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szydlinho/remis.ipynb

Created May 10, 2019 08:29
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remis.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wczytanie potrzebnych bibliotek"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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 potrzbenego do ścieżki do pliku z danymi"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'D'"
]
},
"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",
"\n",
"dics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wczytanie oraz prezentacja zbioru danych"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>Date</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>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>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</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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3755</th>\n",
" <td>2019-04-24</td>\n",
" <td>Espanol</td>\n",
" <td>Celta</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>D</td>\n",
" <td>39.0</td>\n",
" <td>47.0</td>\n",
" <td>48.0</td>\n",
" <td>56.0</td>\n",
" <td>42</td>\n",
" <td>35</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>34</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>19</td>\n",
" <td>10</td>\n",
" <td>15</td>\n",
" <td>DWWDL</td>\n",
" <td>WLWDW</td>\n",
" <td>8.0</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>-9.0</td>\n",
" <td>-9.0</td>\n",
" <td>7</td>\n",
" <td>-2.0</td>\n",
" <td>-2.0</td>\n",
" <td>WDD</td>\n",
" <td>LDD</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>376</td>\n",
" <td>0.965517</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.400000</td>\n",
" <td>1.240000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3756</th>\n",
" <td>2019-04-24</td>\n",
" <td>Levante</td>\n",
" <td>Betis</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>H</td>\n",
" <td>48.0</td>\n",
" <td>39.0</td>\n",
" <td>61.0</td>\n",
" <td>46.0</td>\n",
" <td>34</td>\n",
" <td>43</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>L</td>\n",
" <td>D</td>\n",
" <td>34</td>\n",
" <td>15.0</td>\n",
" <td>6.0</td>\n",
" <td>19</td>\n",
" <td>16</td>\n",
" <td>9</td>\n",
" <td>DLDLD</td>\n",
" <td>LLWLD</td>\n",
" <td>3.0</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>-13.0</td>\n",
" <td>-7.0</td>\n",
" <td>-9</td>\n",
" <td>-1.0</td>\n",
" <td>9.0</td>\n",
" <td>LLW</td>\n",
" <td>WWL</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>376</td>\n",
" <td>0.965517</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.400000</td>\n",
" <td>1.240000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3757</th>\n",
" <td>2019-04-25</td>\n",
" <td>Sevilla</td>\n",
" <td>Vallecano</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.0</td>\n",
" <td>35.0</td>\n",
" <td>41.0</td>\n",
" <td>53.0</td>\n",
" <td>52</td>\n",
" <td>28</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>W</td>\n",
" <td>L</td>\n",
" <td>D</td>\n",
" <td>34</td>\n",
" <td>7.0</td>\n",
" <td>16.0</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>19</td>\n",
" <td>LWWWL</td>\n",
" <td>DLWLD</td>\n",
" <td>9.0</td>\n",
" <td>5.0</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>-18.0</td>\n",
" <td>24</td>\n",
" <td>4.0</td>\n",
" <td>-9.0</td>\n",
" <td>WDW</td>\n",
" <td>LDL</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>376</td>\n",
" <td>1.655172</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.400000</td>\n",
" <td>1.240000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3758</th>\n",
" <td>2019-04-25</td>\n",
" <td>Sociedad</td>\n",
" <td>Villarreal</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37.0</td>\n",
