HE2NMflsLGRSPjMLGfVnqZex/3wtcs7zCUb8fCb5ADAL7JtkK/BmFn4/DvQ7OPe27pUkSZIkSZIWZtc2SZIkSZIk9cVEkiRJkiRJkvpiIkmSJEmSJEl9MZEkSZIkSZKkvphIkiRJkiRJUl9MJEmSJEmSJKkvJpIkSZIkSZLUFxNJkiRJkiRpaiWZS/Iro45jWphIkiRpEUmuSTLbPk6Sv0hya5LPtPP+vyQ3Jdme5GEjDVbS2EuyJcndSfbtmn9Vkkry35J8N8muHcvet8C8Px1m7JKWr4/P/IbRRCatnIkkLaqt+J7dNe+4JJe0P5rm/36Q5Hsd08csss+3JPmrRY43v5+b2h9sew26XJIm1wL10mxbD21PckeSLyV55RL72dCewG3vqHM+nuQ5netV1eOraq6dfBrwHOCAqnpKkvsB7wSeW1V7VdUtgyuppCn2NeDo+YkkG4E92slvA7sCT+5Y/2eAG7vmPR24eG3DlDQgi33mpYljIkkr1v5o2quq9gK+AbywY945q9j1C9t9Phn4aeA3BxGvpKl3Y1t3PBg4EXhfksf1sd3e7XZPBC4APprkuAXW/TG5v6/WAAAgAElEQVRgS1Xd2U7PAA8ArllV5JLWm78EXtExfSxwdvv4P4FLaRJFJHkEcH/gg13zfgITSdKkWOwzT5Ldk7wjyTfaC1t/mmSPdtlD2wtd32pbRH88yQEd284leVuSf24vpn26u/VTL0n+T5JvJrk9ycVJHt/Of1SS25Ls0k7/eZKbO7b7qySvbx+/Msm17XG/muTXOtb7fJIXdkzfL8m3kxy8RFyvSPL1JLck+d+dFxDb5+ndSW5s/96dZPd+nqeuYzw6yUVt2b+d5INLPV/amYkkja2qugH4W+AJo45F0uSoxl8DtwL9JJLmt/tmVb0HeAvw9o4TqC1Jnp3keODPgf/WtmD6APCldvPbkvzDQAsiaZpdCjw4yWPb7mq/CHS21r6YNmnU/r+k/euc97Wq2jqkeCWtzlKf+bfTJIcPBh4N7A/8VrtsF+AvaC5m/SjwPeCPuvb/S8ArgfnE8//qI6a/BQ5qt7kSOAegqr4GfBd4UrvezwDbkzy2nX46cFH7+GbgBTQX8V4JvCvJfMvJs4GXdRzvcGBbVV21UEDtBcA/AY4B9gMeQvNczHsTcAjN8/RE4Cnc2+ign+dp3tuATwMPBQ4A/nChmNSbiSSNrSQH0lQ4nxt1LJImR5JdkrwY2Bu4egW7+AjNSdVjOmdW1enAq4B/aVteHg08vl28d1U9cxVhS1p/5lsoPAf4InBDx7KLgKclCc2PuH8C/gU4pGPeRUiaJAt95gP8KvA/quo7VXUH8LvAUQBVdUtVfbiq7mqXnQz8bNe+/6Kq/r2qvgecR5NoWVRVnVFVd1TV92kuoj0xyUPaxRcBP5vkR9rpD7XTj6JJGv1ru49PVNVX2ot4F9EkZ36m3eavgMOTPLidfnn7HCzmJcDfVNUlVXU3TTKtOpYfA7y1qm6uqm8Bv93ut9/nad5/0iScHllV/1FVlywRl7rsNuoANBH+OsmOjun702St1/p4twOfoKlIJWkpj0xyG/ADmu62L6+qLy2xTS83tv/3GVhkknRff0nT8uhRdHRxaV0K7EXTKvvpwHuranuS6zvm/cEQY5W0egt95h8OPBC4oskTA01yaVeAJA8E3gUcRtOCBuBBSXatqnva6W927O8umvpjQW2rqJOBI9vj/6BdtC/Nb7CLgJ8HtrYxz9EkbP4D+Keq+kG7n+cBb6ZpTbVLW46rAarqxiT/DPxCko8CzwNet1hcwCOB6+cnququJLd0Lf96x/TX23n9Pk/zfoOmVdJnktwKnFpVZywRmzqYSFI/XlRVfz8/0Y4dspa3TtzpeJLUpxurqmdf+GWab0L9nQHsS5J6qqqvJ/kaTevr47uW/UeSz9J0Gdmvqr7YLvqndt5P4vhI0kRZ5DP/bZpuWI9vh/botpmmlfRTq+qb7RhDn6NJNq3ULwFHAM8GttB0Ibu1Y58XAb9Pk0i6iKZr7Z/SJJIugma8IuDDNK2sPlZV/5nkr7viOovmd+NuNC26e5Wv0zY6WoS340R13hH3RpqWRPNjU/4o914A7Pt5qqpv0rQCI8nTgL9PcnFVXbdEfGrZtU2SpJ29mKbP/0paM0nSchwPPLNjAP9OFwOvB/5vx7xL2nnfrKqvDCE+SYPV6zP/A+B9NOMLPQIgyf5Jfq5d/iCaRNNtSfahaQG0Wg8Cvg/cQtOKaKceIFX15faYLwMurqrvAjcBv8C93WrvD+wOfAvY0bZOem7Xcf6a5gZKr+O+LS97+RDwwiT/T5L703Rd60wEfQD4zSQPbwcU/y3uHWuq7+cpyZEdA3HfStN9rrvVkhZhIkmjskuSB3T87T7qgCRNlPt11iEMoIVtkpkkr6E58XjDfLNtSVor7dgily+w+CKa8do6x+64pJ1nayRpAi3ymT8RuA64NMl3gb/n3pY57wb2oGm5dCnwqQGEcjZNt7AbgC+0++12EXBLVX2jYzq049e24xD9Os2YTLfStHI6v3MH7ZhNH6bpzveRpYKqqmuA1wLn0rROuoPm4t7321V+B7gc+DeaLnRXtvNgec/TTwOXJdnexvy6dpBx9SlVtfRaWreSbAF+pVfXtqp62mLrLbLPt3DfDPENVXXAcvYjaX1q64kf65r9z8CG5XRtS7IB+BpwJ82J0Z00Jyd/UFWf6lhvC2291F3/dezjflXVOZacJEnSupfkt4CfqKqXLbnyfbfdC7gNOMhEz3gxkSRJkiRJkgaq7WL2OZoboPTVkjLJC4ELaS7ynQo8FXhymbgYK3ZtkyRJkiRpiiQ5Jsn2Hn/XLL31QI7/qzR3YPvbziRSH3EdQTOA9o3AQcBRJpHGjy2StCaS/C3wMz0W/W5V/W6P+ZI0cEmOAf6sx6KvV9Xjhx2PJEmSNOlWnEhKciDNIF0/QjPS/GlV9Z52/JtfpRm9HeCNVfXJdps30IxUfw/w61X1d6sLX5IkaXIkOYPm9u03V9UT2nm/D7wQuBv4CvDKqrqtHYPrWu69g+ClVfWqoQctSZLUYTWJpP2A/arqyiQPAq4AXgS8FNheVe/oWv9xNLfrewrwSJqR6H+iqha8zd6+++5bGzZsWFF84+zOO+9kzz33HHUYa2Jayzat5YL+y3bFFVd8u6oePoSQJkKv+sn3yeSZ1nLB9JatV7kmqX5K8nRgO3B2RyLpucA/VNWOJG8HqKoT20TSx+fX69eknD9N63u0H5Z9/ZR9kuqnYem3jpqE94oxDs4kxDltMa6mflrx7ZKrahvNLfmoqjuSXAvsv8gmRwDnVtX3ga8luY4mqfQvC22wYcMGLr98oTuiTq65uTlmZ2dHHcaamNayTWu5oP+yJfn62kczOXrVT75PJs+0lgumt2y9yjVJ9VNVXdwmiDrnfbpj8lLgJas5xqScP03re7Qfln121GEMzSTVT8PSbx01Ce8VYxycSYhz2mJcTf204kRSVwAbgCcBlwGHAq9J8gqa2yhvrqpbaZJMl3ZstpUeiackJwAnAMzMzDA3NzeIEMfK9u3bp7JcML1lm9ZywXSXTZIm0C8DH+yYflSSzwHfBX6zqv6p10aTeP60nr9/LPvcqMOQJK3CqhNJSfYCPgy8vqq+m+S9wNuAav+fSnNSlB6b36dfXVWdBpwGsGnTphr3jN9KTEImc6WmtWzTWi6Y7rJJ0iRJ8iZgB3BOO2sb8KNVdUuSnwL+Osnjq+q73dtO4vnTev7+seyzow5DkrQKu6xm4yT3o0kinVNVHwGoqpuq6p6q+gHwPprua9C0QDqwY/MDaG7pJ0kDleTAJP+Y5Nok1yR5XTv/LUluSHJV+3d4xzZvSHJdki8l+bnRRS9pPUpyLM0g3MfM3+a4qr5fVbe0j6+gGYj7J0YXpSRJ0ipaJCUJcDpwbVW9s2P+fu34SQAvBj7fPj4feH+Sd9IMtn0Q8JmVHl+SFrGDplvtD28GkOSCdtm7FrgZwFHA42lvBpBk0ZsBSNKgJDkMOBH42aq6q2P+w4HvVNU9SX6c5tzpqyMKU5IkCVhd17ZDgZcDVye5qp33RuDoJAfTdFvbAvwaQFVdk+Q84As0P/Je7Y80SWthGDcDkKSVSPIBYBbYN8lW4M3AG4DdgQua63RcWlWvAp4OvDXJDuAe4FVV9Z2RBC5JktRazV3bLqH3uEefXGSbk4GTV3rMYdpw0ifuM2/LKc8fQSSSVmOQNwPQ5Omuy63HNWpVdXSP2acvsO6HaYYQmCrzn8vNG3dw3Emf8HOpnXgOrknm+1frxUDu2iZJ42jQNwNY6q5I03wnmkkt2+aNO3aa9jWbfNNarnGxVPK114+kbivZZjnr9/pRttxtBr3+crfZvHEHs13z1uK5XeoH7Che7zMP23PZx+i22tfPH/aStDomkrQkv3w1iRa6GUDH8vcBH28n+7oZwFJ3RZrmO9FMatmO666/jpndaXpSy9WPaS3btJZrVFbyI16Ty9dbkjQIq7prmySNo8VuBtCxWvfNAI5KsnuSR+HNACRJkiSpJ1skSZpG3gxAkiRJktaAiSRJU2fabwYgSZIkSaNi1zZJkiRJkiT1xRZJmgoOCC5JkiStTpK9gT8HnkAzFMAvA18CPghsoBka4KVVdWs7JuV7gMOBu4DjqurKEYQtachskSRJkiRJgiYx9Kmq+q/AE4FrgZOAC6vqIODCdhrgeTQ3KDkIOAF47/DDlTQKtkgac7a0WRu9bn/rcytJkqT1KsmDgacDxwFU1d3A3UmOAGbb1c4C5oATgSOAs6uqgEuT7J1kv6raNuTQJQ2ZiSRJkiRJ0o8D3wL+IskTgSuA1wEz88mhqtqW5BHt+vsD13dsv7Wdd59EUpITaFotMTMzw9zc3JLBbN++va/1Rqk7xs0bd9xnnVGXYRKeR5iMOI3xXiaSJEmSJK0b/bT479V6faltpsBuwJOB11bVZUnew73d2HrpdYfc6rViVZ0GnAawadOmmp2dXTKYubk5+llvlLpjPK5Xr4djZu8zb5gm4XmEyYjTGO/lGEmSJEmSpK3A1qq6rJ3+EE1i6aYk+wG0/2/uWP/Aju0PAG4cUqySRmjdtEhyrCFJkiRJ6q2qvpnk+iSPqaovAc8CvtD+HQuc0v7/WLvJ+cBrkpwLPBW43fGRpPVh3SSSptU4JMg6Y9i8cccPR+KbdNNaLkkLcyB+SdI691rgnCT3B74KvJKmF8t5SY4HvgEc2a77SeBw4DrgrnZdSeuAiSRJkiRJElV1FbCpx6Jn9Vi3gFeveVCSxs7UJJLGoWWOJEmSJEnSNHOwbUmSJEmSJPVlxS2SkhwInA38CPAD4LSqek+SfYAPAhuALcBLq+rWJAHeQ9OP9i7guKq6cnXhS5K0tmzxqkFLcgbwAuDmqnpCO8/zJ0mSNBFW0yJpB7C5qh4LHAK8OsnjgJOAC6vqIODCdhrgecBB7d8JwHtXcWxJkqRJdSZwWNc8z58kSdJEWHGLpPbWjtvax3ckuRbYHzgCfniDq7OAOeDEdv7Z7aBslybZO8l+6+kWkfNXtTdv3MFxJ33Cq9pTxlYLkqR+VNXFSTZ0zfb8SZIkTYSBDLbdngw9CbgMmJk/uamqbUke0a62P3B9x2Zb23k7nQglOYHmihszMzPMzc31FcPmjTt2mu7ebqnlS+2vn22WMr/PmT2ax/3sb9DluvqG23ea3rj/QwYaw8weq3+eVmItXt/llmu5MYyL7du3T0yskjTFxuL8qdtS5x2r2Wah86HlxrSSbUZZbuh9XjHoYwxj/ZVs033eMY7lXuk2krRerDqRlGQv4MPA66vqu01X/t6r9phX95lRdRpwGsCmTZtqdna2rziO624NcszsspYvtb9+tlnKcR0tkk69ere+9rfW5Rp0DJs37uClfb5mg7QWr+9yy7WS53YczM3N0e/nbFI4hptGydaJGrChnj91W+q8YzXbLHQ+tNyYVrLNKMsNvc8rBn2MYay/km3OPGzPnc47xrHcK91GktaLVSWSktyPJol0TlV9pJ1903yT6yT7ATe387cCB3ZsfgBw42qOP2r+WNBq+R5aM/NjuF2Z5EHAFUkuAI6jGYPklCQn0YxBciI7j0HyVJoxSJ46ksglrVfr5vxJkiRNthUPtt1ewT8duLaq3tmx6Hzg2PbxscDHOua/Io1DgNvt3y9pLVTVtvkWRVV1B9A5httZ7WpnAS9qH/9wDJKquhTYu/0hJ0nD4vmTJEmaCKtpkXQo8HLg6iRXtfPeCJwCnJfkeOAbwJHtsk/SdBu5jqbryCtXcWxJ6sswx3Cb5vGmJqVs3ePAbd648/KlXrOVjNXRbVzGS5uU12y5pqFcST5AM7D2vkm2Am/G8ydJkjQhVnPXtkvo3W8f4Fk91i/g1Ss9ntYXu3xpEIY9hts0jjc1b1LKttwxLbrLtZKxOpaKYVTjaEzKa7Zc01Cuqjp6gUWeP0mSpLG34q5tkjTOFhvDrV3uGCSSJEmStEwmkiRNHcdwkyRJkqS1saq7tkkrYbc1DYFjuEmSJEnSGjCRJGnqOIabJEmSJK0Nu7ZJkiRJkiSpLyaSJEmSJEmS1Be7tkmSJlb3mGuSJEmS1paJJEmSlsEbBkiSJGk9s2ubJEmSJEmS+mKLJEnSWOrVbc3WP5IkSdJo2SJJkiRJkiRJfTGRJEmSJEmSpL7YtU0aY8sd1NdBgCVJkqTx4fm5ppEtkiRJkiRJktQXWyRJkrSGeg0a3qnXlUmvXkqSJGlc2SJJkiRJkiRJfTGRJEmSJEmSpL6sOJGU5IwkNyf5fMe8tyS5IclV7d/hHcvekOS6JF9K8nOrDVySJGmaJHlMxznUVUm+m+T1i51fSZIkDdtqxkg6E/gj4Oyu+e+qqnd0zkjyOOAo4PHAI4G/T/ITVXXPKo4vSZI0NarqS8DBAEl2BW4APgq8kh7nV5K0Ftr653Lghqp6QZJHAecC+wBXAi+vqruT7E7zW/CngFuAX6yqLSMKeySuvuF2jltiLERpGq04kVRVFyfZ0OfqRwDnVtX3ga8luQ54CvAvKz2+JGm6LDUotbTOPAv4SlV9PcmoY5G0vrwOuBZ4cDv9dppk9rlJ/hQ4Hnhv+//Wqnp0kqPa9X5xFAFLGq61uGvba5K8giaLvbmqbgX2By7tWGdrO+8+kpwAnAAwMzPD3NxcXwfdvHHHTtPd2y21fKn9DXKfM3s0j/sp21qXa9AxzOwxmBjWulzLfX0HVa7lbjOM13f79u19f84mSZIzgBcAN1fVE9p5bwF+FfhWu9obq+qT7bI30JwQ3QP8elX93dCDlqR7HQV8oGO61/nVDw3q/KnbUt9Lq9lmofOh5ca0km1GWW7ofV4x6GMMY/2VbNN93jGO5V7pNtMgyQHA84GTgf+ZJpP9TOCX2lXOAt5Ck0g6on0M8CHgj5KkqmqYMUsavkEnkt4LvA2o9v+pwC8DvS6l9axgquo04DSATZs21ezsbF8H7m5SuOWY2WUtX2p/g9zn5o07OPXq3ZZcv59jrLZcg45h88YdvHSJ16yfGNa6XMt9fQdVruVuM4zXd25ujn4/ZxPmTOx+K2kCJbk/8PPAG9pZC51f/dCgzp+6LfW9tJptFjofWm5MK9lmlOWG3ucVgz7GMNZfyTZnHrbnTucd41julW4zJd4N/AbwoHb6YcBtVTWfWetsELA/cD1AVe1Icnu7/re7d7qSZPckXOycT4gvx7DLNAnPI0xGnMZ4r4EmkqrqpvnHSd4HfLyd3Aoc2LHqAcCNgzy2JHWy++3odXdV23LK80cUiTRxngdcOX9etcj5lSQNTJL5ltxXJJmdn91j1epj2c4zV5DsnoSLnX94zsc49erl/aQedhJyEp5HmIw4jfFeA00kJdmvqra1ky8G5u/odj7w/iTvpLnafxDwmUEeW5L6tOLut0tdTZuEqxQrtZKyDaLLabeVdLtYbPuVdLFY65h67WMl76tpfT9Oa7m6HE1Ht7ZFzq8kaZAOBX6+vTPkA2jGSHo3sHeS3dpWSZ0NAuYbC2xNshvwEOA7ww9b0rCtOJGU5APALLBvkq3Am4HZJAfTZKK3AL8GUFXXJDkP+AKwA3i1XUYkjcCqut8udTVtEq5SrNRKyjaILqfdVtLtYrHtu8u1kjuvDDqmXvtYydXLaX0/Tmu55iV5IPAc2nOo1u/1Or+SpEGqqjfQdqltWyT9r6o6Jsn/AV5Cc+e2Y4GPtZuc307/S7v8HxwfSVofVnPXtqN7zD59kfVPphm0TdIIdXc3OvOwPUcUyfDZ/VbSuKuqu2jGGOmc9/IRhSNJACcC5yb5HeBz3Pub73TgL9shAb5DM96kpHVgLe7aJkljye63kiRJS6uqOWCuffxVmrEju9f5D+DIoQYmaSyYSJI0lex+K0mSJEmDZyJJ0lSy+60kSZIkDd4uow5AkiRJkiRJk8FEkiRJkiRJkvpi1zZJksZc990Wt5zy/BFFIkmSpPXORJIkSZIkSUvovrCzeeOIApFGzK5tkiRJkiRJ6ostkiRJA9F9lU6SJEnS9LFFkiRJkiRJkvpiiyRJ0kjYgkmSJEmaPLZIkiRJkiRJUl9MJEmSJEmSJKkvJpIkSZIkSZLUFxNJkiRJkiRJ6ouJJEmSJEmSJPXFu7ZJkiRJkjQE3Xet3XLK80cUibRyq2qRlOSMJDcn+XzHvH2SXJDky+3/h7bzk+QPklyX5N+SPHm1wUuSJE2TJFuSXJ3kqiSXt/N6nltJkiSNwmq7tp0JHNY17yTgwqo6CLiwnQZ4HnBQ+3cC8N5VHluStEY2nPSJnf4kDdUzqurgqtrUTi90biVJkjR0q0okVdXFwHe6Zh8BnNU+Pgt4Ucf8s6txKbB3kv1Wc3xJWogtJiVNkYXOrSRJkoZuLcZImqmqbQBVtS3JI9r5+wPXd6y3tZ23rXPjJCfQtFhiZmaGubm5vg66eeOOnaa7t1tq+VL7G+Q+Z/ZoHvdTtrUu16BjmNljMDGsdbmW+/oOqlzL3WYtnofudbZv397352zCnAn8EXB2x7z5q/qnJDmpnT6RnVtMPpWmxeRThxqtNEAbTvoEmzfu4Li2NZnjL0yUAj6dpIA/q6rTWPjc6ocGdf7UbanvpdVss9D50HJjWsk2oyw39D6vGPQxhrH+SrbpPu8Yx3KvdBtJWi+GOdh2esyr+8xoTphOA9i0aVPNzs72tfPjugctO2Z2WcuX2t8g97l54w5OvXq3Jdfv5xirLdegY9i8cQcvXeI16yeGtS7Xcl/fQZVrudusxfPQvc6Zh+1Jv5+zSVJVFyfZ0DX7CGC2fXwWMEeTSPphi0ng0iR7J9lv/oebJA3RoVV1Y5ssuiDJF/vZaFDnT92W+l5azTYLnQ8tN6aVbDPKckPv84pBH2MY669km+7zjnEs90q30fSxe7/U21okkm6a/wHWdl27uZ2/FTiwY70DgBvX4PiStJA1bTE5Ta27VtJybS2udA96H0u9ZuMQUz/76Oe1mG/t0c/6k2SaPme9VNWN7f+bk3wUeAoLn1tJkiQN3Vokks4HjgVOaf9/rGP+a5KcS9Nl5Hav9ksaEwNpMTk3Nzc1rbtW0nJtLa50D3of3dt3v2bjEFNf+7j6zp330dV17bi2a9upV++24DEm1TR9zrol2RPYparuaB8/F3grC59bSZIkDd2qEklJPkDTTWTfJFuBN9Oc5JyX5HjgG8CR7eqfBA4HrgPuAl65mmNL0grYYlLSOJsBPpoEmnO091fVp5J8lt7nVpIkSUO3qkRSVR29wKJn9Vi3gFev5niStEq2mJToPeaDA3KPXlV9FXhij/m30OPcSpIkaRSGOdi2JA2NLSbXngNQSpIkSeuPiSRJU8kWk5IkSZI0eLuMOgBJkiRJkiRNBlskSdI6ZLe09cXXW5IkSYNiiyRJkiRJWueSHJjkH5Ncm+SaJK9r5++T5IIkX27/P7SdnyR/kOS6JP+W5MmjLYGkYTGRJEmSJEnaAWyuqscChwCvTvI44CTgwqo6CLiwnQZ4HnBQ+3cC8N7hhyxpFEwkSZIkSdI6V1XbqurK9vEdwLXA/sARwFntamcBL2ofHwGcXY1Lgb2T7DfksCWNgIkkSZIkSdIPJdkAPAm4DJipqm3QJJuAR7Sr7Q9c37HZ1naepCnnYNuSJEmSJACS7AV8GHh9VX03yYKr9phXC+zzBJrub8zMzDA3N7dkHNu3b+9rvbW0eeOORZfP7LH0OktZ6zKOw/PYj0mI0xjvZSJJkiRJkkSS+9Ekkc6pqo+0s29Ksl9VbWu7rt3czt8KHNix+QHAjb32W1WnAacBbNq0qWZnZ5eMZW5ujn7WW0vHLXHX080bd3Dq1av7Sb3lmNlVbb+UcXge+zEJcRrjvezaJkmSJEnrXJqmR6cD11bVOzsWnQ8c2z4+FvhYx/xXtHdvOwS4fb4LnKTpZoskSZIkSdKhwMuBq5Nc1c57I3AKcF6S44FvAEe2yz4JHA5cB9wFvHK44UoaFRNJkiRJkrTOVdUl9B73COBZPdYv4NVrGpSksWTXNkmSJEmSJPXFRJIkSZIkSZL6Ytc2SZK0pA1dd67ZcsrzRxSJJEmSRslEkiRJuo/uxJEkSZOkn+8xL4pIK7NmiaQkW4A7gHuAHVW1Kck+wAeBDcAW4KVVdetaxSBJ64WtRaTJluRA4GzgR4AfAKdV1XuSvAX4VeBb7apvrKpPjiZKSdKgeQ6nSbTWYyQ9o6oOrqpN7fRJwIVVdRBwYTstSUOVZEuSq5NcleTydt4+SS5I8uX2/0NHHaekdWUHsLmqHgscArw6yePaZe9qz6cONokkSZJGbdiDbR8BnNU+Pgt40ZCPL0nzTHRLGhtVta2qrmwf3wFcC+w/2qgkSZLuay3HSCrg00kK+LOqOg2Yqapt0JwwJXlE90ZJTgBOAJiZmWFubq6vg23euGOn6e7tllq+1P4Guc+ZPZrH/ZRtrcs16Bhm9hhMDGtdruW+voMq13K3WYvnoXud7du39/05m3JHALPt47OAOeDEUQUjaf1KsgF4EnAZcCjwmiSvAC6nabV0n2EBBnX+1G2p76XVbLPQ+dByY1rJNqMsN/Q+rxj0MYax/kq26T7vGMdyr3QbTR67lUkrk6pamx0nj6yqG9tk0QXAa4Hzq2rvjnVuraoFu49s2rSpLr/88r6Ot1QlsNxKotfgbIPa5+aNOzj16t36qqjWulyDjmHzxh289pgjVh3DWpdrua/voMq13G3W4nnoXufMw/ZkdnZ2qVBJckVH652JluRrwK00Ce8/q6rTkty2VP3U9UPtp84999yd9rt9+3b22muvNY+/l6tvuH2n6Y37P2RZ6y/lUQ/Z9T5lW+4+umNa7vaD2Ef39t2v2TjENIh9XH3D7czsATd9b3X7WM76w9Lrc/aMZzxjmuqnvYCLgJOr6iNJZoBv09RXbwP2q6pfXmwfqzl/6rbU99JqtlnofGi5Ma1km1GWG3qfVwz6GMNYfyXbdJ93jGO5V7pNL9N0/jQo/dZRc0ebggUAACAASURBVHNzfZ2jLmQYN42Yr8cGadDJrNU+j8MyCXFOW4yrqZ/WrEVSVd3Y/r85yUeBpwA3JdmvbY20H3DzWh1fkhZxaGeiO8kX+9mobVl5GjQnQd2V9LC+XHqfGHVV51ffudNk90nJccs8ueqVcFzuPrYcs7rtB7GP7u27X7NxiGkQ+zjupE8s++R2ueXoFfcwTMJJ3EoluR/wYeCcqvoIQFXd1LH8fcDHRxSeJEkSsEaJpCR7ArtU1R3t4+cCbwXOB44FTmn/f2wtji9JizHRLWncJAlwOnBtVb2zY/5+88MCAC8GPj+K+CRJw2F3O02CtWqRNAN8tDknYjfg/VX1qSSfBc5LcjzwDeDINTq+JPVkolvSmDoUeDlwdZKr2nlvBI5OcjBN17YtwK+NJjxJkqTGmiSSquqrwBN7zL8FeNZaHFOS+mSiW9LYqapLgPRY9MlhxyJJkrSYtbxrmySNHRPdkiRJkrRyu4w6AEmSJEmSJE0GWyRJkiRJkjSGHHxb48gWSZIkSZIkSeqLiSRJkiRJkiT1xa5tkjRmbMKsSeD7VJK0lq6+4XaO6/iu8XtGGh8mkiRJkiRJE637Ase08kKOxoGJJEmSJEnSRFkviaOl9HoeTC5prZlIkiRJA9fPCb4nupIkSZPHRJIkSRoJm+dLkiRNHhNJkiRJkiRNic4LNZs37mB2GeuDF3a0NBNJkjRi9vGXJEmSNClMJEnSkJk4kiRJkjSpTCRJkiRJkrROLHVR065uWoqJJEmSJEmSppSt4TVoJpIkSZIkSWPNZIg0PkwkSZKksWBTekmSxl+vpJ7f2evL0BNJSQ4D3gPsCvx5VZ0y7BgkqZe1qp+8giZptTx/kjSurJ+k9WeoiaQkuwJ/DDwH2Ap8Nsn5VfWFYcYhSd2snySNK+snSePK+ml96Oei6HIvnNqCabINu0XSU4DrquqrAEnOBY4ArGgkjZr1k6RxZf0kaVxZP2lFuhNPmzfu4Lg1bsXfnbyyS/3KpaqGd7DkJcBhVfUr7fTLgadW1Ws61jkBOKGdfAzwpaEFODz7At8edRBrZFrLNq3lgv7L9mNV9fC1DmZUBlQ/+T6ZPNNaLpjesvUql/XTZJ4/Tet7tB+Wff1Y9/VTO38lddQkvFeMcXAmIc5pi3HF9dOwWySlx7ydMllVdRpw2nDCGY0kl1fVplHHsRamtWzTWi6Y7rIt06rrp2l+Lqe1bNNaLpjesk1ruZYwledP6/S1BCz7ei37lFqyfoKV1VGT8F4xxsGZhDiN8V67rPUBumwFDuyYPgC4ccgxSFIv1k+SxpX1k6RxZf0krUPDTiR9FjgoyaOS3B84Cjh/yDFIUi/WT5LGlfWTpHFl/SStQ0Pt2lZVO5K8Bvg7mttDnlFV1wwzhjExUU3Pl2layzat5YLpLlvfBlQ/TfNzOa1lm9ZywfSWbVrLtaApPn9ad69lB8uuqbDG9dMkvFeMcXAmIU5jbA11sG1JkiRJkiRNrmF3bZMkSZIkSdKEMpEkSZIkSZKkvphIGrIkW5JcneSqJJePOp7VSHJGkpuTfL5j3j5JLkjy5fb/Q0cZ40osUK63JLmhfd2uSnL4KGNciSQHJvnHJNcmuSbJ69r5E/+ajVqS30/yxST/luSjSfbuWPaGJNcl+VKSnxtlnMuV5Mj2vfKDJJu6lk1sueYlOayN/7okJ406ntWY4vrYemtKTOt7dCnr+T2c5AFJPpPkX9uy/3Y7/1FJLmvL/sF2gGbphybh+7lXnTZuFqp/xslC9cQ4SrJrks8l+fioY1nIMHMNJpJG4xlVdXBVbVp61bF2JnBY17yTgAur6iDgwnZ60pzJfcsF8K72dTu4qj455JgGYQewuaoeCxwCvDrJ45iO12zULgCeUFU/Cfw78AaA9vk9Cng8zXvqT5LsOrIol+/zwP8LXNw5cwrKRRvvHwPPAx4HHN2Wa1KdyXTWx9Zb0+NMpvM9upT1/B7+PvDMqnoicDBwWJJDgLfTnFMdBNwKHD/CGDVmJuj7+Ux6/14YJwvVP+NkoXpiHL0OuHbUQfRhKLkGE0lasaq6GPhO1+wjgLPax2cBLxpqUAOwQLkmXlVtq6or28d30FSE+zMFr9moVdWnq2pHO3kpcED7+Ajg3Kr6flV9DbgOeMooYlyJqrq2qr7UY9FEl6v1FOC6qvpqVd0NnEtTrok0xfWx9daUmNb36FLW83u4Gtvbyfu1fwU8E/hQO38qy65VmYjv50n4vbBI/TM2FqknxkqSA4DnA38+6ljGhYmk4Svg00muSHLCqINZAzNVtQ2aygt4xIjjGaTXtF2Xzpj0JuhJNgBPAi5jul+zUfhl4G/bx/sD13cs28qYfYGv0DSUaxrKsJSp+mxbb02ldfU6rsf3cNsV5CrgZprWu18Bbuu4+DKNda9WZz18Pw9dV/0zVrrriaoauxiBdwO/Afxg1IEsYWi5BhNJw3doVT2Zprnmq5M8fdQBqS/vBf4LTZPLbcCpow1n5ZLsBXwYeH1VfXfU8UyKJH+f5PM9/o7oWOdNNM2Iz5mf1WNXY3WVpZ9y9dqsx7yxKlcfpqEM64b1libden0PV9U9VXUwTUvdpwCP7bXacKPSmPP7ecDGvf7prieSPGHUMXVK8gLg5qq6YtSx9GFouYbd1mrH6q2qbmz/35zkozRfqhcvvtVEuSnJflW1Lcl+NJnliVdVN80/TvI+YGwHWVtMkvvRfJGcU1UfaWdP5Ws2aFX17MWWJzkWeAHwrKqaP+HZChzYsdoBwI1rE+HKLFWuBYx9ufowDWVYylR8tq23ptq6eB19D0NV3ZZkjmaclr2T7Na2SprGulersx6+n4dmgfpnLHXUE4fRjNM5Lg4Ffj7NzZYeADw4yV9V1ctGHNd9DDPXYIukIUqyZ5IHzT8Gnst4fUgG4Xzg2PbxscDHRhjLwLQnefNezAS+bkkCnA5cW1Xv7Fg0la/ZMCU5DDgR+Pmquqtj0fnAUUl2T/Io4CDgM6OIccCmoVyfBQ5q7x50f5rBw88fcUyDNvGfbeutqTf1r+N6fg8neXjau5gm2QN4Ns0YLf8IvKRdbSrLrlVZD9/PQ7FI/TM2FqgnvjjaqHZWVW+oqgOqagPN+/EfxjGJNOxcQ+69cK61luTHgY+2k7sB76+qk0cY0qok+QAwC+wL3AS8Gfhr4DzgR4FvAEdW1VgPRNdtgXLN0nRrK2AL8GvzYxtMiiRPA/4JuJp7+/e+kaav9ES/ZqOW5Dpgd+CWdtalVfWqdtmbaMZN2kHTpPhve+9l/CR5MfCHwMOB24Crqurn2mUTW6557ZWldwO7AmdYH48f663pMa3v0aWs5/dwkp+kGUx7V5qL1+dV1Vvb8+FzgX2AzwEvq6rvjy5SjZtJ+H7uVadV1ekjDarLQvVPjdHdpxeqJ0Yb1cKSzAL/q6peMOpYug0712AiSZIkSZIkSX2xa5skSZIkSZL6YiJJkiRJkiRJfTGRJEmSJEmSpL6YSJIkSZIkSVJfTCRJkiRJkiSpLyaSJEmSJEmS1BcTSZIkSZIkSeqLiSQtKsmGJJVkt1HHMmxJ5pL8yqjjkCRJkiRpXJhIGiNJtiS5O8m+XfOvapM5G0YTmSRNnyQPaOvWAwawr+OT/E3H9GySryTZnuSwJPsn+b9J7khy8mqPJ2lyDLKuGcX+JU0n6yathomk8fM14Oj5iSQbgT1GF44kDU+beJn/+0GS73VMH7PEtocluW4NYjo3yffbJNAdSf4tyduS7DW/TlWdXlUv7NjsZOD3qmqvqvoU8N+BLVX1oKp606BjlLQ841jXTIIkp7Q/DJ846likaWTdtDztOdpvds37r0l2jCqm9cJE0vj5S+AVHdPHAmfPTyTZPck7knwjyU1J/jTJHu2yhyb5eJJvJbm1fXxAx7Zz7Y+ff25/DH26u/XTIo5pj/ntJD/8EdTG8+4kN7Z/706ye7tsNsnWJL+R5OYk25K8KMnhSf49yXeSvLFjX7skOam9in9LkvOS7LNUYElekeTr7Tb/u23Z9ew+4lv0+eo6xqOTXJTk9vY5+GCfz5ukZWgTL3tV1V7AN4AXdsw7Z4Shva2qHgQ8HPhV4BnAPyV5wALr/xhwTdf0F9Y2REn9GuO6Zmwl2QV4GfAddj5XlTQg1k2aFCaSxs+lwIOTPDbJrsAvAn/VsfztwE8ABwOPBvYHfqtdtgvwFzQ/WH4U+B7wR137/yXglcAjgPsD/6vPuJ4GPAZ4FvBbSR7bzn8TcEgbzxOBpwCdWeEfAR7QEef7aE5Cfgr4mXZfP96u++vAi4CfBR4J3Ar88WJBJXkc8CfAMcB+wEPaY81bLL5+nq95bwM+DTwUOAD4w8XikrQ2kuyR5I/bxPTWJL+f5H5JHgZ8FPjxjit3D0tyaJLL2iTwjUnelVWM+VZV/1FVlwEvpKkLXtbG9aokf98+3kpTh326jeMDNHX5/26nf2aVT4OkNTbsuqa9+PaNJOmYd3SSz7SP+95/kkuTvKxj+of1Uzv9hCT/0F5EuzbJi/oM89k051mbaS4w7tpv+SQNhnWTxoWJpPE03yrpOcAXgRva+aG5Ev4/quo7VXUH8LvAUQBVdUtVfbiq7mqXnUyTlOn0F1X171X1PeA8mgRLP367qr5XVf8K/CtNUgaaBM5bq+rmqvoW8NvAyzu2+0/g5Kr6T+BcYF/gPVV1R1VdQ3PF/ifbdX8NeFNVba2q7wNvAV6yRGX3EuBvquqSqrqbJllVHcsXjK/P56uzHD8GPLL9IXnJos+WpLXy2zR1xkaahPQs8BtVdQvwYuCrHVfubqH57L4G2Icmef1CYNWD6FfVrcA/tvvsXnYAcDPw3DaOo4EP07Rq2quq/mm1x5e05oZd11xEc573tI55vwS8v308kLosyYOBC4DTac7JXgGckeTRfWx+LM0P1Q8CDwR+brnHl7Rq1k0aCyaSxtNf0nxAj6OjWxtNl4oHAlckuS3JbcCn2vkkeWCSP0vTzeu7wMXA3l1XjL7Z8fguYC/6s9B2jwS+3rHs6+28ebdU1T3t4++1/2/qWP69jn39GPDRjrJdC9wDzCwS1yOB6+cnquou4Jau5T3j6/P5mvcbNJXoZ5Jck+SXF4lJ0to5BnhzVX27qm4Cfoedk9c7qarPVNVnq+qeqvoK8OcsnDBerhtpTpwkTZ+h1jVVVTQJmqMB0nTtf3Y7b5B12YuBz1fVOe2+Pgv8DfALi23U/sh7MfD+9mLkR2kSS5KGy7rpvt40//ux/Q35mRUcX8tkImkMVdXXaQbdPhz4SMeib9MkXh5fVXu3fw9p+9BC09T4McBTq+rBwNPb+WHt3EiTAJr3o+28lbgeeF5H2fauqgdU1Q2LbLONpnsJ0DT3BB7WZ3x9P19V9c2q+tWqeiRNy6k/MUMuDVfbrPpHuG9yeP/eWzTdX5P8bZox5b5L02qx37HhlrI/zVghkqbICOua9wNHti2xjwQuqaptA9w/NOdET+/60fULNMMDLOZI4A5gvhvKOcARSfZeQQySVsC6aUEnd/5+pBnKRGvMRNL4Oh54ZlXd2THvBzRjDL0rySMA0txSer5p8YNoEk23tdniNw8hzg8Av5nk4WkG7v4tdh7TaTn+FDg5yY8BtPs8YoltPgS8MMn/k+T+NM09OxNBi8XX9/OV5MjcOxD3rTTd5+5ZaH1Jg9deFfsm900Ozyeb6z4bNXXmlcB/aRPGb2UAyfX2x9MsYDc1acqMqq6pqitpWlU/m527jix3/3fStGCf9yMdj68HPt110W6vqnr9EuEdC+wNbE3yTZrW87sDL+2vdJJWy7pJ48RE0piqqq9U1eU9Fp0IXAdc2mZ9/56mVQ3Au4E9aFouXUrT7W2t/Q5wOfBvwNU0FcnvrHBf7wHOpxmg9g6aMjx1sQ3acZZeSzP+0jaaq2U3A9/vI77lPF8/DVyWZHsb4+uq6mvLLJ+k1fsA8OZ2AMlH0AyoP58cvgl4RJLOLrsPAm6vqu1JHk8zztyKJXlAkqcAH6Np3bjSxLmk8TaquuYDNDdC+Wl2bpW+nP1fRTPG5AOS/FeaoRLm/TXwpCS/mGaA3vsnOSTJTyy0szQ3RXka8FyasTXnb2DybuzeJg2bdZPGQprEpjQd2orzNuAgEz3SZEuyBfiVquq8o8cDgVNp+tL/gCaJfFJV3d02+f5L4HnArsCPA08C3kszNtrlwP8FnlJVz07yAJpWiQdW1dZF4ji3Pd7dNFfZttAklE+pqu+267wKeElVPbud/mY7fUnHPj5fVStNtEtaI+NS17THPQj4d+AjVfULHfOf1e/+k8zQ/Oj7aeBzNGNAHtJRPz2+LdumdvdX0Vwgu2aBmN4CPKeqDu2av4Hm4uZjq+rLi5VL0vJZNy1eN7Xb3Of8qk1Sfb6qVnyXXi3NRJImXpIXAhfS/MA7laYV05PLN7ckSZIkSQNl1zaR5Jgk23v8LZj9HaY+4juCpovJjcBBwFEmkSRJkiRJGjxbJEmaWEnOAF4A3FxVT2jn7UNzS9INNF2QXlpVt7bNfd9DczfEu4Dj2sEDSXIs8Jvtbn+nqs4aZjk0ekm+Asz0WHRsVX142PFImk7jXNeMc2yS1tY4f/7HObb1zESSpImV5OnAduDsjkTS7wHfqapTkpwEPLSqTkxyOM3A7IfTdH98T1U9tU08XU7TH7uAK4CfqqpbR1AkSZIkSRprYz0A1b777lsbNmzoa90777yTPffcc20DGpFpLhtMd/mmqWxXXHHFt6vq4aOOo1NVXdwO9tnpCJrbsgOcBczR3O3wCJqEU9Hc9XDvJPu1615QVd8BSHIBcBjNYIAL6rd+mqb3wDSVBaarPOu9LONYP41Sd/00Te+PbtNcNrB8k+7OO+/ki1/8ovVTl846aj28B6a1fNNcNlgf5VtN/TTWiaQNGzZw+eWX97Xu3Nwcs7OzaxvQiExz2WC6yzdNZUvy9VHH0KeZqtoGUFXb2lujAuwPXN+x3tZ23kLz7yPJCcAJADMzM7zjHe9YMpjt27ez1157LbneJJimssB0lWe9l+UZz3jGpNRPQ9F9/jRN30XdprlsYPkm3dzcnPVTD5111Hp4D0xr+aa5bLA+yrea+mmsE0mSNEDpMa8WmX/fmVWnAacBbNq0qfr5cpmmL6FpKgtMV3ksiyRJkobFu7ZJmjY3tV3WaP/f3M7fChzYsd4BNHf6W2i+JEmSJKmLiSRJ0+Z84Nj28bHAxzrmvyKNQ4Db2y5wfwc8N8lDkzwUeG47T5IkSZLUxUSSpImV5APAvwCPSbI1yfHAKcBzknwZeE47DfBJ4KvAdcD7gP8O0A6y/Tbgs+3fW+cH3paktdAO9v+hJF9Mcm2S/5ZknyQXJPly+/+h7bpJ8gdJrkvyb0mePOr4JU0v6ydJ/XCMJEkTq6qOXmDRs3qsW8CrF9jPGcAZAwxNkhbzHuBTVfWSJPcHHgi8Ebiwqk5JchJwEs0dJ58HHNT+PRV4b/tfktaC9ZOkJZlIGqANJ31ip+ktpzx/RJFIGifWDZLmJXkw8HTgOICquhu4O8kRwGy72lnAHM0PtSOAs9tk+KVta4H95u9Oudasv6T1w/pJUr9MJC2DlZUkSVqlHwe+BfxFkicCVwCvA2bmf3xV1bYkj2jX3x+4vmP7re28ofxQk7SurFn9lOQE4ASAmZkZ5ubmANi+ffsPHy/X5o07dppe6X7W0mrKN+6muWywPsq3GiaSJEmShmc34MnAa6vqsiTvoekmspD0mFf3WWmBH2kw3T/U1sOJvuWbXKv9oTYCa1I/AVTVacBpAJs2barZ2VmgqVPmHy/Xcd0X+Y9Z2X7W0mrKN+6muWywPsq3GqtOJCXZFbgcuKGqXpDkUcC5wD7AlcDLq+ruJLsDZwM/BdwC/GJVbVnt8SVJkibIVmBrVV3WTn+I5ofaTfNdQpLsB9zcsf6BHdsfANzYvdOFfqTBdP9QWw8n+pZvck1gkmxN6idJ02cQd217HXBtx/TbgXdV1UHArcDx7fzjgVur6tHAu9r1JEmS1o2q+iZwfZLHtLOeBXwBOB84tp13LPCx9vH5wCvauyMdAtw+rPFHJK0v1k+S+rWqFklJDgCeD5wM/M8kAZ4J/FK7ylnAW2hG8D+ifQxNdvuPkqQdnE2SJGm9eC1wTntHpK8Cr6S5uHdekuOBbwBHtut+EjgcuA64q11XktaK9ZOkJa22a9u7gd8AHtROPwy4rarmO9TPD7gGHYOxVdWOJLe363+7c4eL9fFfzDD6WC81TsBajSOwHvqPT2v5prlskqSVqaqrgE09Fj2rx7oFvHrNg5IkrJ8k9WfFiaQkLwBurqorkszOz+6xavWx7N4Zi/TxX8ww+lgvNU7AWo0jsB76j09r+aa5bJIkSZKk9Wc1LZIOBX4+yeHAA4AH07RQ2jvJbm2rpM4B1+YHY9uaZDfgIcB3VnF8SZIkSZIkDdGKB9uuqjdU1QFVtQE4CviHqjoG+EfgJe1q3YOxzQ/S9pJ2fcdHkiRJkiRJmhCDuGtbtxNpBt6+jmYMpNPb+acDD2vn/0+aW0lKkiRJkiRpQqx2sG0AqmoOmGsffxV4So91/oN7R/iXJEmSJEnShFmLFkmSJEmSJEmaQgNpkTSNNnTdgU2SBqW7ftlyyvNHFIkkSZIkLY8tkiRJkiRJktQXE0mSJEmSJEnqi4kkSZIkSZIk9cUxkobIcVGk4UnyP4BfAQq4GnglsB9wLrAPcCXw8qq6O8nuwNnATwG3AL9YVVtGEbckSZIkjTNbJEmaOkn2B34d2FRVTwB2BY4C3g68q6oOAm4Fjm83OR64taoeDbyrXU+SJEmS1MVEkqRptRuwR5LdgAcC24BnAh9ql58FvKh9fEQ7Tbv8WUkyxFglSZIkaSLYtU3S1KmqG5K8A/gG8D3g08AVwG1VtaNdbSuwf/t4f+D6dtsdSW4HHgZ8u3O/SU4ATgCYmZlhbm5uyVi2b9/O5o33LLpOP/sZB9u3b5+YWPsxTeWxLJKkadc9TIik0TGRJGnqJHkoTSujRwG3Af8HeF6PVWt+k0WW3Tuj6jTgNIBNmzbV7OzskrHMzc1x6iV3LrrOlmOW3s84mJubo58yT4ppKo9l0aD4Q02SJC3Frm2SptGzga9V1bf+//buN8au8k7s+PdXIJCQBAeyTF3bqslipaC4C+wI3KWKpnhTgUFxKsGGFSWQeuUXhS1pLAUnfZG26gtHKiFEjai8OBtIUUiWsMVK0LbIcBXxAppAKA7xZvFSCwa8OLsBJxOUJSa/vrjPwHh8Z+bM3D/n3nO/H+nq3vOc5859fj7Xvznnmed5Tmb+GngA+D1gVZnqBrAWeLm8ngbWAZT9ZwA/G2yTJUmSJGn42ZEkqYleADZFxLvKWkebgR8DjwJXlzo3AA+W13vLNmX/I5l5wogkSZIkSRp3diRJapzMfIL2otlPAftp57rdwK3ApyPiIO01kPaUt+wBzirlnwZ2DrzRkiRJkjQCXCNJUiNl5ueBz88rfh64uEPdXwHXDKJdkiRJkjTKHJEkSZIkSZKkSuxIkiRJkiRJUiV2JEmSJEmSJKkSO5IkSZIkSZJUiR1JkiRJkiRJqsSOJEmSJEmSJFViR5IkSZIkSZIqOXmlb4yI04DvAaeWn3N/Zn4+Is4B7gPOBJ4Crs/MNyLiVOAe4HeBvwM+npmHumz/UFu/87t1N0GSJEmSJKlnuhmR9PfAZZn5O8AFwOURsQn4AnB7Zm4AXgW2lfrbgFcz81zg9lJPkiRJkiRJI2LFHUnZNlM2TymPBC4D7i/ldwMfK6+3lm3K/s0RESv9fEmSJEmSJA3Wiqe2AUTEScCTwLnAV4C/Bl7LzGOlyjSwprxeA7wIkJnHIuIocBbwt/N+5nZgO8DExAStVqtSW2ZmZirXrWLHxmNL1pn/eVXes9j7F9Lr2IZNk+NrcmySJEmSpPHTVUdSZr4JXBARq4A/B87rVK08dxp9lCcUZO4GdgNMTk7m1NRUpba0Wi2q1q3ixgrrGx267vjPq/Kexd6/kF7HNmyaHF+TY5MkSZIkjZ+e3LUtM18DWsAmYFVEzHZQrQVeLq+ngXUAZf8ZwM968fmSJEmSJEnqvxV3JEXEb5WRSETEO4HfBw4AjwJXl2o3AA+W13vLNmX/I5l5wogkSZKkpouIkyLihxHxnbJ9TkQ8ERHPRcQ3I+IdpfzUsn2w7F9fZ7slNZ/5SdJSuhmRtBp4NCKeAb4PPJyZ3wFuBT4dEQdpr4G0p9TfA5xVyj8N7OzisyVJkkbZLbT/ADfLu95KGhbmJ0mL6uaubc9k5oWZ+U8z80OZ+Z9L+fOZeXFmnpuZ12Tm35fyX5Xtc8v+53sVhCRJ0qiIiLXAlcBdZTvwrreShoD5SVIVXS22LUmSpGX7EvAZ4D1l+yy6vOutJPVIX/LTQnfmXs4djpe6Q/Yw3im5yXdwbnJsMB7xdcOOJEmNVNZwuwv4EO07RP4b4CfAN4H1wCHgDzLz1fLXszuALcDrwI2Z+VQNzZbUcBFxFXAkM5+MiKnZ4g5Vl3XX24Uu0qDZF2rjcKJvfKOr2wu1QetXfoKF78y9nDscL3WH7Kp3xB6kJt/BucmxwXjE1w07kiQ11R3AX2Tm1WVRyHcBnwP2ZeauiNhJe622W4ErgA3lcQlwZ3mWpF67FPhoRGwBTgPeS3sEwKqIOLn81b/TXW+nF7vr7UIXadDsC7VxONE3vtE1gp1kfclPkpqnm8W2JWkoRcR7gQ9TFvvPzDcy8zWOn8s/f47/Pdn2OO0TptUDbrakMZCZn83MtZm5HriW9l1sr8O73kqqmflJUlV2JElqog8APwX+tNy+9q6IOB2YyMzDAOX57FL/rTn+xdz5/5I0CN71VtKwMj9JOo5T2yQ10cnARcAfZ+YTEXEHi5/cdL0GyUJmZmbYsfHNReuMytD3pq1l0aR4jGU0ZWYLb/LJhQAAFCdJREFUaJXXzwMXd6jzK+CagTZM0tgzP0lajB1JkppoGpjOzCfK9v20O5JeiYjVmXm4TF07Mqf+ujnvnzv//y2LrUGykFarxW2P/XLROsO25shCmraWRZPiMRZJkiQNilPbJDVOZv4N8GJEfLAUbQZ+zPFz+efP8f9EtG0Cjs5OgZMkSZIkvc0RSZKa6o+Be8sd254HPkm78/xbEbENeIG3h2M/BGwBDgKvl7qSJEmSpHnsSJLUSJn5NDDZYdfmDnUTuKnvjZIkSZKkEefUNkmSJEmSJFViR5IkSZIkSZIqsSNJkiRJkiRJlbhGkiRJkipZv/O7x20f2nVlTS2RpONVyU/mMKk37EiSpJp5UiOpScxpkiQ1m1PbJEmSJEmSVIkjkobc+p3fZcfGY9xY/rrnX/UkSZIkqXuOoJRWxo4kSZIk9Y0XapIkNYtT2yRJkiRJklSJHUmSJEmSJEmqZMUdSRGxLiIejYgDEfFsRNxSys+MiIcj4rny/L5SHhHx5Yg4GBHPRMRFvQpCkiRJkiRJ/dfNiKRjwI7MPA/YBNwUEecDO4F9mbkB2Fe2Aa4ANpTHduDOLj5bkiRJkiRJA7bixbYz8zBwuLz+RUQcANYAW4GpUu1uoAXcWsrvycwEHo+IVRGxuvwcceJilJIkSZIkScOkJ3dti4j1wIXAE8DEbOdQZh6OiLNLtTXAi3PeNl3KjutIiojttEcsMTExQavVqtSGmZmZynWr2LHx2JJ15n9elfes5P0T73x7Xy9jHBa9PnbDpMmxSZIkSZLGT9cdSRHxbuDbwKcy8+cRsWDVDmV5QkHmbmA3wOTkZE5NTVVqR6vVomrdKm6sMDro0HXHf16V96zk/Ts2HuO2/Sd3fE8T9PrYDZMmxyZJkiRJGj9d3bUtIk6h3Yl0b2Y+UIpfiYjVZf9q4EgpnwbWzXn7WuDlbj5fkiRJkiRJg7PiEUnRHnq0BziQmV+cs2svcAOwqzw/OKf85oi4D7gEODru6yO5JpIkSZIkSRol3UxtuxS4HtgfEU+Xss/R7kD6VkRsA14Arin7HgK2AAeB14FPdvHZPWenjtQ8EXES8APgpcy8KiLOAe4DzgSeAq7PzDci4lTgHuB3gb8DPp6Zh2pq9gn56NCuK2tqiSRJkiQdr5u7tj1G53WPADZ3qJ/ATSv9PElagVuAA8B7y/YXgNsz876I+O/ANuDO8vxqZp4bEdeWeh+vo8GSJEmSNMy6WiNJkoZVRKwFrgTuKtsBXAbcX6rcDXysvN5atin7N8cidw6QJEmSpHFlR5KkpvoS8BngN2X7LOC1zDxWtqeBNeX1GuBFgLL/aKkvSZIkSZqjmzWSJGkoRcRVwJHMfDIipmaLO1TNCvvm/tztwHaAiYkJWq3Wkm2ZmZlhx8Y3K7R6YVU+ZxBmZmaGpi290KR4jEWSJEmDMrYdSb1YXNsFuqWhdSnw0YjYApxGe42kLwGrIuLkMupoLfByqT8NrAOmI+Jk4AzgZ/N/aGbuBnYDTE5O5tTU1JINabVa3PbYL7sK5tB1S3/OILRaLarEPCqaFI+xSJIkaVCc2iapcTLzs5m5NjPXA9cCj2TmdcCjwNWl2g3Ag+X13rJN2f9IuUGAJEmSJGmOsR2RJGks3QrcFxH/BfghsKeU7wG+HhEHaY9Euram9knSSHF0tqRhZX6S+seOJEmNlpktoFVePw9c3KHOr4BrBtowSZIkSRpBdiSNmPk964d2XVlTSyRJkiRJ0rhxjSRJkiRJkiRVYkeSJEmSJI25iFgXEY9GxIGIeDYibinlZ0bEwxHxXHl+XymPiPhyRByMiGci4qJ6I5A0KHYkSZIkDYgXapKG2DFgR2aeB2wCboqI84GdwL7M3ADsK9sAVwAbymM7cOfgmyypDmOzRpKr9kuSpCEwe6H2VES8B3gyIh4GbqR9obYrInbSvlC7leMv1C6hfaF2SS0tl9RomXkYOFxe/yIiDgBrgK3AVKl2N+2bmNxayu/JzAQej4hVEbG6/BxJDTY2HUmSJEl180JN0iiIiPXAhcATwMRszsnMwxFxdqm2BnhxztumS9kJ+SkittMetcTExAStVguAmZmZt17Pt/+lo8dt79i4sliWY6G2zJrfpo1rzli0/mLxjbomxwbjEV837EiSJEmqQS8v1Ba6SINmX6iNw4m+8Y2ubi/U6hIR7wa+DXwqM38eEQtW7VCWnSpm5m5gN8Dk5GROTU0B7Xww+3q+G2uYUXLouqlF989v01L1F4tv1DU5NhiP+LphR1LDzJ/Cd2jXlTW1RJIkLaTXF2oLXaRBsy/UxuFE3/hG1yh2kkXEKbRz072Z+UApfmV2JGRErAaOlPJpYN2ct68FXh5cayXVxcW2JUmSBmixC7Wy3ws1SQMX7R7tPcCBzPzinF17gRvK6xuAB+eUf6LcFGATcNRpt9J4sCNJkiRpQLxQkzTELgWuBy6LiKfLYwuwC/hIRDwHfKRsAzwEPA8cBP4E+Lc1tFlSDZzaJkmSNDizF2r7I+LpUvY52hdm34qIbcALwDVl30PAFtoXaq8DnxxscyWNi8x8jM7TaQE2d6ifwE19bZSkoWRHkiRJ0oB4oSZJkkadU9skSZIkSZJUiR1JkiRJkiRJqqSrqW0R8VXgKuBIZn6olJ0JfBNYDxwC/iAzXy2LS95Be57/68CNmflUN58vSeNg/fxbYe+6sqaWSJIkSRp33a6R9DXgvwH3zCnbCezLzF0RsbNs3wpcAWwoj0uAO8uzujD/AlOSJEmSJKlfuupIyszvRcT6ecVbgany+m6gRbsjaStwT1k08vGIWBURq72FrSRJ0vjyj2KShpX5SeqsH3dtm5jtHMrMwxFxdilfA7w4p950KTuuIykitgPbASYmJmi1WpU+dGZmZtG6OzYeq9b6ITTxzpW3v+q/X52WOnajrMmxSZIkSZLGTz86khbS6Va3eUJB5m5gN8Dk5GROTU1V+uGtVovF6t44wr3JOzYe47b9KztUh66b6m1j+mCpYzfKmhzbMIuIdbSn3P5D4DfA7sy8wzXcJEmSJKk7/bhr2ysRsRqgPB8p5dPAujn11gIv9+HzJekYsCMzzwM2ATdFxPm8vYbbBmBf2Ybj13DbTnsNN0mSJEnSPP0YkbQXuAHYVZ4fnFN+c0TcR3uR7aOujySpH0pumZ1i+4uIOEB7Km0j1nDrNF/fO7lJkiRJGoSuOpIi4hu0L8reHxHTwOdpdyB9KyK2AS8A15TqD9GeNnKQ9tSRT3bz2ZJURbkhwIXAE9SwhtvMzAw7Nr7ZVQxVDGItrqat+dWkeIxFkiRJg9LtXdv+cIFdmzvUTeCmbj5vOVxhX1JEvBv4NvCpzPx5eymkzlU7lPVkDbdWq8Vtj/2yapNXbBDroTVtza8mxWMskiSpqvnXyo5s13INcrFt1cAkoXEVEafQ7kS6NzMfKMWvzE5Zcw03SVI/eQ4mSWqqfiy2LUm1Kndh2wMcyMwvztk1u4YbnLiG2yeibROu4SZJkiRJHTkiacz41zGNiUuB64H9EfF0KfscruEmSZIkSV2xI0lS42TmY3Re9wiGYA03SVJ/1fGHs27X5/SPfdJ4MD+pCexI0nG8rbjUDP7ClyRJktQPrpEkSZIkSZKkShyRJEmSpJG23GkbjtqUNCjmJzWRHUmS1ADdzn2XJEmSpCqc2iZJkiRJkqRK7EiSJEmSJElSJU5tkyRJ0lhbak0S72orqS7mJw0jRyRJkiRJkiSpEkckSdIY8A4gkiRJknrBjiQtyQtQSZJUlzrOQ6rcCdO7ZUoyP2lcObVNkiRJkiRJlTgiSZLG0HIXbnQkoiRJkiRwRJIkSZIkSZIqasyIpP0vHeVG54JK0oo4l17SoNSRb4Yhxw1DGyQtzvwkVdOYjiStzEqShlNeJEmSJEkaT3YkqWt2LEnNt37nd9mx8diiIz/9vy9JkiQ1nx1J6rmlRjl5sSk1k53KkgZhbq5ZqoN7lHXqwF9uXu1HXjbXSwub//9jx8ZjNPGSux/5aSlVfv645qc64h74tzoiLgfuAE4C7srMXYNugyR1Yn7qrW7n24/LL3+pCvOTeqEff+wblwu3cYlzJcxPGlb+v+2fgXYkRcRJwFeAjwDTwPcjYm9m/niQ7VC95v+H/trlp9fUEult5qfh0+mCZ/4JwFInCJ5AqAnMT5KGlflJGk+DHpF0MXAwM58HiIj7gK2AiWaMdbrjXq+HRg7DxWOVi2LVyvw0Apb6v76c/Ts2HmNqBZ9pZ5VqYH6SNKzMT9IYiswc3IdFXA1cnpl/VLavBy7JzJvn1NkObC+bHwR+UvHHvx/42x42d5g0OTZodnxNiu0fZ+Zv1d2IfuljfmrSd6BJsUCz4hn3WMxPi+enJn0/5mtybGB8o+79wOnjnp9K+UI5ahy+A02Nr8mxwXjEt+L8NOgRSdGh7LierMzcDexe9g+O+EFmTq60YcOsybFBs+NrcmwN1Jf81KTvQJNigWbFYyyN11V+avK/aZNjA+MbdSW+9XW3o8+WzE+wcI4ak+9AI+NrcmwwNvGtX+n7/0EP21LFNLBuzvZa4OUBt0GSOjE/SRpW5idJw8r8JI2hQXckfR/YEBHnRMQ7gGuBvQNugyR1Yn6SNKzMT5KGlflJGkMDndqWmcci4mbgf9G+PeRXM/PZHv34ZU+HGyFNjg2aHV+TY2uUPuanJn0HmhQLNCseY2mwHuSnJv+bNjk2ML5R1/T4zE9La3J8TY4NjG9RA11sW5IkSZIkSaNr0FPbJEmSJEmSNKLsSJIkSZIkSVIlI9+RFBGXR8RPIuJgROysuz29FBHrIuLRiDgQEc9GxC11t6nXIuKkiPhhRHyn7rb0WkSsioj7I+IvyzH8Z3W3SYPVpPwUEYciYn9EPB0RP6i7PcsREV+NiCMR8aM5ZWdGxMMR8Vx5fl+dbVyOBeL5jxHxUjk+T0fEljrbWMVCv+NG+dgMmybkoOV+T6LtyyXmZyLionojWNr8c6GyaPETJbZvlgWMiYhTy/bBsn99ne2uotO5UMOO3b8v38sfRcQ3IuK0Jh2/fjI/jcx33PzEyB67vuanke5IioiTgK8AVwDnA38YEefX26qeOgbsyMzzgE3ATQ2LD+AW4EDdjeiTO4C/yMx/AvwOzY1THTQ0P/2LzLwgMyfrbsgyfQ24fF7ZTmBfZm4A9pXtUfE1TowH4PZyfC7IzIcG3KaVWOh33Cgfm6HRoBy03O/JFcCG8tgO3Dn4Ji/b/HOhL9D+/7wBeBXYVsq3Aa9m5rnA7aXesOt0LtSIYxcRa4B/B0xm5odoLzR9Lc06fn1hfhqN73hhfmobqWM3iPw00h1JwMXAwcx8PjPfAO4Dttbcpp7JzMOZ+VR5/QvaX+419baqdyJiLXAlcFfdbem1iHgv8GFgD0BmvpGZr9XbKg1Yo/PTKMnM7wE/m1e8Fbi7vL4b+NhAG9WFBeIZOYv8jhvZYzNkGpGDVvA92Qrck22PA6siYvWAm13Z/HOhiAjgMuD+UmV+bLMx3w9sLvWH0iLnQo04dsXJwDsj4mTgXcBhGnL8+sz8NALfcfPT6B67oq/5adQ7ktYAL87ZnqZBHS1zleFlFwJP1NuSnvoS8BngN3U3pA8+APwU+NMyHPSuiDi97kZpoJqWnxL43xHxZERsr7sxPTCRmYehfSIInF1ze3rh5jLc+qsxYtPB5v2Oa+KxqUPTclDV78moxT3/XOgs4LXMPFa257b/rdjK/qOl/rBa6FyoEccuM18C/ivwAu0LtKPAkzTn+PXTSB3rKsxPI/f9Nj91efxGvSOpUy9ZDrwVfRYR7wa+DXwqM39ed3t6ISKuAo5k5pN1t6VPTgYuAu7MzAuBX+L0jHHTtPx0aWZeRHto700R8eG6G6Tj3An8NnAB7ROG2+ptTnVN/B03JBqVg5bxPRmZuBc4F1qs/SMTW7Hcc6GRiq902G8FzgH+EXA67d+R843q8eunRv1bmJ+W3DeMzE9tKz5+o96RNA2sm7O9Fni5prb0RUScQjsx3ZuZD9Tdnh66FPhoRByiPZz1soj4H/U2qaemgenMnB1Bdj/tZKXx0aj8lJkvl+cjwJ/THpY+yl6ZHZJcno/U3J6uZOYrmflmZv4G+BNG5Pgs8DuuUcemRo3JQcv8noxS3CecC9EeAbCqTEWA49v/Vmxl/xkM9zTXhc6FmnDsAH4f+H+Z+dPM/DXwAPB7NOf49dOoHesFmZ9G9vttfury+I16R9L3gQ1l9fF30F5Aam/NbeqZMi9xD3AgM79Yd3t6KTM/m5lrM3M97eP2SGb+65qb1TOZ+TfAixHxwVK0GfhxjU3S4DUmP0XE6RHxntnXwL8EfrT4u4beXuCG8voG4MEa29K1efP0/xUjcHwW+R3XqGNTo0bkoBV8T/YCn4i2TcDR2WkKw2aBc6HrgEeBq0u1+bHNxnx1qT+0fxFf5Fxo5I9d8QKwKSLeVb6ns/E14vj1mflpyL/j5idgRI9d0f/8lJkj/QC2AH8F/DXwH+puT49j++e0h5Q9AzxdHlvqblcf4pwCvlN3O/oQ1wXAD8rx+5/A++puk4+BfwcakZ9ozyP/v+Xx7KjFAnyD9nSvX9P+i8s22vO+9wHPlecz625nl/F8Hdhf8s1eYHXd7awQR8ffcaN8bIbt0YQctNzvCe3h+V8pMe+nfcea2uOoEOdb50Il5/4f4CDwZ8Cppfy0sn2w7P9A3e2uENcJ50JNOnbAfwL+knbn/deBU5t0/Pr8b2d+GoHveGm3+WkEj12/81OUN0qSJEmSJEmLGvWpbZIkSZIkSRoQO5IkSZIkSZJUiR1JkiRJkiRJqsSOJEmSJEmSJFViR5IkSZIkSZIqsSNJkiRJkiRJldiRJEmSJEmSpEr+P8jacbsszVMiAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 1440x1080 with 20 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "cols_to_plot = [ 'HTP', 'ATP', 'MW', 'HT_LP', 'AT_LP', 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3',\n", | |
| " 'HTLossStreak5', 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3',\n", | |
| " 'ATLossStreak5', 'HTGD', 'ATGD', 'DiffPts', 'DiffFormPts', 'DiffLP',\n", | |
| " 'H2H_Home_pts', 'H2H_Away_pts', 'Mean_home_goals', 'Mean_away_goals',\n", | |
| " 'Total_value_H', 'Total_value_A', 'H2H_Diff', \n", | |
| " 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw']\n", | |
| "plots = df_final[cols_to_plot].hist(bins=50, figsize=(20,15))\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Korelacja na wykresie" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 864x576 with 25 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from pandas.tools.plotting import scatter_matrix\n", | |
| "attributes = [\"DiffLP\", \"LP_Diff\", \"HTGD\", \"ATGD\", \n", | |
| " \"Mean_home_goals\"]\n", | |
| "plots = scatter_matrix(df_final[attributes], figsize=(12, 8))\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0xe2b3048>" | |
| ] | |