" <td>41.0</td>\n",
" <td>40.0</td>\n",
" <td>44.0</td>\n",
" <td>41</td>\n",
" <td>36</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>L</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>34</td>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>14</td>\n",
" <td>LDLWD</td>\n",
" <td>WWLDL</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>-3.0</td>\n",
" <td>-3.0</td>\n",
" <td>5</td>\n",
" <td>-2.0</td>\n",
" <td>7.0</td>\n",
" <td>WLW</td>\n",
" <td>LWL</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>376</td>\n",
" <td>0.965517</td>\n",
" <td>0.816327</td>\n",
" <td>1.000000</td>\n",
" <td>0.858896</td>\n",
" <td>1.202454</td>\n",
" <td>1.012245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3759</th>\n",
" <td>2019-04-25</td>\n",
" <td>Getafe</td>\n",
" <td>Real Madrid</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>43.0</td>\n",
" <td>57.0</td>\n",
" <td>27.0</td>\n",
" <td>38.0</td>\n",
" <td>54</td>\n",
" <td>64</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>34</td>\n",
" <td>8.0</td>\n",
" <td>3.0</td>\n",
" <td>19</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>WDWDL</td>\n",
" <td>WDWLW</td>\n",
" <td>8.0</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>16.0</td>\n",
" <td>19.0</td>\n",
" <td>-10</td>\n",
" <td>-2.0</td>\n",
" <td>5.0</td>\n",
" <td>LLL</td>\n",
" <td>WWW</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>376</td>\n",
" <td>1.103448</td>\n",
" <td>3.469388</td>\n",
" <td>1.428571</td>\n",
" <td>0.613497</td>\n",
" <td>0.981595</td>\n",
" <td>6.145773</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date HomeTeam AwayTeam FTHG FTAG FTR HTGS ATGS HTGC \\\n",
"3755 2019-04-24 Espanol Celta 1.0 1.0 D 39.0 47.0 48.0 \n",
"3756 2019-04-24 Levante Betis 4.0 0.0 H 48.0 39.0 61.0 \n",
"3757 2019-04-25 Sevilla Vallecano NaN NaN NaN 50.0 35.0 41.0 \n",
"3758 2019-04-25 Sociedad Villarreal NaN NaN NaN 37.0 41.0 40.0 \n",
"3759 2019-04-25 Getafe Real Madrid NaN NaN NaN 43.0 57.0 27.0 \n",
"\n",
" ATGC HTP ATP HM1 HM2 HM3 HM4 HM5 AM1 AM2 AM3 AM4 AM5 MW HomeTeamLP \\\n",
"3755 56.0 42 35 D W W D L W L W D W 34 11.0 \n",
"3756 46.0 34 43 D L D L D L L W L D 34 15.0 \n",
"3757 53.0 52 28 L W W W L D L W L D 34 7.0 \n",
"3758 44.0 41 36 L D L W D W W L D L 34 12.0 \n",
"3759 38.0 54 64 W D W D L W D W L W 34 8.0 \n",
"\n",
" AwayTeamLP Sezon HT_LP AT_LP HTFormPtsStr ATFormPtsStr HTFormPts \\\n",
"3755 13.0 19 10 15 DWWDL WLWDW 8.0 \n",
"3756 6.0 19 16 9 DLDLD LLWLD 3.0 \n",
"3757 16.0 19 5 19 LWWWL DLWLD 9.0 \n",
"3758 5.0 19 11 14 LDLWD WWLDL 5.0 \n",
"3759 3.0 19 4 3 WDWDL WDWLW 8.0 \n",
"\n",
" ATFormPts HTWinStreak3 HTWinStreak5 HTLossStreak3 HTLossStreak5 \\\n",
"3755 10.0 0 0 0 0 \n",
"3756 4.0 0 0 0 0 \n",
"3757 5.0 0 0 0 0 \n",
"3758 7.0 0 0 0 0 \n",
"3759 10.0 0 0 0 0 \n",
"\n",
" ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 HTGD ATGD \\\n",
"3755 0 0 0 0 -9.0 -9.0 \n",
"3756 0 0 0 0 -13.0 -7.0 \n",
"3757 0 0 0 0 9.0 -18.0 \n",
"3758 0 0 0 0 -3.0 -3.0 \n",
"3759 0 0 0 0 16.0 19.0 \n",
"\n",
" DiffPts DiffFormPts DiffLP H2H_Home H2H_Away H2H_Home_pts \\\n",
"3755 7 -2.0 -2.0 WDD LDD 5 \n",
"3756 -9 -1.0 9.0 LLW WWL 3 \n",
"3757 24 4.0 -9.0 WDW LDL 7 \n",
"3758 5 -2.0 7.0 WLW LWL 6 \n",
"3759 -10 -2.0 5.0 LLL WWW 0 \n",
"\n",
" H2H_Away_pts temp Off_H Off_A Deff_H Deff_A \\\n",
"3755 2 376 0.965517 1.000000 1.000000 1.000000 \n",
"3756 6 376 0.965517 1.000000 1.000000 1.000000 \n",
"3757 1 376 1.655172 1.000000 1.000000 1.000000 \n",
"3758 3 376 0.965517 0.816327 1.000000 0.858896 \n",
"3759 9 376 1.103448 3.469388 1.428571 0.613497 \n",
"\n",
" Mean_home_goals Mean_away_goals \n",
"3755 1.400000 1.240000 \n",
"3756 1.400000 1.240000 \n",
"3757 2.400000 1.240000 \n",
"3758 1.202454 1.012245 \n",
"3759 0.981595 6.145773 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_columns = None\n",
"loc = dics + \":/data_football/laliga/\"\n",
"file = \"laliga_final_log\"\n",
"dataset = pd.read_csv(loc + file+ '.csv', index_col = 0)\n",
"dataset.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zmienne wykorzystane w zbiorze danych opisanych zostało w:\n",
"https://analizadanychwpilce.com/2018/09/15/przewidywanie-wyniku-spotkania-z-wykorzystaniem-regresji-logistycznej/\n",
"oraz\n",
"https://analizadanychwpilce.com/2019/02/03/przewidywany-wynik-na-podstawie-rozkladu-poissona/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wyrzucenie wierszy w których brak danych dla odpowiednich ze zmiennych"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dataset = dataset.loc[(dataset[\"HM1\"] != \"M\") & (dataset[\"AM1\"] != \"M\") & (dataset[\"AM2\"] != \"M\") &\n",
" (dataset[\"HM2\"] != \"M\") & (dataset[\"HM3\"] != \"M\") & (dataset[\"AM3\"] != \"M\") ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dodanie dodatkowych zmiennych"
]
},
{
"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>Date</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>...</th>\n",
" <th>Club_x</th>\n",
" <th>Age_H</th>\n",
" <th>Foreign_H</th>\n",
" <th>Total_value_H</th>\n",
" <th>Market_value_H</th>\n",
" <th>Club_y</th>\n",
" <th>Age_A</th>\n",
" <th>Foreign_A</th>\n",
" <th>Total_value_A</th>\n",
" <th>Market_value_A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4155</th>\n",
" <td>2018-09-29</td>\n",
" <td>Real Madrid</td>\n",
" <td>Ath Madrid</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>D</td>\n",
" <td>10.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>Real Madrid</td>\n",
" <td>26.5</td>\n",
" <td>4</td>\n",
" <td>969.0</td>\n",
" <td>40.38</td>\n",
" <td>Ath Madrid</td>\n",
" <td>27.2</td>\n",
" <td>5</td>\n",
" <td>807.0</td>\n",
" <td>40.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4156</th>\n",
" <td>2018-10-20</td>\n",
" <td>Villarreal</td>\n",
" <td>Ath Madrid</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>D</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>Villarreal</td>\n",
" <td>28.7</td>\n",
" <td>15</td>\n",
" <td>196.1</td>\n",
" <td>7.84</td>\n",
" <td>Ath Madrid</td>\n",
" <td>27.2</td>\n",
" <td>5</td>\n",
" <td>807.0</td>\n",
" <td>40.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4157</th>\n",
" <td>2019-02-03</td>\n",
" <td>Betis</td>\n",
" <td>Ath Madrid</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>H</td>\n",
" <td>26.0</td>\n",
" <td>31.0</td>\n",
" <td>23.0</td>\n",
" <td>13.0</td>\n",
" <td>...</td>\n",
" <td>Betis</td>\n",
" <td>27.8</td>\n",
" <td>15</td>\n",
" <td>180.5</td>\n",
" <td>8.60</td>\n",
" <td>Ath Madrid</td>\n",
" <td>27.2</td>\n",
" <td>5</td>\n",
" <td>807.0</td>\n",
" <td>40.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4158</th>\n",
" <td>2019-04-06</td>\n",
" <td>Barcelona</td>\n",
" <td>Ath Madrid</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>H</td>\n",
" <td>78.0</td>\n",
" <td>44.0</td>\n",
" <td>31.0</td>\n",
" <td>20.0</td>\n",
" <td>...</td>\n",
" <td>Barcelona</td>\n",
" <td>26.8</td>\n",
" <td>13</td>\n",
" <td>1100.0</td>\n",
" <td>49.72</td>\n",
" <td>Ath Madrid</td>\n",
" <td>27.2</td>\n",
" <td>5</td>\n",
" <td>807.0</td>\n",
" <td>40.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4159</th>\n",
" <td>2019-03-30</td>\n",
" <td>Alaves</td>\n",
" <td>Ath Madrid</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>A</td>\n",
" <td>31.0</td>\n",
" <td>42.0</td>\n",
" <td>32.0</td>\n",
" <td>18.0</td>\n",
" <td>...</td>\n",
" <td>Alaves</td>\n",