| }, | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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o62B7d+qy5fitv3mWh5+baB73wbsH+b3/4U3rfZvW9Trf6Kz0d7qeA8FCiGellIfXOq4VC/9c48dq/CgU1wTLKa/vnZ5aMZC6EvO+bUvXsE1BFMlFvu2VAozvu30Hb71xoKnQ+7I2RcdftPhs706tS9HPs5zV+/JYgYefm0DXYleIF0Q8/NwEv3h/oWVL/3oeOqICwSvTSqXt7wAIITriX2WlbVIpFOtkuQDkl46P87mnhkknjGUDqSvh+CGzFY/xotMsitqWSzYVxUqukJLj8eTZWYJIcnqiDAjStgESfmxPF7t7MssqmrX65S/Nkjl2fq75+Py/jhdx7PzcZbl2WsnCWSgrcE0vFNeay+paopV++LcCfwl0N36fAX5eSvlSm2RTKNZkOWtuqlIniBYHUsv1gOF8dUWF7wURVTfghZE8JTfAMjU8P2KuUkcXu5sK78jeHp56bbaZ4nlkbw9Pnp0lZRqYuuDEaNxP8I6dOX40Wox97bu7OHpwYJHLZKLo8OjJqabiPHqof02XyuFd3QDU/QjLEHiBXPR4u1i4g6q6cYJe2jauWVfQZheOvZFoxaXzaeA3pJTfgWbl7H8E7muDXArFuljOmuvPJDC0lQOpS5lXaIWaz0i+RjphYkmBoWuEUvL6XJUXx4pNl9F9+3rIJi2Spr5owal5AbouiCL40UiRDttE9wWG0Ba5hrwg4u+Oj3NupoquC8JQUq0H/OL9e1ZVSjdv7+Qnbu3lyydmCPxY2b/v1t7Lsu7Xy8Id1MIF7YGb+vADec0WSV3PLqsroRWFn55X9hAXUgkhlv8GKRSbxHLW3Pvv3EZX2lhUgPXxo/uWte4X+u0zCR3bNLB0nb29aYQQzFRcnjw7y0CH3VxQnjw7y0N37QDADyJA4nghlq4RhhIvjEiYsYLRhSCXNJmteU3XUMnxOTlRoi9jNxeAkxMlSo5Pb0dixdfqBRFvvWmIW7d383q+xg1dKboyiWba4XqVWyvB7OUWNIB6EJG2jE3zjbcyLnLhsbmk2Va53mi0ovBfE0L8b8RuHYCfJQ7iKhRXleWsuZUKsJYy77efLLn4YWw1l1yPihcvFPv6Mpi6dkkAcHi2wvOvFxpujpCqWyNtm+zpTcW+/KkKMoK7b+jED+ViH7JY4YWs9PgCWYNIsqMrRWfaImMZFOsB52crPNeQZS03y2pVwcuxcAc1v6ABJAxt03zjrWQUbUT20fU8i7gVhf9PgN8BvkD80XwC+KV2CKVQtMpyAcj+nN1U9Ct9iXUheG26QiZh0p022deX4fRUmd09aXJJk7cd6OXJs7OLXEYgeeZcnqxtxo8lQuZqHkf29dDfsNrPz1Z4engOL5JES4qJbENnT0+KsbyLacZK9NBgB1l7dWs0aepcyNf44nC+UecOb9rZCUiyCWvVqtmposvxkVn+4Jun6bDNdQezF+6ggrpkT28KEMxWvU3xjbdSFbwRFcTXc7oqtJalkwc+3kZZFIq2sNqXOJSSvX1pJkt1Co5H0tJ58GA/779zGwMdsfJ+QNcWuYzu2d3N08P5ptXvBiHHR/L4YUSHbfDAgT72D2TZ1ZO5ZJGZl8XQdfwwYiCXYCBnc/TgwLpcFa/N1DA00Qwqn5mp0ttpM9CxcgrivFVf9Xzy1QBT18g0lP5awWy4dAcFm5el00qK5ZWmY15LLSfaRStZOgeA3wR2LzxPSvmOjRdLodgYlvsSP/rKJO+5eYhs0iRp6vRkEgxlk80AqhdFTWUPlyo8L4jwglnKrk/S0nnufB7b0NnRmcQPLwYyl8pRcnwefWWSbMKiZyDBUM6m4Pj8gzt2kLGNRccuVaheEPF6voqmwZ0NN5GpC16fq+H50YopiAt7/WRTBoVamZE5h1zSoh6EqwazF7J0B7VeBXqlC0MrKZZXmo65FfL3W3Hp/C3wZ8BnWP/wcsUWYa0v9+VUvW4ES7/Erh9wbDiP4120xh840Mejr0zi1CKSlraqtT1RdHjqtVkcL+ClsSLbu5K4Qcg9u7uphxEJQ8OtRbw0VuCF8QJ+AH4QomsamiZ4abzIfXt7SFo6HbZJzQ8JF1S7L5euCfD4qWkq9YDxgkvC0OhOJyi7Abah855bBnhmON/cgdy3r6fZF2dpr59tWYvRosfrhSoZ0+AT77yR/py94X7rK3GNLJVlpRTLpcddaTrmVsjfb0XhB1LKP22bJIo3LGt9uVsNFG4kC7/Epi547vVCbI13JZtphUf29oBsREzlpZHTiaLDl46Pc6HkMll0uXdfDwcGsgxlk8zW6uzrTfPCaBFdF1Sc2CL83ukpClWfvmyC6VKdbZ1J3nXLIGen4gDrAwf6LgnmLpeuWXI8sikz3hWkE9yzq5OvnpgkmzSwDJ2PPbiPvX0d7OhK4/ghxZrH11+cYKpSpz+T4MjenkW9fixDp8MSvPPAAPv6M7xlf/+G+63X2lWtpoBXkmVpUH6l464kHXMr5O+vqfCFEPNVHV8SQvwK8AiLe+nMtUk2xRuAtfyea7UPbrdsjh9y3764OKpcDnCDkPv29WJoGoYFc06dR1+ZwtIEXhiRNLRFLpmS6/PvvnGS77wyQ4TE8yMKNY+fu283HUmTUj2gJgMAokgyXnTpShtMl+q4fkR+0ieSklBK6kHI3bu6ePLsDKMFp7nDmFcoy6VrnhgvcduOTgY6dIIowgkkh3d1c9uOLJmEwXTFa1YFA/zuo6d49JXpZlD31GSJX3lwD5/6zjlKrk+lHnBoe45EQuf8XI0vPDtKLm2uGfRthfXsqpZbUNb6LC10b6113OXKfr3n76/Hwn8WkFxMGvufFzwniRuqKbYoa/k9l2sfvJ5A4ZWy1AI8sreHVMIgaerYRiPw6IXICF4YmeOxV6bwIrA0eMehAe6+oZPnXi9wIe/w1RcnydkG6YTJbNXj+ZEC7y3V6UxZRFKSS1scGMiSdzwk4Poh+WqFXMpCjyK8IGK26hFGkqRpcHhXN++5dZCsvcTaXSYtU9dFc4cSEbcw7kgaDOWSGLrGZNltvtcjs1W+dXKaVEJvFoV96+Q0H33gRh7+6BA/GsvzhWfH2Nmdbi4oL18oc9uO3KKg75xTZ7LsLopjtMJ6dlXLLSjr9aG329e+GYNfrhZrviop5R4p5d7Gv0t/mspeCPGu9oqquBZZa+D1wrGDwJpVrxvBQgtwfnD5U6/NkrVNjh7qX9R7/Y7tOb76wgR5N6LmReTdiL97/gJf+dEElq6hGxoC8COJRNCRMIik5NWpEiXX5+jBfmxDxw8lXSkrNoGQdKUtvCAkiCI6UyaZhEG+5lPzA44e6qc3k7hEqWRtk0ODHZRdn4LjUXZ9bh3K8u6bB6j5AYWaTxRJbuzLYOiX5sFPVlwkkoQeXzeha0gkkxWX/pzNXTu7sa3F/mhNA12j+fe7UHB4caTIt09O8cjzo0wUnZbf/4U97kcLDm5jZ2No2qq999c7PP1qDFm/XmjFh78W/zfwzYUPCCFs4nz9RONeD0sp/48NvKfiKrOW33O5sYMrVb1uFKtZgEu37N96aYx6FBvXQoCU8Ti3752ZIu90ckNXioSuUfdDXD2gHoQYQlL3QmbLdWYrdQ7v6uKbL00yXnLoSpkgTUbzDn4kGMjadNoJBnMW+3rTHBrK0p1OUHR88pU65+aqzQ6bjh/y47cN8e1XJyk7IR1JnffcPMRgLkl3OkHe8Xjr/h6eGc7HC5YbsKMnSaHq0Zm2GMjYJA2NmhdimRpO3UdD0mmbzWrcA/0dnJupNl0+t27L8Z5bBnny7CxzTp0XR4vcsbOToVySsuvztRMT/KM37VyURbQcS5urJS2Ddx8aJO96mLqGocVDYKLoYtxipaDro69MMlleHEBfbyB3I1CFV+tjuTrBOvAOKWVFCGEC3xNCfE1K+YMNvK/iKrOW33O9Va8bxXLZFkjww6jp777oD47PEdCwzuevYSAQvJ6v8dYDPXzz5DTluo/rR4gIvvriOF4If/vsCLmUwfB0DU0X1L0QyxQIoOZJ8lUPIYvUI/jC8+MIAW/Z203FC3js1Rl0TSAEPHCglyM39lN1A4IwwjZ1DBErz+XcU4+/OsnnnhomAmQkuf/GHu7c1c1b9vfyvdMzzJUdqj7kkga//BfHms/XvICiW0cTOrYpuHdvNzu70zyUTTJZdkEKhnJJ8jWPE2NF8jUPAbz31sF1VbdW3QCQBBG8Nl1hb1+aWj3kh69VMHQNS9f4+SO7mKvWVw4ULwmgrzeQuxFc74VX6x6AsuaFhHhOSnn3Ks+ngO8B/1RK+cPljlEDUK5/Nst6Wk4JpW3zki/x8HSFd/3B4/hR0+hFF/Cb7zrARMOCF0JwaKCDuZrHXz45DFp8nJQgIxBaXLHbm7UYy9eRQM6OlXXdDxFCIKWkp8Om5HhoQuC4Ibop0IRotCuQ/O/vv4UXx0oAzSyeUt0DKS5W9XohE0Wn2f45lTAYzzt4Ycivv/MmMrbB8EyFv/5hvBBlk1bj+Yjffu9Bjo8UiCLJm/d2IxB4UbSoqdsjz49i6RrHRwoIBBLJnTs6Fx239O85P2zENASPvzpNJGXDqtcIZITnx7uLexv3rPkhiMXVwTU/4Cdv28ZXXhxfFIxd7vW3a5jJG3lwynoHoLT9VQghdCHEcWAK+OZSZS+E+IgQ4pgQ4tj09HS7xVFcRSaKDo88P8qXXxi/bP8wrG9W6bwF+J6bB0nbOkO5VNOf//ip6ea5XZkE7zzUR9rUMPU4aHvr9ngq1Z07Orlle467dnZyYChLJmkgG8pdIDCEIADCCHRdwwtkY24tBJEkZRnIxv91XUMTAsswcPwQn9jHLoTA0AUhNNMxdb2RMWTpOF5jgVyh/XMkJZomqAcR3z0zw/GRAtOVOoYu6EwliKTEMjUQMF500HWBaWoYukZH0lzkT593lRQcn3zNI4gi9vWlV/W7L3Sf1YMIXRdIwAsjMrYRv88CrAX3dPwQx4sumambd7xLXHHLvf52zd+9lmb9touNdOkML/eglDIE7hRCdAKPCCFulVKeWPD8p4lbL3P48GE1L/c6ZaPK1lvZcluGhmlogFgxoyNp6rz94CD37u1jruZhCDg/55B3PGxD5323DzV76fSmEuhCEEkIQtmsPowAzw/pShtEMrb+DU1Q84Kmmyj2XWuEkYeGQEPiRzK28OOGmwxmbc5Mx8PPLV2j3Ah0m40A7fz71pOy0ICy45FMGHFAXMKOziSRhGItQBdxgDyVMGILW8K2XJKpUr15/eWCnYO5JP/oTTspOx6jeZdTUxVOXiizpze1ZnVrwoj7AomGzBU3iHcOfkQYyuY9k6YOQl5S4NSVtC5xxSUtDeTmFEOpwqslCCHu49LWCp9r/PsPVztXSlkQQjwGvBc4sdqxiuuPjUilu5xFY60v8cIAYL9hY2iCn7h9G5apEwYRF0outwxmeWmiRCgkN2/Lcmq8iNe4fsbSMDRJuS4pOQEJPbZmBZKqF9GTsajVfSIERcdDCMHBwTR+BKcmKnG7YSk5OJTBMDX29KYByWsz1aYPPIoiLhRD0rZB1Q3QNLh3XzfffnUGoxZLcscNObwoQheCW7ZnuXdvF//xu8OU6wGagKMH+wikbF5/tra4+dlSV5ttGujawrDc8q08L22uFl9/oQ/f0LRF91xYPbywOjiUslkzMf/40YMDlxzbrmIoVXi1ACHEXwL7gONcbK0ggc+tck4f4DeUfRJ4J3E2j2KLsRHW0+UsGst9iW8ZzHJ8NN8MIA/mkvzkbdvIOx6eH3LsfJ6nX5vh26/OYOmChKHzT966i3v29NKZ0PnU469xfraKoWls74yrO13f574be3nXoUFySYs/feIMhYqPYWh4XkjF9+nPJtndm+IDt29nNF/lz79/nnzNoydl8ZEH9nLjQLaZvfLwsyPcv683doF4IXOOy+EbuvjB8AxZK8HgTUluGszy2kyFbdkUO7tTi3oBPXTXDt60qzueuLWrmxsHsot6AeUdj66kRcY2Ltk13bo9i6YJ7r+xh1DGrZBHZmscH82zv7eDrkw80np+kehOJ5ZtrqYLQSjlJQ3X5v//k7dtI5SSYu3iiMj5oHQuZS2K8yy9/tKZwRU3WPSa1sNyYxuXvpbrSdlDaxb+YeAbkr4+AAAgAElEQVRm2VqUdwj4CyGEThwv+Bsp5ZdbEVBxfbAR1tNqi8ZqweCFCv3xVyf4lb9+jlBKNOAjb9vD2w8O8MTpacr1gFPjZTptg6+fmCBpCGzTJgoD/u1XXuGWbTkmSi4H+zPkqz66Jqg0gqgjeYeSG/L4qVl++vAO8lWfwU6bpGkwMlvl3HgNU9cJQ8l02eXEWImulEVXOs7df/Z8ntt2dMUDUlz/kkrV468XKTkhJ8YKpE2DkutzdqZKwhDMdvl4YchQV6r5vv7d8ZFmO4vPiOFmO4ulyn3eop7fNV0oOHzmiXOEMuKsZXL3DZ2cHCvw1ROTfPFHY1iGzkfftofdfRmeORe3aV7OtTZfZb0wM2u5lgjzIyMX7tqeem32kl3bfGbVci69qZLLZ78/jBOEJA2dn7n3hubiuVpPpPUE9a831p2lI4T4W+DjUsoL7RJGZelc/1xpls5yX3hgVb/+/Dn5isf/+93X6E4apBIm0+V6XBS0s5Ns0iRh6jz6ygQzlaB5ri0ADdwQLEMQBJKejEl/xmbO8ai6PkEEPWmLQ9tylByPkbxDNmnQl06wszvNM8NzBFHEgwf7CUOYqdRBSLblUk13ykTJ4dfedQBDCL57eoZj5+ewNI2bBjO8OlFB0wRv3tPNZ58cptCYngWQSRi8eW8PjhfyWz9+kJ50gkLV44N/9uSidhY1L+Tz/9O9fO/szCLlOluNs5C2dyYJwihuDOeH3Loty8kLZYo1nxfHC2zLJentsJkpu4wXHG7fEb9nd+/qwjb0RdksXhDx/z09zKefOEcEzdqLD9y585IsmJlqHU2D7blU8z0/M1nmpm0d3DKYW5TCu1wWzVzN46kz05TceJdXcX3cIOShN+0gYy3fxmG5zCK4mBn1RsnMWch6s3RasfB7gZeFEE+zuJfOBy5DPsUWpZWy9eUWh+V6sy9VAkvnx877/WeFB0j8KHYJpCwd1w8YL7oEkWQgZy9S9gCupOnATJs6pSBguuJj6hp+GGHqGkJI0raBhqTmhQgE23M2NU/y0ngB1w/Z3hkrnVRCwy9FLDS0ql7A+dka//25MUbmaty9q4tbhrL8/cuTvHShiK4J3n/7NiLiLCAJhJHEbgREdRH39Q8iuWo7i1PT5UtcYsKJA8qOFyKRuI33tT9r05+1eXZ4jnOzBt3pBPUgpOoFyEajlQ7b5MRYkSP7egjqcTbLXLXOl46P86nHzpIwNPo6EvhhxCcfPctdO7svub9WE8joYgD3O69M8NUTkyTNOK1zYaO95Vx6+RmP0YLLts4khgbT5ZCyG5DQRTMja6nyXnid6oKxjV4YkdrEsY1Xg1YU/r9slxAKxVJWy8ZZuGgUndj9YRpxJWfC0JrKxzK0RV/ugUwCXWjUvADT0AmjCE0TpCydCLiwIE1UI86+mUcHan7UfMyp++i6Tn+HzVS5jlsPCCXUvICEIbh/fy9np2pMlhzcIMILI85OV/D8iGzS5LbtOcYKLkIIXh4vIoCxgsNIvoYmIJkwuG1HjmLNJ4wkZ6erZJMmmiYY6rR5faZK2Q0IgSdOzbC3L0VXMvatL2xnsXCI+4G+DqZK9UUuMdvQm24VNwgJI8mN/RkMLc6oGexMogk4M1XB0OMZv7ah0ZGIVUcoJUUndtHpQsRtnP0AoUHK0im5AT0Zi3I9YLLiXuKSs02t6VYay9f46olJupMGXRn7kkZ7y7n0koaOrtGoZRAEURQPiNE0TF1QLgeUXJ/ezMVZwctlFsHKmUvXE+tewqSUjy/3007hFNcf68mhX64XzsLc+YUkTZ2qG/D4q9M8MzzH469OU3WDiyX+C77cuZTFW/d3U3F9Zisuc1WPB2/q44aeVFyN27i8BtiNatl5hAAZRc1i3FzSZHdfmht60rz/9kG8KOLMVBnHC7hvbzdSCrZ12gx12rxpZyeRlEwW40Zne3qSvOPgAAeHsgxkY0W0qzuWQROC12aqOF6ILjTSCYN79nRR9QIuFF16Mia9SQspBBGShKGha4JQCsaKNSpu0GxnUa0HjOVrVOsBv/LgHjJJkyN7e5iquBwfyTNVcXngQB839KR58+5uetMW//hwbA3P9xp616EBfmx3N0EUUfMCogj29KS5Y0cn02WH87NV8hWPBw70NXcZN3Sm0BDUwwiJpOz4aMBAxua+fT3NXkZzNY/9/Rm6UnGg9KZtHSRNjVw6QRBFpBIGoZQM56vN3d6RvT2LeiH9+G2DvG1/H+W6T75Wp+5L9vdnCKXkmy9P8vS5Wb54fGxRzcfCXj+zVY89vWn29KaYrXnUloyjbAfr+Q60i1aydO4F/gNwCLCIjZ6qlDLbJtkU1xnrzaFvPRtnaRzq4u8Lg8VzNY96IHnrgT6q9ZCqF1LxQvpTFq4fUPMEaVNQ9SU1f/E1AwnGghVgsuJzoeSRtXUODHQgJeiaQEr42okJ/HCccj0indAIIwiDiIStIxp56j0dSUwDbt+R4+svjvH1lybi6t1I0tthMZhLYuk+d9/QiRtEGEKQsDTu3NFNsVbn3FyNff1phjqShDLi6XN5/t3XX6EzafHzR3bxlv395KsBU5U6tqFRckK+/MI4py8UeeZ8ofne7OlJ8eUfjfInj52Li7iE4KNv282H3ryn2XFz/2CWO3d2kXd8QPLCaJEnz07z3TOzmLpgeLpKb4fJB+7cGVvXhs777xjki8cvEElJ0tB4y/5efjA81wzUjuZrPHxslGfPzzXbLdwymAMJ43mnOcJRE5DSdR55fnRRoDmbvJjF8zP37qK3w6bk+ghAE5KvnbjAyJxLLmnyjRMT5Ct1Pv7Om1Z1DW52BfjVCBC3ErQ9BnyIePLVYeDngf1Syv91o4RRQdurR7tbHrRStp6vePzXY6/Tm7boSicuOXahrI4fKzJDwFjRZXvOplgPuHNnJ7u70iQsvbkLODlZ5DNPnMP1Q0JJXIWqCzK2QU/KxtBhsuhybHiGWSe2viwNvIYh1pvSmK1FaEDSilMgJXHaYiphkLJ0xguxJRlEcYsGITScID5nR1eCohPgh5JfO7qfU1NVLpQcfnBmFkODhGXgB/Fi94f/+A5mah5IjRdHC9yxo5OhziSOFzJaqPHM8Cw9KZu0rfP4q1NoQuPooYHYenTr3Lmzi+5UYlFQ8k035PiTx15D12BPbwbHDyhUfUbmqqRsk7RlUPUCXD/iyx97C9u7U8v+3cbyNf7yqWHSCZ2UbeLUAxw/4uGP3keEbCq0mhsw2JlgolCnN5O4ONSlHvCj1/OkEya2qeH6EXPVOj/1pm184bkxnjw7i5TxrurBg33cvav7kjYMyw1pn88IcoOQf/bXz9ObNkklTFwvYK7m8amffRND61Cu7foutLN1QzuCtkgpzwgh9Eb17J8LIZ68bAkV1wytWB2X+2VYr9X+wkiez35/mHzNI+/4HNnTze6+THNs32TJWZSzfd++Ho6fn+NbJ6eQAtx6QDJh0N9h43ghD+zvpTNt8dJYkVLd5+XxMrapYeoaNT/C0CTdqQTlXIihwVSpjqnHHnxLjwOK8z12KvVY82saaJrWsEJDal5Ese5d8poDQNeipt+04oVoQsPUI7796hSzFY+i6xMChhAYGuiGTs0P+eFwnpsGshza1gFCNhXVRLHG3780gRDw/OtFsgmdohtw/77uZmxjsuxSdgO2d6YWBSWnK17cQC6E0YKDqQkKjk8oJWkrVgVpy6Dm1Tk1XWZ7d2rZdNpdfSkCKamH4FY9BIJ6EDKcr3LP7p5FlrPjh/yXHwxzfLTQnHiWMDTmanUKjk8kYaLkcG66wg/OzeAHkpsGO+hKJ8jZBkNdSRwvWnVI+7dPTvDH3znb8N9rfPCubWgCrMbcA0PXiCS43totEtppgV8LM3NbUfg1IYQFHBdC/B5wAWhfU3PFptBK9erSL8Mtg1lqYbiuDpjr6WBZcQP+7PGzFGpxSb5taJyeqvAP7trOk2dncf3oEmv36y9O8NgrU4SRRAioh+DXAgY74mDs916bIQwiJkp1NMCNoB5EZG0DXUjCMMINQqZLLrt70wRR1CwqDcL4mvN74JsGOnh5okwk40PCMD7WX2GTHPfRufi7LiVOGKEhGS+4dNgG3SmL2fL8cBSdihuga4JbhnKkLIOTF8oYmqDs+PhhyDdeniRhaNiWzs2DHVS8ML5GNWAgG+8ebFPQYRuXBCW7UgauH6JrgpSpU60HsRuHOFNo3sLXGgHeeZa6P6ZLLl4oESIgaRo4foAXSrZnFwfVvSCiVg84PVUmZyfoTJpU3Hgub6Hmk7MtTEPj7GSFCLA0QSUIOTFW5MP37CKSMDJb485duRUL9vIVjz/+zlk6LINM0qTi+PzNs2P0ZyzKrt90DW3vTNLXsfpndKPaf6zEtdC6oZVX8XON4z8GVIGdwE+1QyjF5rHehlFLA6kvjOT5uT9/ml/7/PN88M+e5OFj51e9z7ylWKp7DM9WGc3XqNZ9vvHSBI88P8rIXJWzM2VGCg4pS8fSNTIJk8lKnW+dnCJlGnSm4iyVM9MVgjCK/cdzVeqhpDudaBTOABo4QUDGNnHqEeOFOpoAvWHexPpPkLQMbNNgV3cKNwgZnqkwV60zV22MLGweC7mEhhfF7YZNHWxTxzQ0Oqy1v6ym1gh4BZIgjOhNm1S9uLd+fzbBnr4UgYSZUh3XD3nf7YP0ZGJ3jOOH7Mgl+c7pKb74o3HGiw79HQmCUJJJmuia4NadOUbmqnz39DQvjOV5/+3beM8tg4uCkju7kkxXA/b0JkkYGlPlOvUw4uhNfXzkgb24fsR0JR7L+NG37SaTNFcMKmaSJvfu6aTihkyWXSpuyL17OskkzeZn5dRkic8//Tr/7blRqm5AtR4PdZFI9vWnuXtXF34UMZqvEQFpS8c0DWxTww8lr06WqHkBO7uT/Niu7ubnplT3FgVVL5Qdgihq3juTNImQvP+OIQayceHbQNbm54/cQN7xqLjBsq8J2t88bWGweD7ovNmtG9Zt4UspzzfaIwxJKX+njTIpNpH1Wh0LvwzFmsc3XprC0gX9WRvXC9c/p1bGftxTUyXu3tnNQIfNhYLDH33rNLu6U8yU6zhuiG3pBI22wULMf/kikqaO64dMlV1eGisxUXLwwwjXD7AtvRH4BEvTqbg+QotdK0HIxYYgQN3zqdbBNjROjhep+XFOfcW7VMkZQLEeUZmMrfsD/Unec+s2zk/XmKm6XCjnV3y5BrC3L8WhgSwBgsl8lRMXyvihpFDzKToeYSgxtTgVUzZaC+drHs+dz1PzAr57eppCLXad1OoBx87nSRgalXqAbWg8U4+brCUtHUPTeO58gbfs729a5SXHiyuJ3ZCetE130kYKSBk6O3vS/OL9e3norhs4NV2myzZ5daoSx0VWKGz7sd1dpBMWP3HbEIGUGEIQNdw1E0WHR1+Z5IlT0+SrPttySQpOQMLUOXJjb2NOQLzQHRrKMVFw+NR3zqA10jr9ICKSsU9+dM5hT28K29RBgmXqlwyZH+pIYmgaFcdvWviGpvHW/QO849AQecdjbK7Kw8+O4YURlq7x02/eyb7+SytxN8MCv9ozc9d9NyHE+4n76Hy98fudQoi/a5dgis1hvVbHwi/DZKVOKCNs00ATgoxtNtPnVmJ+h5C1TQY7bUxd58x0BdcLODMdV5L25xJ0p0wKjkc9CPGCkJ6MhW3Of/k0buzP4AcR3z0zzbnZKl2pBIO5JMWaT8nxSWiCXEpHypBK3edg/wpeR01gmRpJS1ByI/wQ5JIGYfO/zduE6YRG2hQMzzqcGCtiWzrvvW2ILnvlr1Ha1tE1wcsTFabKNV4YL6MJ6E5bGALmKgFFJ6TDNtjVk6YnbfHVExM8enICgFu3ZRmeqVL3IwZyNlnbpFDzcf0wDhhbGpNlD1OLdyxJU+Px01OcGC3gBfEC+eTZWbIJi93dKSIkp6YrzDke42WXkhO/uu3dKe7f18erU5VF6bCPnpzi0VcmFz32xKkZdvWkSFo6KUsnaens7cvg+rELRESCYi2gI2FScn329qYYzTtMl+p4YcTRQ/0cPThAJCXZlMXN27NIKSnU6vihpDttILTYLVSqh5ybqTKSd9jRmSRrm0vaW1t87MF9lL2AkXyVshfwsQf30ZWJe+p0JS0efnaMTMLkhu40mhD87lde4eFnRxa16F4p7fNyLPC10i4tQyOXNDdd2UPrhVf3AI8BSCmPCyF2b7hEik1nPVbHwuCd1nCJGCJO41vPnNqFO4SFlnrFC5rVnaaucWAwSypRI2ublOtxNWzV9bkQxtWslqHxC/ft5tPffY3bdqRJmnF2zI9GC9y1q5OupIWmQaHmM1Px4uIqFhdRCaAzaZJNmcxVPKQISeiCpKlRXRDYmw/WQpxxk7FMDF1jquRQdHwGsklmqx65lEW57hLKSxNE60FIEEQEUlCrx5awaRhYps727jSTRSc2WoVgthq3ZzY0jf6OBPft62Om6qJpAqHFiiRtGwRSsr8vzd7eDiZKLnOVWUaLLhfKdaJQYhgaf/zYGbpTFv/w7m3N973keJScgKGczY0DHVi6YKzoNAuTlnNpTJZdgEVBU+HE1bs7ulJomiCKJF4jnhGfH3fHnO/rnzANbuhO8e5bB9jembqkIdo7Dvbx9RcnODtT4cWRIrftyDFScMjZBhOlOkEoSZhixUrYdxwa5K6d3VwoOwx1JJvN3YC4IV6jN38URXFbC+KisPn6jvnCs5XSPlvhaqddrkUrryaQUhbbJoliVdpRrLHwmuuxOuYXhg/fu4vffu9+/CiuTq15Ib/y4B4Slr6ifPM7hLLrUw8i9vSmCCNJ1b9Y3ZkyDWxD54auND2ZBDf159jWmWJnV5q0rfOemwd56K4d9GdtBLGiqQchKUtnV3eKd988SGfaYnd3BhB0pywMoaEJSOjxFKqEHn/o/TBiquhQ9eJMESdoKK0FRFy08uN2ApJCtY4gbhh2YqzAF58f4/ycCxLSF/VM8zw3gLmqh21obMvaJEwNIcALQkq1OiAJg4ggiDC0eIGoN9Ilxwo1Om2LbKOnfRBGVN0QQknZDTk5UUJGcVtmJJiaIJBQ9yO25ZIkLZ2//uEorh9SdnwcL8QPJYaukUnE7i8vuDiRarnh4ElTJ2lplwwMf/PubkpuwHS5Ts0LeeBAH9lG87EwkvR3JCjUPCpOQLUesK8/07zfUgaySX7h/j184ugBDgxlMHWdhK5RdUMsozEgZkE/fZD4QbToWmnbYHtnivSCTpleEKE3BtUUaz4VL6Di+Vi6QBdg6gI3CHn0lalFO5gnz85elrJvpWDwatGKhX9CCPHTgC6E2A98HFBpmZtAO6yGVq+5MB0zlzT50D17eMdNQwznq6R0nZcmSnz5hXFAcs/ubnb1ZIDFxSyHBjv43FPnm77Uh+7YRlc2wVv29HJsZI7RgsPObhs3iDg9WcVKa9y6LUdH0qRU9HGCEKcU8MTpaUpunRdHC2RTBn4oicKIzz45zGylzjsPDVB2faaKdWYdj53dNsOzLn4YEgG5hGCmGlxijVfqIRoXC6xCCZYpCMK4/8548WLq5Q+HC4vODYDKgszMhdcuuiEIl719GbpsnZGif8n7G9RD3MAhaBT8fu2lCb5yYoK7dnTwpt2djMy5uEFEwtCwNMlczcP1Q3K2gaHFC1Ao492MacBstU7dj5ip1ulOGTxb9dEaC013ymRkzuFCyaEnbfGdU5McPTjAYC4ZDxE/OcVk2cXUNY7s68Y2dJ56Le5RX3V9QPDYqSnOTFbY0Z0iaV2sjbixL83nnx4hX61zoVBjKJdkquwyW3E5M1Vp+tAztsn3Ts9Q9UKytsl7bhlgqDPJL923h889dR5dg4IT8OY93XSlLNwgdv1FMsLUdP778bHmkHNYHGM4srcHNwib3TyzKYOnz80igULF48BgBy+MlQhDyVAuQS6VwNTjgTWWruE6cTB6oMNuS+rx1aQVhf+rwL8gbpz2eeAbwL9uh1CKi7QjVazVa660OPTnbDrTVrOYxPUDnnu9yLHhPPv7MxiaaLacPbyri2dfL/DmPd0kTJ3h6Qq//+hptuVsIinZ1ZOiJ2ODFJia1lBOsWV0oeDw4miRMJC8fKHELduydCRMhKgxW65TrQd0JE08P6LsBnzjxAUqdZ/xQh0aM2NtA/o7EkyV69Q8eYmyB0hbGn4Q4S4I7joLci4Tepz2uRKGtjgNc54wgrlqwOnJMheWUfYQx5PDBdcWxAvOsZEy01Wfvkycwjier+JF8TUjYLbsxWmhoWy6rmQAcxWv2VOm7IZkbIN79/QwMlfhO6/O4IcuSVPn8A1dZBNW8+8f31xScnxG5+JMqp5Mgvv29ZC0DL5xYoKUqXN8tEA2aVF0fLrTJp989DSHtmc5OVbi4FAHXhjhRfHfcGKmSl+HzcGhFLOVOv/qyydJWzpjeZfOVLyrnCm7/OrRA9y+s4t/2ddB3vHImAaaLjgzWeKvfnieWj1iNF9lW2eSrnQCAZRqPtmk1Zx5e6Ho8AffepUogpRlcPuOHIWqj20abM/ZDGs1ik7A9kbBqalr1Oo+j18ooeuCiuPHOz0psE2tJePqSoK+mzXruZVeOjUp5b+QUv6YlPJw4/9u2yRTAO1JFVvtmktdR2ttU+evZeqCE+MlOhpfvLPTVc7N1OhJWXhBxB996zTPDs9xYrxEyfX5wbk5IimpBxETRZdnhvOkTJ3h2RrjBZe37u9F1wTfPTvD86/nuWNnJz0dCTRNcPJCmdG8QzJhoGsagQTXjzB0QW/a4nze4fxcvVEFG89YdQPo6UjgRXGbhOWwdbFI2S9lNWUPyyt7QUMJA7mUxmqXWNi6wWtU6gLMlF1OTlQo1AKqAfgL7uPL5WdRnZkq8epUGdsUXGhkMiFgrhawqyfF/oEObt3eyWszNQRxD5yS6zf/1kUnVqSTpTqWpvHk2dn4RQjiilkpydjx7urViQq6JkibcYD61GSFiaJLZ9IiYepomqDiBjiez0ylThBGvD7rkE7oRFKSSuh878wMM+XYv56xDXZ2pejKWOhC8PmnR8jZCbZ3Jim7IcdHiowXHS4UHJ45n6dSD5qxoTNTFaIIhBCkEjrPjxa4UHRJWTpdaavptrx5W5YHbuojlTDxG7m3kZSMF10sQ2OgI9GyS+Zy0y43atbzeljTwl8rE0e1R24v7UgVW+maJcfj6ydmF1nySctYdZs6f61io2IT4rTIhKGjaVDzg3grb+iYejz8+/nXCwzPVJgse2giVpSdSZ3vn51htlJnqDNJ0jJ44KY+Tk9VMHWNoVyyGeydK9cpOj7phEHa1pmrQdn1GZmrUakHWBqYOmRtgyBstBQW4HjRokDsUmpXmG+dMQW1QDatc5bcK2nGTv6VZLANQWXBjiJq/DcIAU2Sr170