" <td>26.2</td>\n",
" <td>14</td>\n",
" <td>59.4</td>\n",
" <td>2.48</td>\n",
" <td>Ath Madrid</td>\n",
" <td>27.2</td>\n",
" <td>5</td>\n",
" <td>807.0</td>\n",
" <td>40.35</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
" Date HomeTeam AwayTeam FTHG FTAG FTR HTGS ATGS HTGC \\\n",
"4155 2018-09-29 Real Madrid Ath Madrid 0.0 0.0 D 10.0 7.0 6.0 \n",
"4156 2018-10-20 Villarreal Ath Madrid 1.0 1.0 D 6.0 9.0 6.0 \n",
"4157 2019-02-03 Betis Ath Madrid 1.0 0.0 H 26.0 31.0 23.0 \n",
"4158 2019-04-06 Barcelona Ath Madrid 2.0 0.0 H 78.0 44.0 31.0 \n",
"4159 2019-03-30 Alaves Ath Madrid 0.0 4.0 A 31.0 42.0 32.0 \n",
"\n",
" ATGC ... Club_x Age_H Foreign_H Total_value_H \\\n",
"4155 3.0 ... Real Madrid 26.5 4 969.0 \n",
"4156 4.0 ... Villarreal 28.7 15 196.1 \n",
"4157 13.0 ... Betis 27.8 15 180.5 \n",
"4158 20.0 ... Barcelona 26.8 13 1100.0 \n",
"4159 18.0 ... Alaves 26.2 14 59.4 \n",
"\n",
" Market_value_H Club_y Age_A Foreign_A Total_value_A Market_value_A \n",
"4155 40.38 Ath Madrid 27.2 5 807.0 40.35 \n",
"4156 7.84 Ath Madrid 27.2 5 807.0 40.35 \n",
"4157 8.60 Ath Madrid 27.2 5 807.0 40.35 \n",
"4158 49.72 Ath Madrid 27.2 5 807.0 40.35 \n",
"4159 2.48 Ath Madrid 27.2 5 807.0 40.35 \n",
"\n",
"[5 rows x 66 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_columns = None\n",
"marketv = pd.read_csv(loc + 'budget_laliga_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', left_on=['HomeTeam','Sezon'], \n",
" right_on = ['Club','Sezon'], sort=False)\n",
"\n",
"dataset = dataset.rename(columns={'Age': 'Age_H', 'Foreign': 'Foreign_H',\n",
" 'Total_value': 'Total_value_H', 'Market_value': 'Market_value_H'})\n",
"\n",
"dataset = pd.merge(dataset, marketv, how='inner', left_on=['AwayTeam','Sezon'], \n",
" right_on = ['Club','Sezon'], sort=False)\n",
"\n",
"dataset = dataset.rename(columns={'Age': 'Age_A', 'Foreign': 'Foreign_A',\n",
" 'Total_value': 'Total_value_A', '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."
]
},
{
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zmienna 'draw' przyjmuje wartość 1 w przypadku remisu, 0 w przeciwym wypadku"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modyfikacja wbranych kolumn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Konwersja wybranych kolumn do typu kategorycznego"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wystarczy konwersja za pomoca .astype(), gdyż wszystkie wybrane zmienne przyjmują wyłącznie dwie kategorie.( "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"warnings.simplefilter('ignore')\n",
"cols_cat = [ 'HM1', 'HM2', 'HM3', 'HM4', 'HM5', 'AM1',\n",
" 'AM2', 'AM3', 'AM4', 'AM5', '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": [
"### Przegląd danych"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>FTHG</th>\n",
" <th>FTAG</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>...</th>\n",
" <th>Mean_home_goals</th>\n",
" <th>Mean_away_goals</th>\n",
" <th>Age_H</th>\n",
" <th>Foreign_H</th>\n",
" <th>Total_value_H</th>\n",
" <th>Market_value_H</th>\n",
" <th>Age_A</th>\n",
" <th>Foreign_A</th>\n",
" <th>Total_value_A</th>\n",
" <th>Market_value_A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4157.000000</td>\n",
" <td>4157.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.00000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>...</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" <td>4160.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.627857</td>\n",
" <td>1.130864</td>\n",
" <td>26.925240</td>\n",
" <td>27.207933</td>\n",
" <td>27.18149</td>\n",
" <td>26.925240</td>\n",
" <td>27.290385</td>\n",
" <td>27.684375</td>\n",
" <td>20.852163</td>\n",
" <td>10.047356</td>\n",
" <td>...</td>\n",
" <td>1.575990</td>\n",
" <td>1.129107</td>\n",
" <td>25.059447</td>\n",
" <td>8.516106</td>\n",
" <td>155.689014</td>\n",
" <td>5.011197</td>\n",
" <td>25.061562</td>\n",
" <td>8.517308</td>\n",
" <td>155.112969</td>\n",
" <td>4.999331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.363565</td>\n",
" <td>1.176323</td>\n",
" <td>18.345956</td>\n",
" <td>18.510728</td>\n",
" <td>16.09453</td>\n",
" <td>15.883894</td>\n",
" <td>17.611017</td>\n",
" <td>17.815696</td>\n",
" <td>10.031258</td>\n",
" <td>5.190427</td>\n",
" <td>...</td>\n",
" <td>0.920874</td>\n",
" <td>0.553381</td>\n",
" <td>1.132740</td>\n",
" <td>5.272861</td>\n",
" <td>187.164221</td>\n",
" <td>6.531338</td>\n",
" <td>1.133098</td>\n",
" <td>5.294927</td>\n",
" <td>187.808292</td>\n",
" <td>6.570286</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>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>22.700000</td>\n",
" <td>1.000000</td>\n",
" <td>21.100000</td>\n",
" <td>0.810000</td>\n",
" <td>22.700000</td>\n",
" <td>1.000000</td>\n",
" <td>21.100000</td>\n",
" <td>0.810000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>13.000000</td>\n",
" <td>13.000000</td>\n",
" <td>14.00000</td>\n",
" <td>14.000000</td>\n",
" <td>13.000000</td>\n",
" <td>14.000000</td>\n",
" <td>12.000000</td>\n",
" <td>5.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>0.934420</td>\n",
" <td>24.300000</td>\n",
" <td>4.000000</td>\n",
" <td>55.562500</td>\n",
" <td>1.750000</td>\n",
" <td>24.300000</td>\n",
" <td>4.000000</td>\n",
" <td>55.150000</td>\n",
" <td>1.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>24.000000</td>\n",
" <td>24.000000</td>\n",
" <td>25.00000</td>\n",
" <td>25.000000</td>\n",
" <td>25.000000</td>\n",
" <td>25.000000</td>\n",
" <td>21.000000</td>\n",
" <td>10.000000</td>\n",
" <td>...</td>\n",
" <td>1.400000</td>\n",
" <td>1.110000</td>\n",
" <td>24.900000</td>\n",
" <td>7.000000</td>\n",
" <td>73.200000</td>\n",
" <td>2.290000</td>\n",
" <td>24.900000</td>\n",
" <td>7.000000</td>\n",
" <td>73.200000</td>\n",
" <td>2.290000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>36.000000</td>\n",
" <td>37.000000</td>\n",
" <td>39.00000</td>\n",
" <td>39.000000</td>\n",
" <td>37.000000</td>\n",
" <td>38.000000</td>\n",
" <td>29.250000</td>\n",
" <td>16.000000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>1.252867</td>\n",
" <td>25.700000</td>\n",
" <td>12.000000</td>\n",
" <td>170.100000</td>\n",
" <td>5.280000</td>\n",
" <td>25.700000</td>\n",
" <td>12.000000</td>\n",
" <td>170.100000</td>\n",
" <td>5.280000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10.000000</td>\n",
" <td>8.000000</td>\n",
" <td>117.000000</td>\n",
" <td>115.000000</td>\n",
" <td>88.00000</td>\n",
" <td>89.000000</td>\n",
" <td>97.000000</td>\n",
" <td>94.000000</td>\n",
" <td>38.000000</td>\n",
" <td>18.000000</td>\n",
" <td>...</td>\n",
" <td>9.395973</td>\n",
" <td>9.666667</td>\n",
" <td>28.700000</td>\n",
" <td>26.000000</td>\n",
" <td>1100.000000</td>\n",
" <td>49.720000</td>\n",
" <td>28.700000</td>\n",
" <td>26.000000</td>\n",
" <td>1100.000000</td>\n",
" <td>49.720000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" FTHG FTAG HTGS ATGS HTGC \\\n",
"count 4157.000000 4157.000000 4160.000000 4160.000000 4160.00000 \n",
"mean 1.627857 1.130864 26.925240 27.207933 27.18149 \n",
"std 1.363565 1.176323 18.345956 18.510728 16.09453 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.00000 \n",
"25% 1.000000 0.000000 13.000000 13.000000 14.00000 \n",
"50% 1.000000 1.000000 24.000000 24.000000 25.00000 \n",
"75% 2.000000 2.000000 36.000000 37.000000 39.00000 \n",
"max 10.000000 8.000000 117.000000 115.000000 88.00000 \n",
"\n",
" ATGC HTP ATP MW HomeTeamLP \\\n",