GUniBSJoKHxDg86URRhF1LyQzlSCwayNoWsEUchY3iEIYyuyI2lSdQPOTJepeQF1P+TgUAfIOOhqm7FC70zGGVO6Hs8BmB92Eoay0Y0zLtwKoziYmrEMbFOnUPOIZFxzYOkahibwogg/invNG5pANCph/ShEI54T7IaXvv8Lg64VN87eEsTxCg2NmUocP3G8kAjJXNVjuuwSSclcpU5H0mgEjvVmVlkEpAwDP5BEUpJLmxwY7KDg+CAFmkbT7bhct83VaDXtst3FXktZzxWPADuA7wL/D/D7S34UbaQdxRrLXfPI3ouTjxZa8roQlwTy5hecqaLL8dE8twxmCWREtR5Qdn3u2JlDShn3mpHxLiCd0Ll9RxZJ/EWcKHmYjRa6Asg7IRlbRxOCsbyDLsAPJN0piw57cVqmBGxLpx5EpEwTXcTKMV/zqdYDOm2TlKnFFreIe7IQQTqhI1ZpHXWl2da6rqE3AsRm488zHxOwdEgbcdrkSkRi8XMSsLXGjkRKutLmImt+flER8dOkLD2e5CUEO7qSmIZGzQsZyNp0py3maj5RJNnXm6YeRFwoOOSrPq/na5ScALuxeEfRRYWuN1xihibI2iYPHOjDiyIGsgkqdZ/tnTZSwo19GWzL4Ma+DJoGXam4B39vxmJfb4akqTFVcnH9iLcd6KUvY1F1A3xfUvMCtnfa9GcureHoSlpYjYHomqZhGfFy6YcSPwwZzNm89UBvXGRWrjNWcNjZleLmoRxBBKN5h75Mgq6kSc0P6Eqa9GcSlOsBNT/g6MF+bEPHD+IFbv5zW3UDHj81zUvjRb5xYqIlq7uVtMt2F3stZT0+/EHgXcCHgZ8GvgJ8Xkr50lonCiF2Es+8HSTe2X5aSvlHly/u1qQdxRpLr7lSwCmUctnRhAtH6OlC8CsP7vn/2XuzYMmu7Dzv2/vMOeed760RNWEqoAH0DPREoptwk1TLpMmQGGF10DbNsEK2pWCEX+QIm3L4wQ6HFRYtRdikRZktiwyFLFmiyCZbFHpkA42h0RgKVUDN453z5pxnPtsPOzMr73yrgEIV0PW/AJXTOZl3n7XXWetf/8/f/NJRXrlUJ1UMzaw7YUqWKY5NFpgserimSZylOCYY0iBOFYYEqbSOzb6KRztMWGyFFF1zk+G1bUr+y2eP8YcvXeHU9RZJliEETBVt5soea90IzzH52QMVvnN2lVRpds7+qh7gKXgGQZjib3E9HZwscHahQ7TNprCR3jn6+PFplyPjZa43uqQZtINkSGnM2SaHxz0enCvRTRLm6z4XVnsotDhbX6KHLMvwTMFc0UJJSa0b4dkW9W6EY+iyVdU1aAZa9yfOYKqgxcFSIWkFCVGSMZmz8PtKkzMll88dnyRKMp47OYMfJXzr7SVuNHxmyy4Hx/PkbIMbTZ8gSYd/6+mSOzQhj7JsmGSMrpuBZ23L1560S+0A25L81lceJIhTXri4SpxAlCSc3F8Z3to4puBTD4zx4qU1qp5FNWfz688c3tKLtuCafP2zh/jGi1cIkpSyZ9ggcMwAACAASURBVDMz4zKed0DB0ak8x6aKHJsqstQO6EYJq52IRCmOTOYZy1s8d3KG16829VqfMfjCiYl1tEvblOtM2NMs4+Ura7imwdNHJ3BN445l3R+03MKe1TIBhBAOOvD/L8D/oJT633d5/Sx6Mvc1IUQR+DHwHyqlTm/1+vtqmXcPAyU/W8pNZtgbFSoHFnqeJfEcc51aomXIIR8675rDSc9RwbMTkwX+02+8qjNhU0/uRknGLz+1n2NTBQxD8oUTkwgFy92Qw9U8lbyNH6f9Kd9Fvn1mkU6YUMnZXFrtMl1yeXi2RLMb8eqVNfaPeSAUB6t5ntxf4bvnV7lRD1jrhhwoebx6owXcLK8YwOdOjPPuQpPF9s3x+5INjmVS78stbBzMf2DMw3MN0iQiiAUPTRVoxAkXFrsUXMl0Kc90ySFR8Nc/eYAsTfkH3znHG1fbKCBv6fJTGMMnj4zx9LFJ/DBlzY84UPVo+DGn51u8s9DGMgRFz+LRmSIPzZaYzBnUw4zDY3nenG9xpdZlqeWTsy2iJKXRi5ksuXzhxMSQhQPwznyT//GbZzg0nqOac4iSjJVOwH/7848wUXSGhuCOlIRZprPsvpnMXkzKR43FlzsBPzi7yljuptrlWi/i8ycmcKSkGcabuPNbYXBOUZzyo0s1/CgbsnRmyh5RktEK4mFTeeMa3ur8Nq7/wfMtP+Zfv36D/VU9xQuw1A74xcfnKPclHN5PvB8svL2qZe4p4PcD/S+gg/1h4I+B31dK3bjFk/o3wD9USv3FVs/fD/h3F29eq6+jTX79s4d4/EB10+tevlzjv/pnr+FaJgqFQBDECX/7y8d57Upjy/dvZCH8X98/N9Rg7/nauckwQGTw+RMTVHIW3353FdsQOKbB3/rSEZ59dJbf+Yt3+O7Zmja7SDIKtoljClIFMxWPa6tterG2ANzYnB3UvLeCAcxVXK41NvMQBrmW4maGv122v91nOwYcmCiwUO/gb2i8DlDxTK34CBimJIgyZssuyx2fdqDvhDT7RFBwTJbbMZah69mHJvJMFl0urXaIkwzTMJACnjxY4b957mHmqjqAfPvMIv/w2+eZb/pEScaxqSKHxj2OT5X49WceYK0b8r2zK9Q60TDDN6UABHnXHBp+J5ka0jKny866DQVuBrF2kPD2fJOnj05QzdnUexEvXFjlQNVjvqGpquMF+5aC3Ma1NJBz8KOMKEkwpUHeNW87eN5JGeO9fqdbxfsmjyyE+APgJPBnwN9TSp265bPRn3MYeBJ46Xbefx93Fp0g0bTJI+PYpiTLFGcW2zw0W960APeVPKJUIWU6VFgM45QfvLvKdNnrN9gSvvHiFX57skihPyE7uFNo+jFff/ooXz25jxcvLPN3/9XbZAqUFtDku++uUvZMXNvANgwMFP/Tt85ycaXHv31riWrOIlO6gdiLU2yrb9QdRDQDpemTWwT2nXKbFFjaItgPntuIWxmlSYFeCqvtLq3NKspDSKHwI12KKZsS0xDMN316scIQWrit3k3oRIpupOmdcQpxqji90OFJw2ChoVkmB8dtap2Y755d4eG5Ml/72ByOYWhlScfiQAXOLLV583qTbpTy1ZOzgC6d2YZkqRVQcCzmGwFxoq0gnzk2wanrzeFk7ULT52q9x+HxAt0g4defObLJR7jsWlxY7vDalTrPHJvgtSt1LClZ68ZaHK8VMFt2b6lkMmpxGSUZf/z6DS6t9oZZ/cExj+cemaF0m/IFW0lC32mRs1vxen4v2EsN/2+g1TFPAP+1EMO2ke4V7cHxSghRAP4l8HeUUq0Nz/0m8JsABw8e3PuZ3yV8UHzZDxKLTZ8/P7XIT67WqeZsTu4rU83ZLLWDLYdGCp7Fzz44wffO1lhLIiSCzxwdA6FNusMkJedI1nqZ5lO7JlGScaXW4eXLa4AYDshEaUakdJNTCM1ISQE/yajktQF21m9a9pJkyMgwpERlunHXCxMc0yBT+jy3o1bu1AbTTklsT+F5H1Dr7fzhSglsU5JmGQrBeMFhpR0gSXUwG4z+9iUcTKn5+IMrshMlWIYkSRWr7RDHMrXfq9KmJI/MlkiyDNc2OL/aYSzv0I0SHhjL8c23Fnl4tryJpbPcDhB9Kmanr60fp5keguoH/huNHu0w5pc/vrVEw1MHK7xwscalWpcgSXlyf4WzKx0KrknDj5BSkMRqy7W2G1pBzJnFNpMFd7jZnF3ugGDbz9rLNXy3Rc7uFHYN+Eqp9/RNhRAWOtj/M6XUv9ri838X+F3QJZ33cqw7jfej1navbRiDbKzs6uaZQHDqRpMnDlQ26dUP4FkGnzoywVMHx2iGCWXHJFHw4vlVXr/aGGqQV3I2Vc/Wt9xnlnm13wgb2Pb9r996l6QvZ5BluqQzyJzTTLHaDsi7JmmaYRiaoVN0DbpRQppl+nGpNVZkloEyd8y8d4rnarcXcGtlnNtB1bO50WeDWFLgRzGWFH3tfQWWGlI1QdNZBzx/0H/LJFNIoSh6FrZhECUps0WXbpxS9ixMKVnr0yYNBZaQzFT6RuxJuomlYxt6GC1NFQXbJE0V7SBmpRVQzll4loHTZ+BsJdHg2ZoO+YnDVX7mxDTfObtEzjTXsYCy/vV0W43KHf+gm3Er1/AHlXV/kLij30bo24F/DJxRSv39O3msrbCV/sztatLcjk7GxmPd6oDF6PsH/98Jkj2f/27fNUoyltoBQaK52SfnNG2y3ou4trZZr35Ud+eLJyZBCGxDp+afOzY+lEVo+wlxkrGvanOj3uNbpxdAaNnfnG3yxo0mL5xb4fXrDa7WfR3QgIFumQSmiw69MGWtHeJHMc89MsW+Sp6vnpzBswzc/oU4lrNIEp3p1vwd6iX0M/htYAHC2OEF3NlgLwA/0dTUibxNprR8csmzmC7Z2FIbshhAxZW4/dg4iGueoTfJoqdprs0gptUL+diBCt0wIcm0ZtF//rlDBIm+K+pFKR8/VCFJwTYkkwV3SNctexYtP2Ku4nJ0Ks/BsRyLrYBmL+D7Z5dZbkecXerSCROyVDFVcrBHKKdPHqzQCuIh7ffZh6aZq3o8+9D0kNbZDEIqOWuoxXM7wbXkWTw8U6IdaL39dhDz8EyJUl/Pf/T6We2EPH9m+Z7WurnTuCWLw9vAM+iS0FtCiNf7j/1dpdQ37/Bxt9zJYbPmRjlnb8sy2Oideis6GRuP//TRmzz3W5UzGG2UXVzp7qnRtVsmM3heu0g1kUowW/F4Yn+FWk/b/JVcbTiy1gn5B//+HI/tr2g99Aeq+jfrk9pbfsy/ef0G55c7QEbJszGE4i/P1Ti71GW5FXCgmmOtF+OaJgXH4O2FFnnbZH81Ry9KuLDawxS6zn5yTnPtl9u6yZozNRPocq3LwfEC/9tfe5J/8sOLvHm9RZBofZwkhWxgQQgUbNbVywclm3SbzC8G4m2sqzYm/nci07cF/MyJSephxI8v1OilCkMp9lUdnjwwzoWVDtWcxXzDx+mvx9VOiCkEcZYy3whoBjFZmjKWd7AMybW6zzffmudbp+Y5PFHgB2dXkQg+dbDKQ5N5Ti916MUZTT/iP+nTIjuhHj4qeRYfO1gdaun8+zNLXF/raX9coZlFSQbX13qcnCtzcl+JkmutW3eIm7pKG83DL9c65B09qbvjcMRuv5sp+doTczx/Znl4rT778NSw+Ty4fpI0IwPOLrX5/PFJvL6s872mdXOncUcDvlLqL9l68vuOYqvptefPLINQQzPkhabP7zx/jscOlAmjlAELYbvN4emj43vmy253fCEE4/ndN4zR91umGDbKLFOua3Q9/84Szz0yu6k5tdv03ujz43nN5njjWoNMKFzT4IsnJvmL08ucut6k7sfUuxFzFQ9QnLrR4qVLNUwhePJglf1Vjz97c4FrDV/f1ieKmhXRDBNEpii4NoutkMVmwLHpIvPNHoK+omLJHZ6vJTXnuuUnvDXfWRdQ672EPz21SClvc2A8h2HAK5cb+HEC/clJ0JOqSaTIYFNzVLE1M2Yv2BiO7kQ+GCo4t9Ti7cWOHs7KUjop/PBCg5cvNXhkrsiXH3mA2qlFTi+0yDlaWfTkXJHn31nGtQwMKal1UmrdaDiNqpQiSOD0QodemNIOE240evz1Tx0gQ9IOY07uLzNVctd5FkyXtCfwK5fXuL7W48dXGjT9iG4CnjmwUEwIEj0Q9rUn9gFsWnevXW0MhfRG8ZOrDcZyzvsyXTpT9vjVTxxYRxsdsGwsU/DKxRrzTW1hudgM+OG5Ff6Dx2aJk/dQSvqQ4k5n+HcFu+l6DzQ3DCko2ibvzLcB+OKDk8SJ2rQ5+FHKCxdqQ93s3Tr3Wx1f+JBlak8bxuj7BybUmdINskrOpuFHtIKY16818KOMomuuy+B3uxvZ+Pxs2SNT8LMPTTFd1Bf+82cWeWehrT1bk4x6L6ToWhT7ZidRoht3tiVYagcYUmBIiWmkNMOEIEgwLUm9Fw5H2EExU3IRAizpEqUZtU7IUjvAtQxmSi6NXmfL8muYwg/OrlJ0Lf7t6/M0/RhDCuRIdpgqPdG6m+bN+4X3u8e70ou1dHKW0RnRWEszOD3f5vpaj7PLbZZaIYYRIRV0w4gkTcnZNkmaIaUkU4os08bkQawlBwTaeN2xDBabIa9cajBT9vAcg4KjSxvPPjS9ad1cW4v54fkaY3kbgY2gS5QqCgIcyyBTGX/l8TltQuPHe7oLvhOqkqP19tHzaPkxi+1QT7+6FscmC5xZbHFltUclb33gFoN3Gx/Jb7qbrnfYL9W4/WBrGALDEIRJpoNxnPYHO9aPO5dzNr/05H5+8fE5funJ/duWU7Y6vmsaPPvQ1J4kEkbfPzShVgxHzFFwer6Faxrsr3qbapFbHX90c9ny/CzJdN/k+cJSm7OLbay+Vo5hSObrPp0gJu13DQcGJkmqS02G0PooWaYbjAnoqUpDIqUup8wUPAwp6IQpRycLFD0D09AbxXTJ1qbhO/xdXVsfc7HhA3rT8UctCZUuCdkf0D3lxmC/22F3e/74RB6BGDY/BzD6KpzfP7fClZqPQOGZOrBfWvGJYoVtwFjRwZYCA/07dMOEKFPD+QHXkn2dn4zB/I0hBGXP6pdg2LQuMnTjfqUd0goTHEPPPHSChDDOeGSuyEOzZWD3dTf8Pn0rw7Yf7/i628W68+ibskghtA+Aaw3NWHa6hj+q+Ehm+FvxaEdH9IM4G4775/rMAwDHlMPNAbG3bHyvxx9k4DNlb1eWzuj7B+PeozX8uYrHtXqPp49OYEqJaTPMkEBnUE8fHef751ZYat+cSBwcb/D533p7idZaTMm1+JkHJ7lc6/DKpToXVtokCkqWxDAMHEOw0ok4v9zm6lqP/WMeEu10dXbJoOSaOKbBu4st6n4yrHGHidbXMfuOTe8utym5NiemCpzcX2Gm7HFowmPpm10avZhU7VwusQ1BL9WjXltl8XF/M6rkTFa625tV3ynslu3v9LxngG2bPDhT4O1rN32GHENz7UE3ZRX6383eze+3r+Kw0k3I9bPavDSpdcJ1v5ElIUhSpBCM5y2iTNHoRXzygTHiRA1PbvQuNkkVj+0r8c0354kThW1JJos29W7Eseki00WXv/Wzx4aSCIN19fw7S1uuO7jZO+pFKaduNDkyqc1u3mumvZH9Nrh+gjhjqmBjSV2+SlPFo3Oldc5bHzTuJlPvIxnwYXse7ait2gsXatR6EQ9M5IC+xdyGzWE0YI82gnajdG13/L1SvTa+H26OswdJyrdOLeKa/cf7G1KzF/Hnfau2bqBva21zs/EzwHIr4I2rdfwkRWWKpabPQivANQ2OTRawBKx2Yzw7oxckJEq7W4WJotsPBivtkNMLLdI0Y6Lk0uoHe6uv8xJn4JomkZWx0oroRD1M6XNk0qPWCfk3r98gZxu0g5R2kDAy47GpXGIIuFrrUe8l224Kg8T4bgT7vWDQlC44ktlqjizNSJXi+ESetxc7XFztEAQxEyWHxWY4NF/PANeEMM20SfuGz22GGQeqDo/tr/La1Rqn5nvD5wYCdRLFXMXFMw2mSy69ONX0ynbYd6nK+Nc/0aYiXzg+ybW1Hv/85Wv84FxClKY4poFtin6/SFDOWbj2Nut4sN42rLt1vaNph9myS8OP+YXH5rbU0dkrtiMojF7r3z+3sk6O4W4F+7ttgXhLWjp3Gh+0tMLoTgs7s3SAD3zceifsxAKyDMH3zq4AN/sSo+faCRJ++49PUXC0dv0b1xoEccLx6SJlz9asmeU2r19vIJXCT6CaM7FNrWYZJim9MMYwDA5N5FlthbR9nVE6JggpSZOMMIVPHi7z1o02nql1d9JMEaUpOdtkqRlgCPBTLS18YrrMtUaXOM0whSBDkabapSnJwHMMWjsJ1t/j2F9xaPm6LPbFE1O0gpimH6OE4MHpAjnL4E/eWiBKUnJ2XxQty7AMSTXvMF1yNjltAewr2/TijK8+Os0fvrJZ7eTIeI4oTfmff+Vx/t9Xr/G9szUQoDLF546Oc3y2yEIjHE6qzlYcTs+3afkxpiG5Xu+Rd0x+8WOzw0TjiQMVelFGJ4z57a+dHA7X7XSNNP2YP3lzflg6hPeuUbNXGYStsuoPOtO+k5IN75u0wkcZG7PtjT/6do0geP/ty2518e2kdtnrN3pBl1XyG4yfRzXGw/5jUSp0HRetBV7KWTw6W0ZKxdmlHkXXpBsm5B2TbtSf9DR0Hdm2JEYkkVlGkkGWZn2WiDYLt0yBbWm5Bvq69I1uQME1MA1J2I3pxIobzR5xqghizfXPWQaOLWkGGQXTwJEf7oCfZnowKkkVl9d65B0DzzIo9hUjr9S6hHGKUIowSSg4Bt1YMF6wsQzJSt+AeyM6UYZrSC6sdACt3TP6K3XjhLGcTRIrvvtujVz/uL0o4btnV+jGKcemSkMG15vXWsw3exyo5jENyf5qjosrHZaaAWkGD04XkVJScCVrvZC6H2GbUs90xNm2TLRRX2MpxZAfb4jbb7rstQG88VrfKdPe6+Zwq9fsvWCB+FMd8G8Fd1LG9HZv8zYu4sH52Ybc1JcYnOt6Y+cIy5QEcYIUgicPVHh7vkUvSlhphRydLFDybN5d6LDQ9DENSSuIsQyJUjqw2IaeqjUNSU5AI1hfcHnhwirtCJob9IgFaGaPuFm3X2prLqUlIUlSVsJ0aJBiy4Tp4oebPrfQukm9ubTaJUn1AJLZ9PnJVZ259wazAElGK9AetTMliWebOJkCNg/rRVFCO4VOf/Bs45Y4XXDZP+ah+ToKx5DEiXY3i9KMq2s+s+UctqkVK6UAMkGUpJiGxJKCiYLD1z42R70XMWitDyZxozjlX/z4Ku0w4ex8Gyk082vjuvPjlONTef7wpWu0/JjVbsSnD1f507fmb7u0cTvX5U605a3KtsCeHtvt/D9oKeStcD/g7xF3SlDp/XS8efJgZWjcvLEvsbEHUcqZvHq5juyP0n/icBUhBSf3l3h4psQPz9dY7YS0wxjHksT9hR0nmjVyeLLAciNkpR1qVcb9FZ4/u7rpnJJtEnKJDhiOfXOxDzxj4wykuEm5lP1NIUnvzdr87cAyBLZh0O5voFGa4Wwx6ZsBx8dznF7ukqEo2JLOCDPJM8FPwBFQytt0Nwwg5C3B4ck8v/G5B5gpeeQsY8jeSdMU15QcGc/z7lKbw+M57SglYKxocrnWGxp5fO7YOI/MVYYm42u9cGhI/vyZpaF4WaYUL15Y5dNHJoaesIN1p4f8Gjw0U+TCSpfpkkcvzrClvK01P9hE9kqXHmC7THvU4nG7+Z3tHtvL+d8NUbaNuB/wbwE7CSrdbj1w4+KzDLFnW7XBMVt+NGxKWSY8/cDEcNhlu2GUU7HixHSBh2fLFByDKMt4+sg4k0UtQnVuucNMyWGlEzJXyaGUYrntU/a0UuXBap6Zgk2tG/G1j82y1kt5/uwqWkh3hG2zIYYZ/ecmixZL7ZisX6Kx0Zl91FfMVJlWvcyEoOKZdKMMyzIRpHdS32xH3Arvfrdp3CzVMghtP8ExBQfG8sRJSq3XG75f9p28rrcDnj4yjp+keKbBtXqXgmtxcq7M9UaPf/7ydUIFCyPB/uCYy3jeJkzgb3/5OEcniwD81s8d5+9/6yxhHKMyODJZIFVafXOtF9HoRvzsw1NMlzx+dKFGO4z59NExfuHkHLYp15mMVz2bIEn5pz+6OhQvy1kmi22fz5+YYH8lB9xcd66lkFJwfqWLEHrQrtYNUX0G2sbSxug1FSXZ8JgF19yyhzVqarITtsu0BxaP283v7PTYXkszd1uU7X7Av0VsxbJ5L5330cUXxAmvXW0QJDdHxHeTTgjijJcurmIbkoJnkaaKMMr49WcK2/YgulFCmKRcbwRkSvPne1FCnDAc4np4psg3XrxCL0o4u9S6WWogxjHgW6eCYengJ9fPDc9rI4skbwn8ERH6wXuW2zGKm4YiEZDEN98bKYj6ZP5e3JcCju9esNdnsnfsNo3biTM6fVpRmMYYAg6M5QEd8AU3tfvrnZAXLqxiWhIymKm4HJsucK3u6yZw/zNHNxlbGkR9uqUc2XU/d3yKpVbE//3CZRq9iIurHc4ud9hXdfni8QneutFipR1xbKrIVx6d5nrd568+vo+J4s3ko+CaQ1ZN0N58C2cIQcmxho3awbobeBJ3w5Q4Sbmy2iNTCtF3rhotbYxeU4sNnwsrHUxDYhuSX/vUAc6vdNdl4i9cqN2SvPJWmXbJszZtBFtRtN8P2vbdYgl9JAevPkjcjqjaKAaLrxVGvHCxBsDTRycouda2nzN6zIJjsNwOWetGlBxTi5Ndb1Lrrm/wGUI3ydpBjCEEF1e61Nohq92AN681WGqGzJT0+T9/Zpm35ps8c2yCx+bKI8FeI0x3lhoeRXsrYXpuBiaxxWPbYTsW4L2C2209FixNgb1U6w0/Y/D7jnmSOBN0I03JRekNYPC6MM2GF/Ho75eofkNUQsXTtfnBurENSTdMsAyBlBJDCJaaEUpB3tEuZWGSESeKomtS2oFBs5N4GaxPaAaexFmW9e9sM+YqLlIO7gtZd54DPf2XLq3R6CXsq+QoOBZ/8MIVukHynnxgB5n26BDl4FocHY589uEpnn1oetfHPiwTu/cz/PeI96PzPlP2eO6RWfwoW2ertt3njB4zDvSFlAH1XsSNRkDTj/nTNxf4xcdnmSl7w2zJjxLevtFkpuQSpynjBQelQAiBlPpCK3nWyC2rxSsNvQltZH5sBQnI/lRo1TMZy1tUcxbnV3rkbcFiM4Z+mWKwDewkSraxhLKNttk9A8WtlX2GrzVMxizBcyeneeNGE5VlBDFMFCx6cYYfJax1Qh3kLEnRs7EMrXl0ba3HC+drmIbo2/NFRAnkbYOia/HkgQqy3xsYrJsg1XX6vK0nbDNDTy23o5RjkwXeuN6g0YuHNfjd6tJbiZcNGD8ba+y2KfnPPv8AL15cYyLvEKQpBdukGWwt/bHaT1xsU5L0mWWrnYAwzd5z83OrTHu3+Z3dHrvXcT/gv0e8X533kmdRdE0t6GRvP24eJRlxkgH6ljJnmcyUHa7XfS6sdpHA4bEcZVffafzCY3Prh11KHjdaPkcmCkyXXDJAqjbdJMWPU0T/OyG07vmBfh12L7mTEH32jdDDPqmCzxyZZKF1nXaoSzjZDtHQMbR5yehmMBpA846gu51H4T2AW9XWGbw2yzJSaXB4okA3SLlQ65Jz6PdKcvz4yhpKCFzL6JuGx3i2bvh6juTh2SJnFtpEIkMIyRMHi/zSU/swhCBM1JD2OFirY56NY2ijGp3h981UUu1s9be/fHzP9XDYLF5mm3LHGrvWalrmh4stPWyn4OCYx1o3xBBi3TVVsHWIihLNBOsECa5l8HOPTPPqlfodaX5utRHs9bF7HfcD/nvE+9V538vnjHp3xklGsxtjWwYnporsq3i8ca1BlCoMQ3JqvsV0yaHuR+vvQBwDS0gemMwxXw9JldKTsXHKixdXkULwVx6fpeia/JO/vEytF1H1BHX/ZigbzwnqPbWpBKOUzsIFUOtGSBTfentBa9rvZceQEpWu/9TRAFrr3rvBHm5fSK0ZZFRdwY8v1wnihCDSOvi9KKGat7Atg16csNaNkBLGiwU8U/BPX7qi5RaU4hcfm6Wct3FNSbGvOjqQ0v7Tt+Z5cKpAPYh5cKrAu8sdfuahSb79zgqmAFNKvnBsAtOQJCrFMuQtD0KNBr+tmGeDGvtaV2vS//jKGpdrer4jSTLeuNbgnaU2rmnw9c8e0hIN/buGjx+qcKXW40ajN6zhTxRdfuGxuaGJ+p0OvPeacdHt4n7Afx/wfnXet5JTaPrx8P9HvTs7QYwfJzx1cByEllBoBok2B7H0QNTFlS4Fy7zZFE5SXrtS14YntsHpxSZppuiFKc+dnKbi2Xz33Aq/853zLDV6rHS0VEIKTOYMbNuk68eEqcAxU/zkJutmINAF+rGgn4lfrfUI96gnHOyiX2z2J0HvVVRcg8ZtDobVg5TXrtXp9XssUsohx91AYRl6DSSZ4tJyh3/ywmWSDMbzNqYUtIKYv/OVB5koOkRJxr/48TWeOTZB0bX40zev8/f++O2hUN1/8YXD/Hdfe4zf+krMlXqXF8/VWGqHXF3rgaLf9D9y2+t4W9qjr2mPAmgFCa4p6QYJ7TDFs7V4X5rBN168wt/80tGhTv7+sRy/8vED2JZBFKe8eqXOO0udYVJ0u1O6e8XdlkN4P/Hh3aruMdimHHKW34/PWeuG69yxzi+3OLPYpuhalFyTtU7EWiemmjO5thZwtdZjruxS70W8cKHG+aUOk0UHaegF2gpiXrigefJPHazw5o0WQaTNSkwpefVKg++fW6XsaKGp5U6CAHK2QAKrvZRukNAKMzqhDvamgLmquymzHWXLO9bOrUxvpGJVsHf+baxdHKnuNm51CtiUuvw1+FZjwo2wHgAAIABJREFUOYsozWiGKavtkMWmZue0ghRDChzTIE0z/DjR5Q5HuzoVHIuFVkiQ6hp4qhS2KSm6FrVOyL87vYJpCMbzDq4l+T++f5mOH7NvLMeJ6RJnV9ostgKWWyGLrYAfX63TCuIdz30nbKeaOVCuNA1Bw49xLBPLNLQkdJKRZoqCaxIkKf/u9BIlx+bweJ6SY/PqlTpVT//3g3Sseq+kjHsN9wP+PYitFtkLF2pDaeI41eUUQ0riRGEYAtMUrPVi9lc89lc9Do7lWGnrmuhM2eO5kzM8OlfmiycmkULT5RzboOxa5ByDlVaIn2iz7CDWIVsKUErRpyjTDFJMQ/PlQdMGu+HOw1DJLhn5aEneNHZejuukkO9B3OrZmUL3LQYcFUMK4lT/7o5lYpuSWi8hb0kqORvPNPBsk4JrMSBbZkpLYQggjjUzZzTgzjd9MpXhmNroJm+bZEpxdkV7QMRJxnIrxJKCvGNiScFiM2CtE952UNuK7fLFE5OUXE17TFJF1bOIkpRM6WM4puzfuSZItJn7RhbOpvLkbbBzbhVb3a3c6WPeSdwP+B8wtvKp3XhhbbXILFNybLJAO4jphnosfqbkUO5z79NUMVf2SJUOspYpOTKZJ+2L45XcflM4VViGdolKU11wL3kmCP0ZLT/iyHge0ME4ytbbAuqs/+aySUZq7lvl38ZORrKsX4A6kG2PD+cltjUm8hZRCnGiNwrPEjT8hL7cEGmmaYt522DfeI6iY1DJG3imYKro8uhckU4Y0wkTmr2IsbzFjy7X+P9+cp21bjgMuLYhEVopjQzohjECmO0b3ViGZLrkEmd6+KoTJiSp4ttnl/fku7wddqI9KqGYLLkUPJOHZkt8bL8W7VtqBXTCmF9/5hAFx9x0h1D17D3p7b+f2KvG/4cFd7SGL4T4feAXgWWl1Mk7eay7gVtt5Gz0qU2zDMs0hpK0AxbDUGTKj4cKhqYUfPnhab751jyXa11OzpYo5k3eWWozVbaJk4y3rrdwLcmxqSIHxnO6adZfmN0gYa7scna5gykl+8oOF1e7vH1DN4g/c3iM2YrDX5xZpt6LKNiCTqRItIcETx4scm6hraUShMSRGWGmL4gwyghHqJYD4TQpNOUzZyksKQhitameP/rvxebW4mAfFmxHXbWFHiQb4FDF5Nc+e4Q3rzao9SJUluFHWus+Z4UkaUaYKASKE1MFfu2TB/i9H1wiSDI8x+SJ/WUqeRvTkIzlLerdhMfmSmSZQmXw/Jllnjs5w889PEMnTmj7IX/w4lVaga7Rf+74BD++1uDPTi0ghGJf2aUVJoR9A/ojEwWm8g5ppvjzU4v8yscP7CpfvNW1sB3t8Vc/fpBPHh7jxQtrxKm+I/nMkTFsyxhO0s5VcpsIDIX+UOAHKU1wL8ghvJ+4o/LIQogvAB3gG3sJ+B+0PPJ7wa02ckalUS1T8GdvLrDQCjg2WaAbJsRZtk5/ZLkV8I0XrxClGUmacXSywKuXa7x06aY8bsUzmK3kaPYi3ZRNE7oRuAZ4jsXf+tID/MYXjvPtM4v8w+9cIMm0MuHXP3OQH5xd5TtnV7R1Yn+CVUitfTNVcOiEuplmSECBZ0sc02C1G2NJUBkYhi7DdKNsW4ZK/+24pp6c/Shl6XtByZG0wgwDfWcUpxlSSH7+sVlsS2IbAtc2aPYSOmHC+eUWDT9hPGdjWwafPjxG0TO5tNojTjOiNNXmPVNFrq11mSm5/ORag8VmwIAYOlfx+NQD48w3fA6O5/jJlTqLLZ8kBT9OeGS2TDuIeHFkLR2sOpyYKXGj4TNXdIky7SY2lrf47LFJvnpyZtep71ttau6WMG33/N1gzNzrLJ17Qh5ZKfV9IcThO3mMu4HbETwbLdM0/YildoBt6ov9esOnF6cUXRNLyqE40zPHJsiU4uVLa1xY7vDy5QaG1GWSIFGs+SnHpkzOL3dQma7rWwJSJah6Fn/wwlU+fnCc3/n2ecqORcGz6Pgxv/eXl1nrBNiWBKVoBTrgO+jG6HInJM50+Ci5Jms93aydtAzG8ma/cZjhWAYF16QTbZ+ZDxJ4/6Oje7YneKbAkIJBPiWElq1o+DFxmtGOEprNiLafYEg4PlWimrMoujZ51+LLD08zU3RZ7AS8da3JvkoOU8Kp+RadMKHkhVRzLmvdmLOLbTKlKOVsWj3td7y/qqdSr9Z6XFjtkrMMDo3nuLbW4+2FFjca2otgoEZ6tR7yy0+WOL/Y5vtLq8Py3IGxHF95dHbb9f1exP9247Fv9/zd4L9/GDn3W+GufwMhxG8KIV4VQry6srJyt09nT7idRs5oLVAMfTYlAm3wPRBvGvXULboWliGxTEmtF5IphvX3wQW52glQ6Mw5VSCkYOBi2g4T/ulLl1ltB0N9/IJnEcYZrTDta8+rYXaeZKCUGA5HSaEbgvSPFycZQaxle+NUNwwHvqQ/7RBo1s3g7yKEVo70+/2ZVEEnSkkzPa9wdbXLajskU9qd7JXLNd6eb7HYDOgEMRdXurx0aQ2/z9ABbeGYqQwpGDJaOlFCqhRSGmQZ+r8KulGiTUnSDPrrAjS1tRfqv9mgvTI47/MrHZY7EVLopMI0BAvNgCBKt13fH7Wm5kcddz3gK6V+Vyn1CaXUJyYnJ+/26ewJozX2XpTQ9uNdGzlDzZwgZqUdMV6wGcvp0fkoyZgpOuScm+JMA8N125C0ezG9PuUviBVkN4P0eL/WqvrNviRVZJmmCEoheOJAFVMaXF/zyTJFx4+xzb4Ofaa1VgYwJQRJNmzSSnRWCrosEyba/NrUGl60w5TWXkn2PwWwpRj+XdJUa9EMoIA4ScmUthoUEgwhiRO9+WYoHpwuMJG3CeObZu6uJXloukjTj6h3I8I4Y7roYkhB049wLQNLSlxLMJ63cS2BKSWeadDsRdiGoORpo5sozRACjP4fPe4PUPT3BPKOSZplGFIihJaozlTGUsffdn1v1dQERdwnJdzHvYX7g1c7YLu6nW3KoZpklGbD6b9BVrNdPbIdxCRKZ2wf219GILBMgwMVD9OQW3rqBn5KnGUcnswTJAnnVnwG7MQxz9ClIFtzr+NUEaRaZCzOMv7K47PYlsFXT07xb99c5PxKG9eS/MrH5/iztxa5shaQZnrSNs3QGaLQZRzPEqx2YtpBqumDJkSZdhkve3fHJPxehAm4FnRi6PUDfM7UvZA+CQpTgG3AY/srjOd1f+TccoeiazFX8cga0PJjWmGCbRs8MuZxeDzHbMmlm2Q8MO7x7mKbbpziWoJOFOvJ217Mk/tLHJ8pcrXWY6UdIoTgoek8E0WHV6/UGc9bTBUcEqX9XO1A8jc+c4gXzy3zoyutoT7RoarDWicizUCim8O9MAUhKFjWto3KjU3NbhADgm+dXvzQDyl9FHE/4G+D3SzQziy2eebYBFIK1johf/TyNR7bXxk2XUcX+WLT5/kzy7x6ZQ1bSh7dV2I8l6OXJDz3yOxQWXArcaaldkDLj1lqhTw4W8azTKJU8aUTE/zSkweI0oyXr9aQwLVaj2t1n26kG3tvXGvy1o02QZzimJK8LVlph3z7nRXCBB6dLeLZJgutgPGchRSCimeTASf3lXjxQo2xgk2SpLx5rc61egACPNuELQK+gL5Dk8VqK+bDxrnZSSBOAg9O57BMwbW1gIafDuWdO/2qlin1ppmqEVZO/78qhfG8i9mftMo5Wi44SDNMKZkpuTy2r8zLF2qcvtFiqRUgEXz8YIVmL9KOYggWmz5SCPZXPQwRc63uU3EN3vZDEAJDCD53fJycY/NrUwVsyyDLFK0g4YmDZV69XGdf2eOBiQJffjTgJ1frHBjL8eB0iTBNWetFvLPYJlOQdwyePjbBr3ziACXPGk59byc41gpivnVqkZJrvWczn/u4M7jTtMw/Ar4ETAghrgP/vVLqH9/JY74f2K0RNahbjuctkjTjcq2HlIJKzkIi1r128FmmIRDAQtPnylqXY1NF5iou1kgzaCtxpqpnc2W1h2cZrHVi/DgjUYqVbsz3zq3w6888gELxj759nneXO3iW4PEDFV46v8pqR2vtdMOYkmtiSJuCY7Pcinh4usi1ps+njkzwGIoozji92CJOMk7MFXn7RotKzubxfRX+6OUrREowVnBoBjG1bbxVi45EASvdmGjLV2zGXlQ4PygY/TudrZABHz80zpvzLRxLorYQBzKlIMwU0RZfKFUQpAmrjQilFEXHouXHBFHKvqrDajvmJ1e1nkw1Z1FwTIIo5fRCGykUqRKkWcZSK9SKma5J1bOp92IuLbfJ2RaTZZc4TvnDl67zH3/mEPurueHxW2FMybEI44TvnV0Z0n0fnClSzjkUPQsvNTi5r8K+So4HJvNMFRxMU+LHCc+/s7QjC8c2JZYh+wnB3fNsvY+dcadZOr92Jz//TmE3yePRuqVCEfQzc8eUmFKue+3gs8quxWIzwLNMpJSkmda62c3AOVWKI5N5Ltd6LDR9crbBuGdScEzOLLZYbYf8n987z/fOLRMmOqE8s9gF9BRnXurv0I5SpIxxTK13vtKNGMtZfOmhSfww4f/50VVqnZBXVru8fr1OmCkemSnyo4urLLQCio7e/BCCRidiK6mwbpStkz7eC+6lKu9ug7zffXeZ1U647URwkqhtv7sA2kFCO0wYz1kcmsiz0Aho9mJc0+KXn5oCIVhqBZQ9i+miy1I7YLHh0wwSjoznyTkmUkCvv6akFENKL1Kw1o2xpCBKUxpBNFRwXWj4vHW9SZIqTt1oYxuCQv+u0jIkpsHwtccmC7wRNhjL62D/2SPjvHChticWzr3g2XofO+N+SWcL7LZwR+uWQZKSZopjUwVMKTe9dvBZ3Shhpuyx2g6I0gxDCg6MFYaTsDudy3jBIe8aXFzWWjpCCKw+xeLKaod/f2ZlS2neMNUj+jJICGNFM4txrAzPMhEC6n5C2bH4/R9cwrEkrSBhMu+QKoWDvnP59JExVF9CQQiJYWjFzSDRvE0LCAZliy3OYTfcqqTw3USlYLLSCcmyrXcGxxIk24j2S+DIeJ6lZsjVNR/X0VIHY0Wbx/aXOTCWHyYO3TDhaq2LKSUFx8QUkuVOyLQU5GyTMEnpBjErrQDLlPiRIk4SDCHppVpf+rmHZzi92Ga+6fPOQounDlUp9yW4LUPy1MEqOdug1o341OEqr11taL16S66TR74Vv4eP2pDSRxH3A/4W2MvCHVW2/JkTU7x4scZSO9j02sFnPX9mGYVituLxyFyJkquFsnbLfkbfP150qHdj9lc9ulHKwzNFar1A0zoNsV4DoY9ulCAEWIZm3GgVS83q+cShKjdaPo1eRDdKafRi8raBAsYKNo1eQjtImSw4LDR9lErJEOyruBwcy/H2fFNT+5K+eckeI7eJHtoK03srw98JM0WTh6bKNDop9V6Ea6qhIugA8cgPYIqbOkEGsH/M1bIJQv9N4z4dtmhKHEsOk4vH95X4k7cWSZUiZxk89+gMy+2QS6tdJgsOYzkbP07ohDHtMMOTEteSBLEWVQN4cLqoZyyEIk4zUpXhWga2IXH7QdyQQnsvSMGh8QKHxgvbDhbdStZ+tz1b72Nn3A/4fQwYOVmq6MQJVc/edeEOhjHKnsVM2dv02sFnjuUdfvUTB/jUA1VevrwGCKI023P2M1P2+KtP7OOR2SKvXa+jMjGUY1hqaHeqYENmObgcx3IWgSV5cLpEsS+U1o1Sjk7muVLr8dqlOmeXOkwWbGxTEiYZSZYxkXcYL9h89ug4Sqm+oYkiSVNc0+DZR6b5/LFJhMz4s1PLGFJxeqG76dzNvvBaqqBogyUlpiWRSmKagrG8zdmFNgl9E/O7PI07uOMo2oKyZ2EYBkGcEKeKy2tdhIRSzmCy4CKUwLI0PdePdVBdaPQwDEnRsbSpSKY4ua9C3rV4ZK7IWjfkYq1HO+y/x/ToBgnX4y7CECy3I/7Gpw9xdqWDa0i6UcqjsyVSlfHQdImCa3JyrsTv/eASD81UiNKUTqCndGcqLkXbpJgzefHiGmXXpOJZ2IbBa1fqfPHByR0drfbCwtlL1v5RGVL6KOJ+wOcmI+fyqh52Gc9blD2br3/2EI8fqAK7j1ZvXORbsXyOT5d2zKR2O78kU7iGySePVXFNgxcu1Gj6MbbR51SPQAFjOcm+MY9GN+ax/SUqeYfT8y1UXyv/qUNVSp7FiekC55Y6kGXDQNCNYz55sKqNTAzBLz01xzdfv8FL13VQ/8m1FlXPYHYsx3yjR3sbPv4gy7XRQmHtLINAv9bsn6gwBFmsSMXOwX638o9rQvAe2KK2vFnHDxKF345wTUHe1pvk9bqPBA6N5/nE4XFu1H2OTxdYbge8cmlNUxqF1nWP04xOmOFY2iS+G8YkiaLuJzw+V2bNj1FZNiyXnJ5vcXgiT6IUYwWbzxTHOXWjSb0X8cBEnt/6yoPDMkvLj7FMgWVoaQYh9J1D2TUBPSSx0Ozx7TNtEqW0NaBjcr3uU3TN23K0up+1fzTwUx3woySjFcQ8f2YZ25S8ca1B0TZJM/Ask2+8eIXfntSqhIOAC4pPHR7j0HhhW30PYEeWz8Y7gMF7/FgbVY+6+GzFGHrl8hoogW0IelGCYjPbpZwzMaRgruxxdKzAd8+uMlVysaTgK49MUuskTBYdemFKN0xYavnDQZxK3uRANcdyN2SmmieKUr7zzgovXW0hBdiGwE8UNT9lDujuMHxloamLEWyq3yRAohSqX4pyTTmc7N0Ku1WMSu8x4A+CvURPyiapohMpwijBNPVAk21ILNPgP/r4fiquTcOP+P75ZR6cLlLvxbxxvUGtFdKKEgpKUMpZeLbJ0ck8zz46TZBkzDd8FtoBBduk5FpcWOlimZIDYzkWmwGvXW3wxROTPHGgQsOPN4mXDYzDL612SZWinLMYy9vMVnJ4lkE1Z/H9c6tUXItqX06jHcR85eFpZivebQXs+1n7RwM/tQF/kDW3g4S355s8PFMkyRQVz6AZJtimtp5b6QS8fGmNnGUSxAmvXW3y6uU6nzg0xrMPT60zCR9k808drOza6NqonJmkGUGScm3N5/h0kfGCzRdPTOLZ5qbPWmpn3Gj0ODPfotGL6G2hbhDGCfUY/uWr18mAyaLNVNFlsRXwJ28uIqXAu25gSMlrV+qaXaPnqlhqhkRxg6mSw7MPWSjgzRsNMqWpi6PN2XcX2jvW4S1Dv3g7M6taJxo+916ndpeD9/T2ITIgGumHxECaKIIkI04zLix3qHVCXr60RjtIeOXyGkXHwDIN5us9VjsRhtRic1Mlhy89OEUz0LTIqZLDTMlBoWUyklQPzOUdg5xt8NShKi9cWOV6Q2fjXz05s0mpctQ4vBMmxEnG4/vLjBUcskyx2AqYLNh9iQWtuDpRsJGGuB+0f8rxUxnwR7PmsmtxYbnDueU2cZpxbrmDkII0zRjL27im1gaxDMGrV1oUXQsj1jojG03CBxn4y/0MfLtG1+jxLVPwysUa1xs+UgpMITClYLY8Mfz8jU0zIRUvX6wRJVqHZyv0Yl0CybsGLT9lsRkxWehRydk0g4Qxz2K+GVBwTTL0JOjAxUon+orldshiM6DeizlQzVHvtgD6dzoaecck2kUZbRtSCwBF12Ctp+9NJPduEzdDb04ARcfgh+drHJkokLdN6t2Iti95eLZE00+I0owxxwIEtU6EH+u/f8mzhvXwg2MeF1e6HBrPc6XWHbK8XNPgE4fGeO7kDCV3ewe1UePwlq9dzjphgikFP/fINBeWO9ru0jYIIm1QX/V2sRS7j488fioD/kaq2VMHK/zgwio526DRiyk5WoDqyESBYt+lp+nHQwqlIXRDr9aNtnThaYWCTz5Q5Sd9qtvGRtfo8Vt+zGI7xJTab7TgmFxr+CilA2uqlGbpvLPEUjvDMjW9L80UhoQgzIaBcmPAtARkSgwz90urPXJOSME22Ff2OFpwmC25/PDcCmF6s7kK/f9PM97uT31OFV1OzhU4Nd8ZvsYEwjjdVFsfODhlaEE2gJmSTb0dEW4wU9nNx/ZewiD2dsOUME6xTG3VN1txWWgGXF3rIqTg2FSR/VWP1U5EvRex2gn5pSf3YZtyXT18UL5r9qJ1LK9nH55iouDs+bymS5tr7F//7CG+8eIVmkGMbeh/76Zpfx8ffXxkVsCt6FVv5Nm7lsnj+3QZZqZo4ycZRcekGSTrAm43TEhTxVOHqkNK26gLz2g2f3i8wOFtGrTrBKf6ypmuKTGkGDpIBcnNuwI/SkBpo+prtR6L9ZBaJ0QJMRTZMvoaOFGiA3A3Vqj+k4PafpIp6t0IPzS4VOtweCzPwTGPyZLDjXo4DNpaNVnTAgueRd42uF7vYZuSnAWOJekEWh5ZSkGc3bRETDJd9rEl5ByL4zNFLi13WGrdnL3NGaCEvqPobcNbvx1sdYfgGnCbvuJbwpACUyoafsT33l1BSLiy2tO/V9GFTAvYTZdccpZF2bX46584SHXEsHdjPXw7ltdO2E2D/vEDVX57skjdj4amIvdxHx+Jgt5i019n+L2bLdtWnpvPPTpNNWdhSE25U0oMA+7ApedvfukoJ/eVidKMXpzw2SPjpErx9NHxTf6do5TN7QZUenFCO0iYKtiM52zG8jbtMKHimSgFXzyh1UO/d3aFnG3Q7MWUPZt6L8IwJGGcYhqSgqvVL00pmCg6/LVPHeSJA0UAvVkAeVt7lrqWNlGt9ucAbjRDyq7F4/vKjOdMcpbAtSQ52+DxgxU+/cA4rqk1eK6u+XQjKDomJc+iF6WIfn0+zjSvPlUQpzrwFx2DvGWw3NbBXqEXXJDe1Mf3zPfPmHzd3Y3Ux9pNpde5hSHQONPsHalgPOdsOmjeNXlwukCUpFxd6+HHCb/x+QfWBfvtsN1a2Qp7NdYuuLr5fj/Y38cAH/qVcLsGDFtRzb5oyG35xrYpOTFdGmbtg7rpIMP67JFxyrnbo7r97EOTfP/cCn6U8cTBMk8fmRiygJp+rO8ALH37X/Esmn7IRMGl6KaUPQtT5ql1AybyDjNlj0PjBTphypHxInnP4DtnVrBNrXPjx4IgzqjmHA5P5/j88QneuLpGI0hwLQNDCIQhmCp6WFLS7EW8s9TBMwUJugxzrR4xV/U44Frsr7r85bk1HcwFQ8OPsmdweS3gylpAohiabaBuUjVdAyo5CydMaIYZh8dzXKz1tv3NbKk3Ekv2m8Bi+2EvQ4Bna22bdIsWg9l/b5Le/OyiqzXlO2FKqhiat4/Os0nAsCRrvZAnDlR0A7u/Zz22v0zVm+RGy+ezR8Y5WM1vGWzfq3vSrUy/3sd9jOJDH/Dfy+LfeGu9E9949CL1LIM/P7VeX+TFi7VbVgUcvQv41Y9vfUs/LP+EqdbuySI8y8S1JHnH4OhkgQsrHao5h59/fIbT8x1+fGUNKQRPHaoyWXBYbAS8s9ihEyZICfsqDrYpubTS5RdOGgRxyko7oNXnNJYsc+iOlHMMMqWo5Gwafkyaab38Zi+i7FqYUm8kgwErsx+UVzoplgEV1/z/2zv34Dju+oB/vrv3vtPpZekkvyLFdmLHj5jUhBimJoMLCUkmbWfClDblVShTBih9MECA6TCdMtBpp5BQ2hkIATqTmTSTlhJoUpq4QANpQkwSx3ESsB07tmzFsmQ9Tne6u929b//YlXyyJFuWdTqf7veZ0ej2/f3e7n73d9/9PjiVc/HUf6cwWbffBhAhbNvkxSMWglu2dHHvz1+dM7Ryy8o0L/aP+aWc4bxxmkXPd0vN9UCYjK2fXO6WoawWecfxZRRfF8uSKbdTb2sERy1OjBT4j+dP8uiLp7htW9eUHK2JCI6rpCIh1nc0zXotLLQdYCWmZo1hodT9cGCxu8rP9tP6XJfR0aHxRe/ycz73T0cqwoO/PM6Bk6M8cfA0tgVbutPkiy77jo9wcmSCN/W00t2c5K1XdbB1dQt/vOtKIiGLoXyJHT1t7L66k9ZECNdThvMOTx4Zon+0wA9f6GcoV6I1ESEd92vsjBdcDp/OkoqGaEtEiAbuI0v8EbEt0Bzz6+kcHcr7zT2CkXux7L8z8GvoW1i2RTjQxSn7sffJCHSmI5Q93xfulsusz6R4ZWCceMimaRY/SywElmXRkQrPVkFiBoovy1wGv1RWUlGbdMyiqykcNHRxKHm+jH51y7PhmRELYpEo/SMFLIFVrTEitsUP9r3OimSE3hVJhnKlaS69GcecpyvmQszmkjQ1awzzoe5H+NUu2DRr4tORYRBdkhHWeMHlkf2vs7m7mVjYYqzgMJAt8Ru9LWwO3ic8+9oIgzkH1yvjeEpTLMT6zibWdzYx4Xg8ur+P7/zsJANB4fbulhiOW6Z/tEAsYhEL+710Q5ZFImzjlGEs7xGyXLasbOa2a7v5z339lNXPHL0q04TjlXHyRSIhmRGlE7N9d0jYElQVLLCDxikF18MWi450jLdvynDD+nZeG5wg0xxjwnE5eCqLoMTCFp7n4ZSVzqYow3mXoucxmHVmRASdz/5XPoIrk9Ncz+/stKYlTqms9AS/avYeGUIsy+/25HmUFDZnkhw5M8HrWf/htiodIR2Lkooq/SMT7NzQzo4r2i/opllMV4zJfjUshLo3+FDdi3+um/T6nrapCoPVrAo4PFGi5JXpag6TK7qczhY5NVZAUd6+KUMmHeeGdfa0ZJ1JWQZGCzx/fIiv7TlMLBImEfVT+Q8P5OhKR/EUXuobQ1FGiyWcIEIoFbUJBZE6I4USv7mhk0w6zlNHBlndkiQRtfjBvn7CltAcDU8zugIgwpZVKV48mSPnOHhl36eP+B2+bBGiIZvXx/2mLmfyrp/fYAk97QkO9I9hizDhCa3JMOGQzXVrU7geFMvZad+PMv+Km5Xj6N62BKWyYocsnLzLilQEL+gGHAnZiOdwJnAtHRrM8wc3rGZnTwdfeHg/yZj/EjZfdAnbFutXNM1g/vhyAAANhElEQVQrE3WxXTEm+9VwsSwLgw/Vu/jnukkvVGFwsWiNR4jYFqMTJU4MT+AFI/imaHgqBX+2ZJ2H9r7GPXsOkys5DOdcVrdEg9IASq7sJwclIiG6WmJEQhav9I/RHI/QmgyztjXG4dN5BrJFnjs2SsTO0t0cZee6FQxmSwwHCUipWBjH0ylDagcvQguuMjTuctM1XRS9Mi+fHMPTMrmSSzoawlMlYgsDY0VsZOr7tYAJp8y6jhQbMk2UnDJDuSI7etrobU/yxKEBYGb4ZUj8BLON3U089erIjO9w+6omXjyZxbZBFG7d1s21a1p5pT/LUK6E647zSv8YKsKEC2XPwcX/RSD47zz2vDTIR3ZdxV/cdDX37DlMtuhii/Cnu9fR2Ryb17k05YMNtWbZGPxqcaGbtNo3ayoW4r07r+CbTxxhMFckFQmxe2MnLcnotFF9ZbLOwGiBe/YcJhGxSSdCjOSznBgp0tMe5/R4CcWiKRriqq40uZJHIhpi9zUZSm6ZwfESlmXheEprwq+giULYtknHw6xuSeB4yqtD4+SL5Wm1byr95ZYtDOVLbOxKMzhe5ORonkgohOMqiYgFCJl0jGQ0NPX9ZgsubckwTVHf6x+P2GxvayEetnE85ZruZiz6ZsTadzX7v1a60gl62wscGTpbY2FTJsn2te10tyS4ZWuGE8NFVrUmiEdsmqJh+obz7Dt2hqZYmHDIYjRfJFvym4iHLWFlS4y2VIz+0QmODue4Y8cV7NqQ4ehwjp7W5LyN/VlZjSvGUDuqbvBF5GbgbvwB072q+uVqH3OxqfVNum1NK399e5IH9h5jRTJCazLKRMmbMwX/6LBfVCsV8w3nynSEE6MlRoou6ViIO29YSyIaJh0NT7W6K5XL7LyynScODnImX2K85LCzt51ENEQ05DdYf2OPnz3slpUbr+rk4KksJbdMuD8LApYFqJ+YdXUmzetjBUYmSnSnY7TE/bj/saLDqma/yNf6zhTp4EX1ZE/UeNivKWNZQrmslLwyb17XHoTAwobOOL8emJhy4axpibKjp43XhvJsyDTxxt5W1jTH+dnhAQ6fzpOMhhkvOnzkxnVsW9M6FSUz+fBe35XCDtlk0jHKqnSmYxw7PYaHRXsqQksiynjBwRahpzUJQGdz7KINfSXGFWOoFdXuaWsDXwfeDvQBz4jIw6r6UjWPWw1qfZO2piL87htW8dNfn75gCn5PaxJbhPGCQyoWJhmLkPHKfPqdm3hjbzur2hJnwwPd6eGB72qOMzbh8KOX+klFzzajRqEtGeUdm7qm+gU4bpn+7AS7Ng7yzZ+8Srbgh33esm0lGzJNPHdshFUtcVoSYd62sZMJx+OnvzrNyIRDWzLCrVu7p/1SWpGKsntTpy+Xc7YyaSYd59atKzk2nMPVHmK28Iujwxwfyvvlf8vw+ds2sa4zPfVA3rUxw3jBnZFpeu7DeyRXwhYhV3CIR0PkCg6JWJT371zLd/7vGP2jExftujEYLldEL9Bi75J2LrIT+IKq3hRM3wWgql+abf0dO3bo3r17qybPcmC+STuTPnxPFS0rb1nfzvYr2qYZ9/Pt69xqnqC4QR/eKztShIIsqmRQa2hzV5oXTo7w84ND2EEVyCta47SnY8TDNrs3dTIwVuDbTx6h4PiJZB94c+9Uv4FzdTw6NB5EQ0Gu4ABCNGyzv2+Ea1e30N0SZzhXZDBXmlG64GK5938P8vWfHKGsiiXCR2/s5UO7NjAwWliw68ZgWEpE5JequuOC61XZ4N8B3KyqHwqm3wO8SVU/Ntv6xuAvLgOjBQ4NZjnQN0ZXc3xqtJ533HkliZXc8tRoPxEK8XzfCILgapmS4/flfetVHTieMlYsgQqJiI0qPHVkCFvOLj+TL/L88WGaY1FSsVDQpcnhC7dvmZGNWnLLfO+5Pr+aaFCVFOCtV3dwOltk3/ERtq5pJhayF5S4NNuxXLfMaNGlORoiFLiYjNvFUC/M1+BX24c/W6GUaU8YEfkw8GGAtWvXVlmcxqKzOUY0YnNkML/gTORwyH/BattnSzsMZAsgYNsyFe1zKuu/Ss2kY+RK7tkmL8Hy7BmXgqOsavEvuVQsxJl8keGJ0gyDXxkKmy+52LZ/GRXdMt3NccoKb9vYSaYpdslGefJYmeY4mWDeqWzBlCkwLEuqfUX3AWsqplcDJytXUNVvqOoOVd3R0dFRZXEaj0vNRJ7c3vM0eC/gTkXueJ4SsScbcFtBZU+PaMjC83Ta8qZYiFjY3x78hLKIbc1ao71S5oh9dl/RkL+vWNhaFGO/GN+PwVBPVNvgPwNsEJFeEYkA7wYervIxDRVcahr+5PalcplMOsp40WFlc5z1nSl6VyQYyvvlBHZvzLB7Uyd5x2Uo5/dhrVx+0+YuPvDmXsaLDsfO5BgvOnPWaK+UeShfondFYl6lC2rx/RgM9URVffgAInIL8FX8sMz7VPWLc61rfPjV41IrNE5uX9lzF5ixz9n69FYuny1yZj4yz7avxeRSvx+DoZZcLj58VPUR4JFqH8dwfi41rHSu7Wcr9lY579zlqVho3vXZL7SvxaTWYbcGw1JgrnCDwWBoEIzBNxgMhgbBGHyDwWBoEIzBNxgMhgbBGHyDwWBoEKoelnkxiMhp4LVay7EIrAAGay1EFViOei1HnWB56mV0mpsrVPWCmauXlcFfLojI3vnExNYby1Gv5agTLE+9jE6XjnHpGAwGQ4NgDL7BYDA0CMbgV4dv1FqAKrEc9VqOOsHy1MvodIkYH77BYDA0CGaEbzAYDA2CMfhVQEQ+KSIqIiuCaRGRe0TkkIi8ICLX1VrG+SIifycirwRyf09EWiqW3RXo9CsRuamWci4EEbk5kP2QiHym1vIsBBFZIyI/FpGXReSAiHwimN8mIo+JyMHg/8xekpc5ImKLyHMi8sNguldEng50+teg5HpdISItIvJQcE+9LCI7l/JcGYO/yIjIGvym7ccqZr8T2BD8fRj45xqItlAeA7ao6jbg18BdACJyDX5/g83AzcA/BU3r64JA1q/jn5trgN8PdKo3XOAvVXUTcAPw0UCPzwB7VHUDsCeYrjc+AbxcMf23wFcCnYaBD9ZEqkvjbuC/VHUjcC2+fkt2rozBX3y+AnyK6a0cfxv4F/V5CmgRke6aSHeRqOp/q6obTD6F37UMfJ0eUNWiqh4BDgHX10LGBXI9cEhVX1XVEvAAvk51har2q+qzwecsvgFZha/Ld4PVvgv8Tm0kXBgishq4Fbg3mBbgbcBDwSr1qFMa2AV8C0BVS6o6whKeK2PwFxERuR04oar7zlm0CjheMd0XzKs3/gh4NPhc7zrVu/wzEJEe4A3A00BGVfvBfygAnbWTbEF8FX/gVA6m24GRisFHPZ6vK4HTwLcDV9W9IpJkCc9V1RugLDdE5HGga5ZFnwM+C7xjts1mmXfZhEedTydV/X6wzufw3Qf3T242y/qXjU7zoN7ln4aIpIB/A/5MVcf8AXF9IiK3AQOq+ksRuXFy9iyr1tv5CgHXAR9X1adF5G6W2NVmDP5Foqq/Ndt8EdkK9AL7gpttNfCsiFzPPJq515K5dJpERN4H3Abs1rNxvJe1TvOg3uWfQkTC+Mb+flX992D2KRHpVtX+wH04UDsJL5q3ALcH7VFjQBp/xN8iIqFglF+P56sP6FPVp4Pph/AN/pKdK+PSWSRUdb+qdqpqj6r24J/c61T1dfzG7e8NonVuAEYnf8Jd7ojIzcCngdtVNV+x6GHg3SISFZFe/BfSv6iFjAvkGWBDEPkRwX8B/XCNZbpoAt/2t4CXVfUfKhY9DLwv+Pw+4PtLLdtCUdW7VHV1cB+9G/gfVb0T+DFwR7BaXekEENiC4yJydTBrN/ASS3iuzAh/aXgEuAX/xWYe+EBtxbko/hGIAo8Fv1yeUtU/UdUDIvIg/gXrAh9VVa+Gcl4UquqKyMeAHwE2cJ+qHqixWAvhLcB7gP0i8nww77PAl4EHReSD+BFj76qRfIvJp4EHRORvgOcIXn7WGR8H7g8GGa/i2wKLJTpXJtPWYDAYGgTj0jEYDIYGwRh8g8FgaBCMwTcYDIYGwRh8g8FgaBCMwTcYDIYGwYRlGhoOERlX1VTF9PuBHUA/Z0PitgL7g8/3qeo9IvKH+On+Nn4o6jPAJ1V1RER+AnQDRSACPA58PqiVYjBcFpgRvsEQoKpfVNXtqrodmJj8HBj7m4E/B96pqpvxU+SfBDIVu7gzqCq6Dd/w11VikGH5Ywy+wTA/Poc/mj8BoKqeqt6nqr86d8Wg+uangLUicu0Sy2kwzIlx6RgakXhFVipAGxcuq7AZeHa+B1BVT0T2ARuBc6unGgw1wYzwDY1IpbtmO/BXF7OxiGwVkedF5LCI/N75Vr00MQ2GxcUYfINhfhzA99tPFsrbjt8bID7bykFHra1M79hkMNQUY/ANhvnxJeDvg05Mk8xl7MPB+sdV9YWlEM5gmA/Gh28wzANVfUREOoBHg9H7CPAifrXNSe4XkSJ+ddHHqcOWiYbljamWaTAYDA2CcekYDAZDg2AMvsFgMDQIxuAbDAZDg2AMvsFgMDQIxuAbDAZDg2AMvsFgMDQIxuAbDAZDg2AMvsFgMDQI/w9q2uhOvnzJ/gAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_final.plot(kind=\"scatter\", x=\"HTGD\", y=\"Mean_home_goals\", alpha=0.3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0xe288940>" | |
| ] | |
| }, | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_final.plot(kind=\"scatter\", x=\"HTGD\", y=\"MW\", alpha=0.3)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Zmienne, których odchylenie standardowe rośnie wraz ze biegiem sezon, należy podzielić przez dany numer kolejki." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Modyfikacja zmiennych które rosną wraz z kolejkami" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Z racji na fakt zwiększania się niektórych zminnych wraz z biegiem sezonu należy taki zmienne ustandaryzoać aby nie zależały od momentu sezonu, co mogłoby zakłócac wyniki.\n", | |
| "Najprostrzym sposobem bedzie podzielnie wybranych zmiennych przez kolejkę sezonie." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df_final[\"HTGD_by_MW\"] = df_final[\"HTGD\"] / df_final[\"MW\"] \n", | |
| "df_final[\"ATGD_by_MW\"] = df_final[\"ATGD\"] / df_final[\"MW\"] \n", | |
| "df_final[\"HTP_by_MW\"] = df_final[\"HTP\"] / df_final[\"MW\"] \n", | |
| "df_final[\"ATP_by_MW\"] = df_final[\"ATP\"] / df_final[\"MW\"] \n", | |
| "df_final[\"HT_Wins_by_MW\"] = df_final[\"HT_Wins\"] / (df_final[\"MW\"]-1) \n", | |
| "df_final[\"HT_Losses_by_MW\"] = df_final[\"HT_Loss\"] / (df_final[\"MW\"]-1) \n", | |
| "df_final[\"HT_Draws_by_MW\"] = df_final[\"HT_Draws\"] / (df_final[\"MW\"]-1) \n", | |
| "df_final[\"AT_Wins_by_MW\"] = df_final[\"AT_Wins\"] / (df_final[\"MW\"]-1) \n", | |
| "df_final[\"AT_Losses_by_MW\"] = df_final[\"AT_Loss\"] / (df_final[\"MW\"]-1) \n", | |
| "df_final[\"AT_Draws_by_MW\"] = df_final[\"AT_Draws\"] / (df_final[\"MW\"]-1) \n", | |
| "\n", | |
| "df_final[\"Goals_mean_diff\"] = df_final[\"Mean_home_goals\"] - df_final[\"Mean_away_goals\"]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Korelacja zmiennych - wartości" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <th>Total_value_A</th>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <th>Age_diff</th>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <th>HTGD_by_MW</th>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <th>HT_Wins_by_MW</th>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <th>AT_Wins_by_MW</th>\n", | |
| " <th>AT_Losses_by_MW</th>\n", | |
| " <th>AT_Draws_by_MW</th>\n", | |
| " <th>Goals_mean_diff</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>HT_LP</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889973</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.902016</td>\n", | |
| " <td>0.842907</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_LP</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889140</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.898787</td>\n", | |
| " <td>0.844142</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffPts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.879163</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffLP</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mean_home_goals</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.83074</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Total_value_H</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Total_value_A</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.874038</td>\n", | |
| " <td>-0.876916</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Age_diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.879163</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HTGD_by_MW</th>\n", | |
| " <td>-0.889973</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.892840</td>\n", | |
| " <td>-0.887502</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889140</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.891313</td>\n", | |
| " <td>-0.884921</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>H2H_Home_pts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.874038</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>H2H_Away_pts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.876916</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Wins_by_MW</th>\n", | |
| " <td>-0.902016</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.892840</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <td>0.842907</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.887502</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Wins_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.898787</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.891313</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Losses_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.844142</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.884921</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Draws_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Goals_mean_diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.83074</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.00000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HT_LP AT_LP DiffPts DiffFormPts DiffLP \\\n", | |
| "HT_LP 1.000000 NaN NaN NaN NaN \n", | |
| "AT_LP NaN 1.000000 NaN NaN NaN \n", | |
| "DiffPts NaN NaN 1.000000 NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN 1.0 NaN \n", | |
| "DiffLP NaN NaN NaN NaN 1.0 \n", | |
| "Mean_home_goals NaN NaN NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN NaN NaN \n", | |
| "Total_value_H NaN NaN NaN NaN NaN \n", | |
| "Total_value_A NaN NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN NaN NaN \n", | |
| "LP_Diff NaN NaN -0.879163 NaN NaN \n", | |
| "HTGD_by_MW -0.889973 NaN NaN NaN NaN \n", | |
| "ATGD_by_MW NaN -0.889140 NaN NaN NaN \n", | |
| "H2H_Home_pts NaN NaN NaN NaN NaN \n", | |
| "H2H_Away_pts NaN NaN NaN NaN NaN \n", | |
| "HT_Wins_by_MW -0.902016 NaN NaN NaN NaN \n", | |
| "HT_Losses_by_MW 0.842907 NaN NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN -0.898787 NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN 0.844142 NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN NaN \n", | |
| "\n", | |
| " Mean_home_goals Mean_away_goals Total_value_H \\\n", | |
| "HT_LP NaN NaN NaN \n", | |
| "AT_LP NaN NaN NaN \n", | |
| "DiffPts NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN \n", | |
| "Mean_home_goals 1.00000 NaN NaN \n", | |
| "Mean_away_goals NaN 1.0 NaN \n", | |
| "Total_value_H NaN NaN 1.0 \n", | |
| "Total_value_A NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN \n", | |
| "HTGD_by_MW NaN NaN NaN \n", | |
| "ATGD_by_MW NaN NaN NaN \n", | |
| "H2H_Home_pts NaN NaN NaN \n", | |
| "H2H_Away_pts NaN NaN NaN \n", | |
| "HT_Wins_by_MW NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN \n", | |
| "Goals_mean_diff 0.83074 NaN NaN \n", | |
| "\n", | |
| " Total_value_A H2H_Diff Total_Diff Age_diff LP_Diff \\\n", | |
| "HT_LP NaN NaN NaN NaN NaN \n", | |
| "AT_LP NaN NaN NaN NaN NaN \n", | |
| "DiffPts NaN NaN NaN NaN -0.879163 \n", | |
| "DiffFormPts NaN NaN NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN NaN NaN \n", | |
| "Mean_home_goals NaN NaN NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN NaN NaN \n", | |
| "Total_value_H NaN NaN NaN NaN NaN \n", | |
| "Total_value_A 1.0 NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN 1.000000 NaN NaN NaN \n", | |
| "Total_Diff NaN NaN 1.0 NaN NaN \n", | |
| "Age_diff NaN NaN NaN 1.0 NaN \n", | |
| "LP_Diff NaN NaN NaN NaN 1.000000 \n", | |
| "HTGD_by_MW NaN NaN NaN NaN NaN \n", | |
| "ATGD_by_MW NaN NaN NaN NaN NaN \n", | |
| "H2H_Home_pts NaN 0.874038 NaN NaN NaN \n", | |
| "H2H_Away_pts NaN -0.876916 NaN NaN NaN \n", | |
| "HT_Wins_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN NaN \n", | |
| "\n", | |
| " HTGD_by_MW ATGD_by_MW H2H_Home_pts H2H_Away_pts \\\n", | |
| "HT_LP -0.889973 NaN NaN NaN \n", | |
| "AT_LP NaN -0.889140 NaN NaN \n", | |
| "DiffPts NaN NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN NaN \n", | |
| "Mean_home_goals NaN NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN NaN \n", | |
| "Total_value_H NaN NaN NaN NaN \n", | |
| "Total_value_A NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN 0.874038 -0.876916 \n", | |
| "Total_Diff NaN NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN NaN \n", | |
| "HTGD_by_MW 1.000000 NaN NaN NaN \n", | |
| "ATGD_by_MW NaN 1.000000 NaN NaN \n", | |
| "H2H_Home_pts NaN NaN 1.000000 NaN \n", | |
| "H2H_Away_pts NaN NaN NaN 1.000000 \n", | |
| "HT_Wins_by_MW 0.892840 NaN NaN NaN \n", | |
| "HT_Losses_by_MW -0.887502 NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN 0.891313 NaN NaN \n", | |
| "AT_Losses_by_MW NaN -0.884921 NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN \n", | |
| "\n", | |
| " HT_Wins_by_MW HT_Losses_by_MW HT_Draws_by_MW \\\n", | |
| "HT_LP -0.902016 0.842907 NaN \n", | |
| "AT_LP NaN NaN NaN \n", | |
| "DiffPts NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN \n", | |
| "Mean_home_goals NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN \n", | |
| "Total_value_H NaN NaN NaN \n", | |
| "Total_value_A NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN \n", | |
| "HTGD_by_MW 0.892840 -0.887502 NaN \n", | |
| "ATGD_by_MW NaN NaN NaN \n", | |
| "H2H_Home_pts NaN NaN NaN \n", | |
| "H2H_Away_pts NaN NaN NaN \n", | |
| "HT_Wins_by_MW 1.000000 NaN NaN \n", | |
| "HT_Losses_by_MW NaN 1.000000 NaN \n", | |
| "HT_Draws_by_MW NaN NaN 1.0 \n", | |
| "AT_Wins_by_MW NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN \n", | |
| "\n", | |
| " AT_Wins_by_MW AT_Losses_by_MW AT_Draws_by_MW \\\n", | |
| "HT_LP NaN NaN NaN \n", | |
| "AT_LP -0.898787 0.844142 NaN \n", | |
| "DiffPts NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN \n", | |
| "Mean_home_goals NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN \n", | |
| "Total_value_H NaN NaN NaN \n", | |
| "Total_value_A NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN \n", | |
| "HTGD_by_MW NaN NaN NaN \n", | |
| "ATGD_by_MW 0.891313 -0.884921 NaN \n", | |
| "H2H_Home_pts NaN NaN NaN \n", | |
| "H2H_Away_pts NaN NaN NaN \n", | |
| "HT_Wins_by_MW NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN \n", | |
| "AT_Wins_by_MW 1.000000 NaN NaN \n", | |
| "AT_Losses_by_MW NaN 1.000000 NaN \n", | |
| "AT_Draws_by_MW NaN NaN 1.0 \n", | |
| "Goals_mean_diff NaN NaN NaN \n", | |
| "\n", | |
| " Goals_mean_diff \n", | |
| "HT_LP NaN \n", | |
| "AT_LP NaN \n", | |
| "DiffPts NaN \n", | |
| "DiffFormPts NaN \n", | |
| "DiffLP NaN \n", | |
| "Mean_home_goals 0.83074 \n", | |
| "Mean_away_goals NaN \n", | |
| "Total_value_H NaN \n", | |
| "Total_value_A NaN \n", | |
| "H2H_Diff NaN \n", | |
| "Total_Diff NaN \n", | |
| "Age_diff NaN \n", | |
| "LP_Diff NaN \n", | |
| "HTGD_by_MW NaN \n", | |
| "ATGD_by_MW NaN \n", | |
| "H2H_Home_pts NaN \n", | |
| "H2H_Away_pts NaN \n", | |
| "HT_Wins_by_MW NaN \n", | |
| "HT_Losses_by_MW NaN \n", | |
| "HT_Draws_by_MW NaN \n", | |
| "AT_Wins_by_MW NaN \n", | |
| "AT_Losses_by_MW NaN \n", | |
| "AT_Draws_by_MW NaN \n", | |
| "Goals_mean_diff 1.00000 " | |
| ] | |
| }, | |
| "execution_count": 27, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cols_selected = ['HT_LP', 'AT_LP', \n", | |
| " 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5',\n", | |
| " 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5',\n", | |
| " 'DiffPts', 'DiffFormPts', 'DiffLP', 'Mean_home_goals',\n", | |
| " 'Mean_away_goals', 'Total_value_H', 'Total_value_A', 'H2H_Diff',\n", | |
| " 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw', \n", | |
| " 'HTGD_by_MW', 'ATGD_by_MW', \"H2H_Home_pts\", \"H2H_Away_pts\", \n", | |
| " 'HT_Wins_by_MW', 'HT_Losses_by_MW', 'HT_Draws_by_MW', 'AT_Wins_by_MW', 'AT_Losses_by_MW', 'AT_Draws_by_MW',\n", | |
| " 'Goals_mean_diff']\n", | |
| "\n", | |
| "corr_matrix = df_final[cols_selected].corr()\n", | |
| "corr_matrix[(corr_matrix>0.8) | (corr_matrix< -0.8) ]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Usuniecie wysoce skorelowanych zmiennych ze soba, a pozostawienie tych, które mojej subiektywnej ocenie powinny pozostać pomimo sporej korelacji z innymi zmiennymi." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
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| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <th>Age_diff</th>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <th>HTGD_by_MW</th>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <th>HT_Wins_by_MW</th>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <th>AT_Wins_by_MW</th>\n", | |
| " <th>AT_Losses_by_MW</th>\n", | |
| " <th>AT_Draws_by_MW</th>\n", | |
| " <th>Goals_mean_diff</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>HT_LP</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889973</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.902016</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_LP</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889140</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.898787</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffPts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.879163</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>DiffLP</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Age_diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.879163</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HTGD_by_MW</th>\n", | |
| " <td>-0.889973</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.892840</td>\n", | |
| " <td>-0.887502</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.889140</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.891313</td>\n", | |
| " <td>-0.884921</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Wins_by_MW</th>\n", | |
| " <td>-0.902016</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.892840</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.887502</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Wins_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.898787</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.891313</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Losses_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-0.884921</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AT_Draws_by_MW</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Goals_mean_diff</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HT_LP AT_LP DiffPts DiffFormPts DiffLP \\\n", | |
| "HT_LP 1.000000 NaN NaN NaN NaN \n", | |
| "AT_LP NaN 1.000000 NaN NaN NaN \n", | |
| "DiffPts NaN NaN 1.000000 NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN 1.0 NaN \n", | |
| "DiffLP NaN NaN NaN NaN 1.0 \n", | |
| "Mean_away_goals NaN NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN NaN NaN \n", | |
| "LP_Diff NaN NaN -0.879163 NaN NaN \n", | |
| "HTGD_by_MW -0.889973 NaN NaN NaN NaN \n", | |
| "ATGD_by_MW NaN -0.889140 NaN NaN NaN \n", | |
| "HT_Wins_by_MW -0.902016 NaN NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN -0.898787 NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN NaN \n", | |
| "\n", | |
| " Mean_away_goals H2H_Diff Total_Diff Age_diff LP_Diff \\\n", | |
| "HT_LP NaN NaN NaN NaN NaN \n", | |
| "AT_LP NaN NaN NaN NaN NaN \n", | |
| "DiffPts NaN NaN NaN NaN -0.879163 \n", | |
| "DiffFormPts NaN NaN NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN NaN NaN \n", | |
| "Mean_away_goals 1.0 NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN 1.0 NaN NaN NaN \n", | |
| "Total_Diff NaN NaN 1.0 NaN NaN \n", | |
| "Age_diff NaN NaN NaN 1.0 NaN \n", | |
| "LP_Diff NaN NaN NaN NaN 1.000000 \n", | |
| "HTGD_by_MW NaN NaN NaN NaN NaN \n", | |
| "ATGD_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Wins_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN NaN NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN NaN \n", | |
| "\n", | |
| " HTGD_by_MW ATGD_by_MW HT_Wins_by_MW HT_Losses_by_MW \\\n", | |
| "HT_LP -0.889973 NaN -0.902016 NaN \n", | |
| "AT_LP NaN -0.889140 NaN NaN \n", | |
| "DiffPts NaN NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN NaN \n", | |
| "HTGD_by_MW 1.000000 NaN 0.892840 -0.887502 \n", | |
| "ATGD_by_MW NaN 1.000000 NaN NaN \n", | |
| "HT_Wins_by_MW 0.892840 NaN 1.000000 NaN \n", | |
| "HT_Losses_by_MW -0.887502 NaN NaN 1.000000 \n", | |
| "HT_Draws_by_MW NaN NaN NaN NaN \n", | |
| "AT_Wins_by_MW NaN 0.891313 NaN NaN \n", | |
| "AT_Losses_by_MW NaN -0.884921 NaN NaN \n", | |
| "AT_Draws_by_MW NaN NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN NaN \n", | |
| "\n", | |
| " HT_Draws_by_MW AT_Wins_by_MW AT_Losses_by_MW \\\n", | |
| "HT_LP NaN NaN NaN \n", | |
| "AT_LP NaN -0.898787 NaN \n", | |
| "DiffPts NaN NaN NaN \n", | |
| "DiffFormPts NaN NaN NaN \n", | |
| "DiffLP NaN NaN NaN \n", | |
| "Mean_away_goals NaN NaN NaN \n", | |
| "H2H_Diff NaN NaN NaN \n", | |
| "Total_Diff NaN NaN NaN \n", | |
| "Age_diff NaN NaN NaN \n", | |
| "LP_Diff NaN NaN NaN \n", | |
| "HTGD_by_MW NaN NaN NaN \n", | |
| "ATGD_by_MW NaN 0.891313 -0.884921 \n", | |
| "HT_Wins_by_MW NaN NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN NaN \n", | |
| "HT_Draws_by_MW 1.0 NaN NaN \n", | |
| "AT_Wins_by_MW NaN 1.000000 NaN \n", | |
| "AT_Losses_by_MW NaN NaN 1.000000 \n", | |
| "AT_Draws_by_MW NaN NaN NaN \n", | |
| "Goals_mean_diff NaN NaN NaN \n", | |
| "\n", | |
| " AT_Draws_by_MW Goals_mean_diff \n", | |
| "HT_LP NaN NaN \n", | |
| "AT_LP NaN NaN \n", | |
| "DiffPts NaN NaN \n", | |
| "DiffFormPts NaN NaN \n", | |
| "DiffLP NaN NaN \n", | |
| "Mean_away_goals NaN NaN \n", | |
| "H2H_Diff NaN NaN \n", | |
| "Total_Diff NaN NaN \n", | |
| "Age_diff NaN NaN \n", | |
| "LP_Diff NaN NaN \n", | |
| "HTGD_by_MW NaN NaN \n", | |
| "ATGD_by_MW NaN NaN \n", | |
| "HT_Wins_by_MW NaN NaN \n", | |
| "HT_Losses_by_MW NaN NaN \n", | |
| "HT_Draws_by_MW NaN NaN \n", | |
| "AT_Wins_by_MW NaN NaN \n", | |
| "AT_Losses_by_MW NaN NaN \n", | |
| "AT_Draws_by_MW 1.0 NaN \n", | |
| "Goals_mean_diff NaN 1.0 " | |
| ] | |
| }, | |
| "execution_count": 28, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cols_selected = ['HT_LP', 'AT_LP', \n", | |
| " 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5',\n", | |
| " 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5',\n", | |
| " 'DiffPts', 'DiffFormPts', 'DiffLP',\n", | |
| " 'Mean_away_goals', 'H2H_Diff',\n", | |
| " 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw', 'HTGD_by_MW', 'ATGD_by_MW',\n", | |
| " 'HT_Wins_by_MW', 'HT_Losses_by_MW', 'HT_Draws_by_MW', 'AT_Wins_by_MW', 'AT_Losses_by_MW', 'AT_Draws_by_MW',\n", | |
| " 'Goals_mean_diff']\n", | |
| "corr_matrix = df_final[cols_selected].corr()\n", | |
| "corr_matrix[(corr_matrix>0.85) | (corr_matrix< -0.85) ]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Finalny zbiór danych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HT_LP</th>\n", | |
| " <th>AT_LP</th>\n", | |
| " <th>HTWinStreak3</th>\n", | |
| " <th>HTWinStreak5</th>\n", | |
| " <th>HTLossStreak3</th>\n", | |
| " <th>HTLossStreak5</th>\n", | |
| " <th>ATWinStreak3</th>\n", | |
| " <th>ATWinStreak5</th>\n", | |
| " <th>ATLossStreak3</th>\n", | |
| " <th>ATLossStreak5</th>\n", | |
| " <th>DiffPts</th>\n", | |
| " <th>DiffFormPts</th>\n", | |
| " <th>DiffLP</th>\n", | |
| " <th>Mean_away_goals</th>\n", | |
| " <th>H2H_Diff</th>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <th>Age_diff</th>\n", | |
| " <th>LP_Diff</th>\n", | |
| " <th>draw</th>\n", | |
| " <th>HTGD_by_MW</th>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <th>HT_Wins_by_MW</th>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <th>AT_Wins_by_MW</th>\n", | |
| " <th>AT_Losses_by_MW</th>\n", | |
| " <th>AT_Draws_by_MW</th>\n", | |
| " <th>Goals_mean_diff</th>\n", | |
| " <th>HM1_D</th>\n", | |
| " <th>HM1_L</th>\n", | |
| " <th>HM1_W</th>\n", | |
| " <th>HM2_D</th>\n", | |
| " <th>HM2_L</th>\n", | |
| " <th>HM2_W</th>\n", | |
| " <th>AM1_D</th>\n", | |
| " <th>AM1_L</th>\n", | |
| " <th>AM1_W</th>\n", | |
| " <th>AM2_D</th>\n", | |
| " <th>AM2_L</th>\n", | |
| " <th>AM2_W</th>\n", | |
| " <th>HM3_D</th>\n", | |
| " <th>HM3_L</th>\n", | |
| " <th>HM3_W</th>\n", | |
| " <th>AM3_D</th>\n", | |
| " <th>AM3_L</th>\n", | |
| " <th>AM3_W</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>4045</th>\n", | |
| " <td>1</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>44</td>\n", | |
| " <td>6</td>\n", | |
| " <td>-7</td>\n", | |
| " <td>2.367347</td>\n", | |
| " <td>9</td>\n", | |
| " <td>3.464833</td>\n", | |
| " <td>4.1</td>\n", | |
| " <td>-9</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.303030</td>\n", | |
| " <td>0.030303</td>\n", | |
| " <td>0.843750</td>\n", | |
| " <td>0.062500</td>\n", | |
| " <td>0.093750</td>\n", | |
| " <td>0.250000</td>\n", | |
| " <td>0.250000</td>\n", | |
| " <td>0.500000</td>\n", | |
| " <td>0.132653</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4046</th>\n", | |
| " <td>7</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>9</td>\n", | |
| " <td>3</td>\n", | |
| " <td>-5</td>\n", | |
| " <td>1.580511</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1.662759</td>\n", | |
| " <td>2.6</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.300000</td>\n", | |
| " <td>0.100000</td>\n", | |
| " <td>0.448276</td>\n", | |
| " <td>0.275862</td>\n", | |
| " <td>0.275862</td>\n", | |
| " <td>0.275862</td>\n", | |
| " <td>0.241379</td>\n", | |
| " <td>0.482759</td>\n", | |
| " <td>0.551068</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4047</th>\n", | |
| " <td>14</td>\n", | |
| " <td>10</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-10</td>\n", | |
| " <td>-2</td>\n", | |
| " <td>9</td>\n", | |
| " <td>2.054737</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>0.257214</td>\n", | |
| " <td>2.8</td>\n", | |
| " <td>4</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-0.666667</td>\n", | |
| " <td>0.250000</td>\n", | |
| " <td>0.217391</td>\n", | |
| " <td>0.478261</td>\n", | |
| " <td>0.304348</td>\n", | |
| " <td>0.304348</td>\n", | |
| " <td>0.217391</td>\n", | |
| " <td>0.478261</td>\n", | |
| " <td>-1.379737</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4048</th>\n", | |
| " <td>18</td>\n", | |
| " <td>11</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-11</td>\n", | |
| " <td>2</td>\n", | |
| " <td>8</td>\n", | |
| " <td>1.170000</td>\n", | |
| " <td>-3</td>\n", | |
| " <td>0.170875</td>\n", | |
| " <td>2.4</td>\n", | |
| " <td>7</td>\n", | |
| " <td>0</td>\n", | |
| " <td>-0.685714</td>\n", | |
| " <td>-0.028571</td>\n", | |
| " <td>0.205882</td>\n", | |
| " <td>0.558824</td>\n", | |
| " <td>0.235294</td>\n", | |
| " <td>0.235294</td>\n", | |
| " <td>0.294118</td>\n", | |
| " <td>0.470588</td>\n", | |
| " <td>1.080000</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4049</th>\n", | |
| " <td>8</td>\n", | |
| " <td>12</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " <td>1.247525</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0.542967</td>\n", | |
| " <td>1.1</td>\n", | |
| " <td>-4</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0.066667</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.357143</td>\n", | |
| " <td>0.285714</td>\n", | |
| " <td>0.357143</td>\n", | |
| " <td>0.285714</td>\n", | |
| " <td>0.285714</td>\n", | |
| " <td>0.428571</td>\n", | |
| " <td>0.152475</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HT_LP AT_LP HTWinStreak3 HTWinStreak5 HTLossStreak3 HTLossStreak5 \\\n", | |
| "4045 1 10 0 0 0 0 \n", | |
| "4046 7 10 0 0 0 0 \n", | |
| "4047 14 10 0 0 0 0 \n", | |
| "4048 18 11 0 0 0 0 \n", | |
| "4049 8 12 0 0 0 0 \n", | |
| "\n", | |
| " ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 DiffPts \\\n", | |
| "4045 0 0 0 0 44 \n", | |
| "4046 0 0 0 0 9 \n", | |
| "4047 0 0 0 0 -10 \n", | |
| "4048 0 0 0 0 -11 \n", | |
| "4049 0 0 0 0 2 \n", | |
| "\n", | |
| " DiffFormPts DiffLP Mean_away_goals H2H_Diff Total_Diff Age_diff \\\n", | |
| "4045 6 -7 2.367347 9 3.464833 4.1 \n", | |
| "4046 3 -5 1.580511 0 1.662759 2.6 \n", | |
| "4047 -2 9 2.054737 -3 0.257214 2.8 \n", | |
| "4048 2 8 1.170000 -3 0.170875 2.4 \n", | |
| "4049 2 3 1.247525 0 0.542967 1.1 \n", | |
| "\n", | |
| " LP_Diff draw HTGD_by_MW ATGD_by_MW HT_Wins_by_MW HT_Losses_by_MW \\\n", | |
| "4045 -9 0 1.303030 0.030303 0.843750 0.062500 \n", | |
| "4046 -3 1 0.300000 0.100000 0.448276 0.275862 \n", | |
| "4047 4 0 -0.666667 0.250000 0.217391 0.478261 \n", | |
| "4048 7 0 -0.685714 -0.028571 0.205882 0.558824 \n", | |
| "4049 -4 1 0.066667 0.000000 0.357143 0.285714 \n", | |
| "\n", | |
| " HT_Draws_by_MW AT_Wins_by_MW AT_Losses_by_MW AT_Draws_by_MW \\\n", | |
| "4045 0.093750 0.250000 0.250000 0.500000 \n", | |
| "4046 0.275862 0.275862 0.241379 0.482759 \n", | |
| "4047 0.304348 0.304348 0.217391 0.478261 \n", | |
| "4048 0.235294 0.235294 0.294118 0.470588 \n", | |
| "4049 0.357143 0.285714 0.285714 0.428571 \n", | |
| "\n", | |
| " Goals_mean_diff HM1_D HM1_L HM1_W HM2_D HM2_L HM2_W AM1_D AM1_L \\\n", | |
| "4045 0.132653 0 1 0 0 0 1 1 0 \n", | |
| "4046 0.551068 0 1 0 0 1 0 1 0 \n", | |
| "4047 -1.379737 0 1 0 1 0 0 1 0 \n", | |
| "4048 1.080000 0 1 0 0 1 0 0 1 \n", | |
| "4049 0.152475 1 0 0 0 1 0 0 1 \n", | |
| "\n", | |
| " AM1_W AM2_D AM2_L AM2_W HM3_D HM3_L HM3_W AM3_D AM3_L AM3_W \n", | |
| "4045 0 0 1 0 0 0 1 1 0 0 \n", | |
| "4046 0 0 1 0 0 0 1 1 0 0 \n", | |
| "4047 0 1 0 0 0 0 1 0 0 1 \n", | |
| "4048 0 0 1 0 1 0 0 1 0 0 \n", | |
| "4049 0 1 0 0 1 0 0 1 0 0 " | |
| ] | |
| }, | |
| "execution_count": 29, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cols_final = ['HT_LP', 'AT_LP', \n", | |
| " 'HTWinStreak3', 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5',\n", | |
| " 'ATWinStreak3', 'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5',\n", | |
| " 'DiffPts', 'DiffFormPts', 'DiffLP',\n", | |
| " 'Mean_away_goals', 'H2H_Diff',\n", | |
| " 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw', 'HTGD_by_MW', 'ATGD_by_MW',\n", | |
| " 'HT_Wins_by_MW', 'HT_Losses_by_MW', 'HT_Draws_by_MW', 'AT_Wins_by_MW', 'AT_Losses_by_MW', 'AT_Draws_by_MW',\n", | |
| " 'Goals_mean_diff', 'HM1_D', 'HM1_L', 'HM1_W',\n", | |
| " 'HM2_D', 'HM2_L', 'HM2_W', 'AM1_D', 'AM1_L', 'AM1_W', 'AM2_D', 'AM2_L',\n", | |
| " 'AM2_W', 'HM3_D', 'HM3_L', 'HM3_W', 'AM3_D', 'AM3_L', 'AM3_W']\n", | |
| "data_final = df_final[cols_final]\n", | |
| "data_final.tail()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Over-sampling z wykorzystaniem SMOTE" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Przed przystąpieniem do implementacji modelu należy zrobić coś z nieproporcjonalnym zbiorem danych. Jak przedstawione zostałe wyżej, zmienna objaśniana przyjmuje wartośc '1' wyłącznie w około 22% przypadkach. W takim przypadku algorytm widzi dominację kategorii 0 nad 1, co może zakłucać działanie algorytmu. Innymi słowy algorytm może przyporzadkować wszystkie obserwacje jako kategorię 0, co będzie skutkowało skutecznościa predykcji na poziomie 77%, co jest stosunkowo dobrym wynikiem. \n", | |
| "Aby bardziej zobrazować problem, wyobraźmy sobie algorytm do wyszukiwania oszustw bankowych. W zbiorze danych takie zdarzenie miało miejsce w 5 przypadkach na 10000. Jeżeli algorytm przyporządkuje wszystkie przypadki jako te bez-oszustwa to skuteczność modelu będzie wynosić: 99.95%. Na pozór bardzo dobry model predykcyjny, a przecież algorytm nie znalazł ani jednego zdarzenia z oszustwem bankowym. OK, wracamy do naszego zadania znalezienia meczów kończących się remisem.\n", | |
| "\n", | |
| "Jednym z rozwiązań jakie adresowane jest do tego rodzajów problemu jest Over-sampling of minority class, czyli próbkowanie klasy z mniejszościową kategorią. \n", | |
| "Nie zagłębiając się w szczegóły, które możecie znaleźć w (http://rikunert.com/SMOTE_explained), funkcja SMOTE działa następująco:\n", | |
| "1. Tworzy sztuczne obserwacje na podstawie mniejszościowej kategorii w zbiorze danych (draw = 1) zamiast dodawać ich kopi.\n", | |
| "2. Losowo wybiera jeden z k-najbliższych-sąsiadów i wykorzystuje go do stworzenia podbnej lecz delikatnie zmodyfikowanej nowej obserwacji." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "X = data_final.loc[:, data_final.columns != 'draw']\n", | |
| "y = data_final.loc[:, data_final.columns == 'draw']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from imblearn.over_sampling import SMOTE\n", | |
| "\n", | |
| "os = SMOTE(random_state=0)\n", | |
| "\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n", | |
| "columns = X_train.columns" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "length of oversampled data is 4202\n", | |
| "Number of no draw in oversampled data 2101\n", | |
| "Number of drzaws 2101\n", | |
| "Proportion of no draws data in oversampled data is 0.5\n", | |
| "Proportion of draws data in oversampled data is 0.5\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "os_data_X, os_data_y = os.fit_sample(X_train, y_train)\n", | |
| "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n", | |
| "os_data_y= pd.DataFrame(data=os_data_y,columns=['draw'])\n", | |
| "# we can Check the numbers of our data\n", | |
| "print(\"length of oversampled data is \",len(os_data_X))\n", | |
| "print(\"Number of no draw in oversampled data\",len(os_data_y[os_data_y['draw']==0]))\n", | |
| "print(\"Number of drzaws\",len(os_data_y[os_data_y['draw']==1]))\n", | |
| "print(\"Proportion of no draws data in oversampled data is \",len(os_data_y[os_data_y['draw']==0])/len(os_data_X))\n", | |
| "print(\"Proportion of draws data in oversampled data is \",len(os_data_y[os_data_y['draw']==1])/len(os_data_X))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Stosunek kategorii w zmienej wynikowej 'draw' jest teraz idealny i równy 1/1.\n", | |
| "Proszę zwrócić również uwagę, że zbiór danych został zwiększony a kontretnie uzupełniony o stworzone nowe obserwacje." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Wybór zmiennych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wybór zmiennych dokonany zostanie za pomoca rekursywnej eliminacji zmiennych (Recursive Feature Elimination RFE)\n", | |
| "Działanie tego mechanizmu oparte jest na wielokrotnym tworzeniu modelu i wybieraniu z niego najlepszej lub nagorszej zmiennej pod względem jakości stworzonego modelu. Nastepnie zmienną tę eliminuje się (lub dodaje w zalezności od wybranej techniki - w naszym przypadku jest to eliminacja) i powtarza się konstrukcję modelu oraz oszacowuje się jakość nowego modelu.Czynnośc tę wykonuje się aż do momentu, kiedy przeanalizowane zostaną wszystkie zmienne w zbiorze danych.\n", | |
| "Głównym celem działania RFE jest wybór zmienych poprzez wybór modelu z jak najmniejszą ilością zmiennych." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "HT_LP False\n", | |
| "AT_LP True\n", | |
| "HTWinStreak3 True\n", | |
| "HTWinStreak5 True\n", | |
| "HTLossStreak3 True\n", | |
| "HTLossStreak5 True\n", | |
| "ATWinStreak3 True\n", | |
| "ATWinStreak5 True\n", | |
| "ATLossStreak3 True\n", | |
| "ATLossStreak5 True\n", | |
| "DiffPts False\n", | |
| "DiffFormPts False\n", | |
| "DiffLP False\n", | |
| "Mean_away_goals True\n", | |
| "H2H_Diff False\n", | |
| "Total_Diff True\n", | |
| "Age_diff False\n", | |
| "LP_Diff False\n", | |
| "HTGD_by_MW False\n", | |
| "ATGD_by_MW True\n", | |
| "HT_Wins_by_MW False\n", | |
| "HT_Losses_by_MW True\n", | |
| "HT_Draws_by_MW True\n", | |
| "AT_Wins_by_MW True\n", | |
| "AT_Losses_by_MW True\n", | |
| "AT_Draws_by_MW True\n", | |
| "Goals_mean_diff False\n", | |
| "HM1_D False\n", | |
| "HM1_L False\n", | |
| "HM1_W True\n", | |
| "HM2_D False\n", | |
| "HM2_L True\n", | |
| "HM2_W False\n", | |
| "AM1_D False\n", | |
| "AM1_L True\n", | |
| "AM1_W False\n", | |
| "AM2_D True\n", | |
| "AM2_L True\n", | |
| "AM2_W False\n", | |
| "HM3_D True\n", | |
| "HM3_L True\n", | |
| "HM3_W True\n", | |
| "AM3_D False\n", | |
| "AM3_L False\n", | |
| "AM3_W False\n", | |
| "dtype: bool" | |
| ] | |
| }, | |
| "execution_count": 33, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data_final_vars=data_final.columns.values.tolist()\n", | |
| "y=['draw']\n", | |
| "X=[i for i in data_final_vars if i not in y]\n", | |
| "\n", | |
| "from sklearn.feature_selection import RFE\n", | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "\n", | |
| "logreg = LogisticRegression(solver = 'lbfgs')\n", | |
| "\n", | |
| "rfe = RFE(logreg, 25)\n", | |
| "rfe = rfe.fit(os_data_X, os_data_y.values.ravel())\n", | |
| "#print(rfe.support_)\n", | |
| "#print(rfe.ranking_)\n", | |
| "\n", | |
| "mask = pd.Series(rfe.support_)\n", | |
| "mask.index = os_data_X.columns\n", | |
| "mask" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 34, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "X=os_data_X.loc[:, mask]\n", | |
| "y=os_data_y['draw']" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Implementacja modelu regresji logistycznej" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 35, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Warning: Maximum number of iterations has been exceeded.\n", | |
| " Current function value: 0.665748\n", | |
| " Iterations: 35\n", | |
| " Results: Logit\n", | |
| "=================================================================\n", | |
| "Model: Logit Pseudo R-squared: 0.040 \n", | |
| "Dependent Variable: draw AIC: 5642.9465 \n", | |
| "Date: 2019-05-11 20:44 BIC: 5795.1861 \n", | |
| "No. Observations: 4202 Log-Likelihood: -2797.5 \n", | |
| "Df Model: 23 LL-Null: -2912.6 \n", | |
| "Df Residuals: 4178 LLR p-value: 4.0484e-36\n", | |
| "Converged: 0.0000 Scale: 1.0000 \n", | |
| "No. Iterations: 35.0000 \n", | |
| "-----------------------------------------------------------------\n", | |
| " Coef. Std.Err. z P>|z| [0.025 0.975]\n", | |
| "-----------------------------------------------------------------\n", | |
| "AT_LP 0.0570 0.0168 3.4006 0.0007 0.0242 0.0899\n", | |
| "HTWinStreak3 -1.0022 0.2052 -4.8837 0.0000 -1.4044 -0.6000\n", | |
| "HTWinStreak5 -0.9574 0.4930 -1.9419 0.0522 -1.9237 0.0089\n", | |
| "HTLossStreak3 0.2136 0.1623 1.3159 0.1882 -0.1045 0.5317\n", | |
| "HTLossStreak5 0.6287 0.3248 1.9353 0.0530 -0.0080 1.2653\n", | |
| "ATWinStreak3 -0.3800 0.1685 -2.2552 0.0241 -0.7102 -0.0497\n", | |
| "ATWinStreak5 -0.0840 0.3050 -0.2753 0.7831 -0.6818 0.5139\n", | |
| "ATLossStreak3 -0.2736 0.1770 -1.5462 0.1221 -0.6205 0.0732\n", | |
| "ATLossStreak5 0.2562 0.3365 0.7615 0.4464 -0.4033 0.9157\n", | |
| "Mean_away_goals -0.1254 0.0663 -1.8913 0.0586 -0.2553 0.0046\n", | |
| "Total_Diff -0.1638 0.0258 -6.3429 0.0000 -0.2144 -0.1132\n", | |
| "ATGD_by_MW 0.7157 0.1653 4.3287 0.0000 0.3917 1.0398\n", | |
| "HT_Losses_by_MW -0.7743 0.2890 -2.6796 0.0074 -1.3407 -0.2079\n", | |
| "HT_Draws_by_MW 0.9712 0.3371 2.8811 0.0040 0.3105 1.6319\n", | |
| "AT_Wins_by_MW -1.1198 nan nan nan nan nan\n", | |
| "AT_Losses_by_MW 0.3817 nan nan nan nan nan\n", | |
| "AT_Draws_by_MW 0.6799 nan nan nan nan nan\n", | |
| "HM1_W 0.2013 0.0769 2.6161 0.0089 0.0505 0.3520\n", | |
| "HM2_L 0.1226 0.0765 1.6025 0.1090 -0.0274 0.2726\n", | |
| "AM1_L -0.1237 0.0791 -1.5624 0.1182 -0.2788 0.0315\n", | |
| "AM2_D -0.2387 0.0955 -2.4983 0.0125 -0.4259 -0.0514\n", | |
| "AM2_L 0.0356 0.0853 0.4177 0.6762 -0.1316 0.2028\n", | |
| "HM3_D -0.0881 nan nan nan nan nan\n", | |
| "HM3_L -0.1408 nan nan nan nan nan\n", | |
| "HM3_W 0.1706 nan nan nan nan nan\n", | |
| "=================================================================\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "D:\\Programy\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", | |
| " \"Check mle_retvals\", ConvergenceWarning)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import statsmodels.api as sm\n", | |
| "logit_model=sm.Logit(y,X)\n", | |
| "result=logit_model.fit()\n", | |
| "print(result.summary2())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 36, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def results_summary_to_dataframe(results):\n", | |
| " pvals = results.pvalues\n", | |
| " coeff = results.params\n", | |
| " conf_lower = results.conf_int()[0]\n", | |
| " conf_higher = results.conf_int()[1]\n", | |
| "\n", | |
| " results_df = pd.DataFrame({\"pvals\":pvals,\n", | |
| " \"coeff\":coeff,\n", | |
| " \"conf_lower\":conf_lower,\n", | |
| " \"conf_higher\":conf_higher\n", | |
| " })\n", | |
| "\n", | |
| " \n", | |
| " results_df = results_df[[\"coeff\",\"pvals\",\"conf_lower\",\"conf_higher\"]]\n", | |
| " return results_df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wybór zmiennych istotnych statystycznie." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>coeff</th>\n", | |
| " <th>pvals</th>\n", | |
| " <th>conf_lower</th>\n", | |
| " <th>conf_higher</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>AT_LP</th>\n", | |
| " <td>0.057033</td>\n", | |
| " <td>6.725038e-04</td>\n", | |
| " <td>0.024161</td>\n", | |
| " <td>0.089905</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HTWinStreak3</th>\n", | |
| " <td>-1.002174</td>\n", | |
| " <td>1.041348e-06</td>\n", | |
| " <td>-1.404377</td>\n", | |
| " <td>-0.599970</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ATWinStreak3</th>\n", | |
| " <td>-0.379984</td>\n", | |
| " <td>2.411893e-02</td>\n", | |
| " <td>-0.710218</td>\n", | |
| " <td>-0.049749</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Total_Diff</th>\n", | |
| " <td>-0.163791</td>\n", | |
| " <td>2.254454e-10</td>\n", | |
| " <td>-0.214403</td>\n", | |
| " <td>-0.113180</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ATGD_by_MW</th>\n", | |
| " <td>0.715723</td>\n", | |
| " <td>1.499815e-05</td>\n", | |
| " <td>0.391656</td>\n", | |
| " <td>1.039789</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Losses_by_MW</th>\n", | |
| " <td>-0.774333</td>\n", | |
| " <td>7.371626e-03</td>\n", | |
| " <td>-1.340717</td>\n", | |
| " <td>-0.207950</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HT_Draws_by_MW</th>\n", | |
| " <td>0.971206</td>\n", | |
| " <td>3.963256e-03</td>\n", | |
| " <td>0.310504</td>\n", | |
| " <td>1.631907</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>HM1_W</th>\n", | |
| " <td>0.201262</td>\n", | |
| " <td>8.894282e-03</td>\n", | |
| " <td>0.050477</td>\n", | |
| " <td>0.352046</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>AM2_D</th>\n", | |
| " <td>-0.238650</td>\n", | |
| " <td>1.247844e-02</td>\n", | |
| " <td>-0.425874</td>\n", | |
| " <td>-0.051426</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " coeff pvals conf_lower conf_higher\n", | |
| "AT_LP 0.057033 6.725038e-04 0.024161 0.089905\n", | |
| "HTWinStreak3 -1.002174 1.041348e-06 -1.404377 -0.599970\n", | |
| "ATWinStreak3 -0.379984 2.411893e-02 -0.710218 -0.049749\n", | |
| "Total_Diff -0.163791 2.254454e-10 -0.214403 -0.113180\n", | |
| "ATGD_by_MW 0.715723 1.499815e-05 0.391656 1.039789\n", | |
| "HT_Losses_by_MW -0.774333 7.371626e-03 -1.340717 -0.207950\n", | |
| "HT_Draws_by_MW 0.971206 3.963256e-03 0.310504 1.631907\n", | |
| "HM1_W 0.201262 8.894282e-03 0.050477 0.352046\n", | |
| "AM2_D -0.238650 1.247844e-02 -0.425874 -0.051426" | |
| ] | |
| }, | |
| "execution_count": 37, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df = results_summary_to_dataframe(result)\n", | |
| "\n", | |
| "cols_to_model = df.loc[df[\"pvals\"] < 0.05].index\n", | |
| "df.loc[df[\"pvals\"] < 0.05]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Szczegóły dotyczące analizy wyników regresji logistycznej możecie znaleźć pod adresem: https://analizadanychwpilce.com/2018/09/15/przewidywanie-wyniku-spotkania-z-wykorzystaniem-regresji-logistycznej/.\n", | |
| "Na podstawie p-value (kolumna P>|z|) najeży wybrać zmienne które są istotne statystycznie.\n", | |
| "W naszym przypadku poziom ufnoścu przyjeliśmy równy 5% czyli wsystkie zmienne z p-value powyżej 0.05 należy odrzucić z modelu." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Istotność zmiennych" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.feature_selection import SelectKBest\n", | |
| "from sklearn.feature_selection import f_classif\n", | |
| "# feature extraction\n", | |
| "test = SelectKBest(score_func=f_classif, k=4)\n", | |
| "fit = test.fit(X, y)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 39, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " Specs Score\n", | |
| "1 HTWinStreak3 58.184088\n", | |
| "10 Total_Diff 42.894973\n", | |
| "13 HT_Draws_by_MW 28.157140\n", | |
| "2 HTWinStreak5 25.750660\n", | |
| "16 AT_Draws_by_MW 20.602829\n", | |
| "5 ATWinStreak3 15.096748\n", | |
| "14 AT_Wins_by_MW 5.270021\n", | |
| "18 HM2_L 4.720474\n", | |
| "4 HTLossStreak5 4.374071\n", | |
| "0 AT_LP 3.400747\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "dfscores = pd.DataFrame(fit.scores_)\n", | |
| "dfcolumns = pd.DataFrame(X.columns)\n", | |
| "#concat two dataframes for better visualization \n", | |
| "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n", | |
| "featureScores.columns = ['Specs','Score'] #naming the dataframe columns\n", | |
| "print(featureScores.nlargest(10,'Score')) #print 10 best features" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 40, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Optimization terminated successfully.\n", | |
| " Current function value: 0.672864\n", | |
| " Iterations 5\n", | |
| " Results: Logit\n", | |
| "=================================================================\n", | |
| "Model: Logit Pseudo R-squared: 0.029 \n", | |
| "Dependent Variable: draw AIC: 5672.7458 \n", | |
| "Date: 2019-05-11 20:44 BIC: 5729.8356 \n", | |
| "No. Observations: 4202 Log-Likelihood: -2827.4 \n", | |
| "Df Model: 8 LL-Null: -2912.6 \n", | |
| "Df Residuals: 4193 LLR p-value: 1.0314e-32\n", | |
| "Converged: 1.0000 Scale: 1.0000 \n", | |
| "No. Iterations: 5.0000 \n", | |
| "-----------------------------------------------------------------\n", | |
| " Coef. Std.Err. z P>|z| [0.025 0.975]\n", | |
| "-----------------------------------------------------------------\n", | |
| "AT_LP 0.0648 0.0107 6.0483 0.0000 0.0438 0.0858\n", | |
| "HTWinStreak3 -1.1159 0.1813 -6.1547 0.0000 -1.4713 -0.7606\n", | |
| "ATWinStreak3 -0.5681 0.1442 -3.9388 0.0001 -0.8509 -0.2854\n", | |
| "Total_Diff -0.1761 0.0232 -7.6014 0.0000 -0.2216 -0.1307\n", | |
| "ATGD_by_MW 0.4158 0.0942 4.4156 0.0000 0.2312 0.6004\n", | |
| "HT_Losses_by_MW -1.0459 0.1833 -5.7060 0.0000 -1.4051 -0.6866\n", | |
| "HT_Draws_by_MW 0.4476 0.2486 1.8008 0.0717 -0.0396 0.9349\n", | |
| "HM1_W 0.0888 0.0709 1.2525 0.2104 -0.0501 0.2276\n", | |
| "AM2_D -0.1870 0.0774 -2.4150 0.0157 -0.3387 -0.0352\n", | |
| "=================================================================\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "cols_to_df = [\"HTWinStreak3\", \"ATWinStreak3\", \"Mean_away_goals\", \"Total_Diff\", \"HTGD_by_MW\", \n", | |
| " \"HT_Draws_by_MW\", \"AT_Draws_by_MW\", \"Goals_mean_diff\", \"HM2_L\", \"AM2_D\"]\n", | |
| "\n", | |
| "X=os_data_X[cols_to_model]\n", | |
| "y=os_data_y['draw']\n", | |
| "logit_model=sm.Logit(y,X)\n", | |
| "result=logit_model.fit()\n", | |
| "print(result.summary2())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Cross walidacja" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 41, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[0.51711027 0.57414449 0.5513308 0.5513308 0.55893536 0.58587786\n", | |
| " 0.59923664 0.59732824]\n", | |
| "0.5669118073897773\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from sklearn import linear_model\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "scores = cross_val_score(linear_model.LogisticRegression(), X, y,\n", | |
| "scoring='accuracy', cv=8)\n", | |
| "print(scores)\n", | |
| "print(scores.mean())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Weryfikacja modelu " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 42, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Accuracy of logistic regression classifier on test set: 0.56\n", | |
| "[[283 368]\n", | |
| " [191 419]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from sklearn.linear_model import LogisticRegression\n", | |
| "from sklearn import metrics\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n", | |
| "logreg = LogisticRegression()\n", | |
| "logreg.fit(X_train, y_train)\n", | |
| "\n", | |
| "y_pred = logreg.predict(X_test)\n", | |
| "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))\n", | |
| "\n", | |
| "from sklearn.metrics import confusion_matrix\n", | |
| "confusion_matrix = confusion_matrix(y_test, y_pred)\n", | |
| "print(confusion_matrix)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wiecej o confusion matrix: https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.60 0.43 0.50 651\n", | |
| " 1 0.53 0.69 0.60 610\n", | |
| "\n", | |
| " micro avg 0.56 0.56 0.56 1261\n", | |
| " macro avg 0.56 0.56 0.55 1261\n", | |
| "weighted avg 0.57 0.56 0.55 1261\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "from sklearn.metrics import classification_report\n", | |
| "print(classification_report(y_test, y_pred))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Przeliczanie dokładności, skutczności i miary skutczności F1.\n", | |
| "\n", | |
| "Precision (dokładnośc) jest to stosunek tp/ (tp+fp) gdzi tp jest ilością prawdziwie sklasfyikowanych zdarzeń (draw = 1)(u nas 419) a fp to błędnie sklasyfikowane rmisy kiedy w rzczywistości nie miały miejsca (u nas 319). Tak napradę to interesuje nas wartośc przy 1, czyli równa 0.55. W takim stopniu model prawidłowo sklasyfikował zdarzenia.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 44, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from sklearn.metrics import roc_auc_score\n", | |
| "from sklearn.metrics import roc_curve\n", | |
| "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n", | |
| "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n", | |
| "plt.figure()\n", | |
| "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n", | |
| "plt.plot([0, 1], [0, 1],'r--')\n", | |
| "plt.xlim([0.0, 1.0])\n", | |
| "plt.ylim([0.0, 1.05])\n", | |
| "plt.xlabel('False Positive Rate')\n", | |
| "plt.ylabel('True Positive Rate')\n", | |
| "plt.title('Receiver operating characteristic')\n", | |
| "plt.legend(loc=\"lower right\")\n", | |
| "plt.savefig('Log_ROC')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Nowe dane" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 45, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>HomeTeam</th>\n", | |
| " <th>AwayTeam</th>\n", | |
| " <th>0</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Atalanta</td>\n", | |
| " <td>Genoa</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Cagliari</td>\n", | |
| " <td>Lazio</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Fiorentina</td>\n", | |
| " <td>Milan</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>Torino</td>\n", | |
| " <td>Sassuolo</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Frosinone</td>\n", | |
| " <td>Udinese</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Sampdoria</td>\n", | |
| " <td>Empoli</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Spal</td>\n", | |
| " <td>Napoli</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>Roma</td>\n", | |
| " <td>Juventus</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>Bologna</td>\n", | |
| " <td>Parma</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>Inter</td>\n", | |
| " <td>Chievo</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " HomeTeam AwayTeam 0\n", | |
| "0 Atalanta Genoa 1\n", | |
| "1 Cagliari Lazio 1\n", | |
| "2 Fiorentina Milan 1\n", | |
| "3 Torino Sassuolo 1\n", | |
| "4 Frosinone Udinese 1\n", | |
| "5 Sampdoria Empoli 0\n", | |
| "6 Spal Napoli 1\n", | |
| "7 Roma Juventus 1\n", | |
| "8 Bologna Parma 0\n", | |
| "9 Inter Chievo 0" | |
| ] | |
| }, | |
| "execution_count": 45, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "loc = dics + \":/data_football/seriaa/\"\n", | |
| "file = \"predict\"\n", | |
| "predict = pd.read_csv(loc + 'predict.csv', index_col = 0)\n", | |
| "loc_to_functions = dics + \":/data_football/\"\n", | |
| "league = \"seriaa\"\n", | |
| "\n", | |
| "import os, re\n", | |
| "os.chdir(loc_to_functions)\n", | |
| "\n", | |
| "import functions_laliga as f1\n", | |
| "\n", | |
| "\n", | |
| "df = f1.dataset_prepering(loc, file, league)\n", | |
| "\n", | |
| "df = df[cols_to_model ]\n", | |
| "y_pred_new = logreg.predict(df)\n", | |
| "\n", | |
| "df = f1.dataset_prepering(loc, file, league)\n", | |
| "\n", | |
| "\n", | |
| "final = pd.concat([df[[\"HomeTeam\", \"AwayTeam\"]].reset_index(drop=True), pd.DataFrame(y_pred_new)], axis=1)\n", | |
| "final" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.1" | |
| }, | |
| "widgets": { | |
| "application/vnd.jupyter.widget-state+json": { | |
| "state": {}, | |
| "version_major": 2, | |
| "version_minor": 0 | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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