"count 4160.000000 4160.000000 4160.000000 4160.000000 4160.000000 \n",
"mean 26.925240 27.290385 27.684375 20.852163 10.047356 \n",
"std 15.883894 17.611017 17.815696 10.031258 5.190427 \n",
"min 0.000000 0.000000 0.000000 4.000000 1.000000 \n",
"25% 14.000000 13.000000 14.000000 12.000000 5.000000 \n",
"50% 25.000000 25.000000 25.000000 21.000000 10.000000 \n",
"75% 39.000000 37.000000 38.000000 29.250000 16.000000 \n",
"max 89.000000 97.000000 94.000000 38.000000 18.000000 \n",
"\n",
" ... Mean_home_goals Mean_away_goals Age_H \\\n",
"count ... 4160.000000 4160.000000 4160.000000 \n",
"mean ... 1.575990 1.129107 25.059447 \n",
"std ... 0.920874 0.553381 1.132740 \n",
"min ... 0.000000 0.000000 22.700000 \n",
"25% ... 1.000000 0.934420 24.300000 \n",
"50% ... 1.400000 1.110000 24.900000 \n",
"75% ... 2.000000 1.252867 25.700000 \n",
"max ... 9.395973 9.666667 28.700000 \n",
"\n",
" Foreign_H Total_value_H Market_value_H Age_A Foreign_A \\\n",
"count 4160.000000 4160.000000 4160.000000 4160.000000 4160.000000 \n",
"mean 8.516106 155.689014 5.011197 25.061562 8.517308 \n",
"std 5.272861 187.164221 6.531338 1.133098 5.294927 \n",
"min 1.000000 21.100000 0.810000 22.700000 1.000000 \n",
"25% 4.000000 55.562500 1.750000 24.300000 4.000000 \n",
"50% 7.000000 73.200000 2.290000 24.900000 7.000000 \n",
"75% 12.000000 170.100000 5.280000 25.700000 12.000000 \n",
"max 26.000000 1100.000000 49.720000 28.700000 26.000000 \n",
"\n",
" Total_value_A Market_value_A \n",
"count 4160.000000 4160.000000 \n",
"mean 155.112969 4.999331 \n",
"std 187.808292 6.570286 \n",
"min 21.100000 0.810000 \n",
"25% 55.150000 1.750000 \n",
"50% 73.200000 2.290000 \n",
"75% 170.100000 5.280000 \n",
"max 1100.000000 49.720000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wyszukanie braków danych"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FTHG True\n",
"FTAG True\n",
"HTGS False\n",
"ATGS False\n",
"HTGC False\n",
"ATGC False\n",
"HTP False\n",
"ATP False\n",
"MW False\n",
"HomeTeamLP False\n",
"AwayTeamLP False\n",
"Sezon False\n",
"HT_LP False\n",
"AT_LP False\n",
"HTFormPts True\n",
"ATFormPts True\n",
"HTGD False\n",
"ATGD False\n",
"DiffPts False\n",
"DiffFormPts True\n",
"DiffLP False\n",
"H2H_Home_pts False\n",
"H2H_Away_pts False\n",
"temp False\n",
"Off_H False\n",
"Off_A False\n",
"Deff_H False\n",
"Deff_A False\n",
"Mean_home_goals False\n",
"Mean_away_goals False\n",
"Age_H False\n",
"Foreign_H False\n",
"Total_value_H False\n",
"Market_value_H False\n",
"Age_A False\n",
"Foreign_A False\n",
"Total_value_A False\n",
"Market_value_A False\n",
"Name: count, dtype: bool"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.describe().loc['count',:] < len(df_final)"
]
},
{
"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": 10,
"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": [
"### Stworzenie nowych zmiennych"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df_final[\"H2H_Diff\"] = df_final[\"H2H_Home_pts\"] - df_final[\"H2H_Away_pts\"]\n",
"df_final[\"Market_Diff\"] = df_final[\"Market_value_H\"] / df_final[\"Market_value_A\"]\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\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wybranie wybranych kolumn w zbiorze danych"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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</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>...</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>Market_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>4155</th>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>...</td>\n",
" <td>6.315789</td>\n",
" <td>0.852805</td>\n",
" <td>969.0</td>\n",
" <td>807.0</td>\n",
" <td>0</td>\n",
" <td>1.000743</td>\n",
" <td>1.200743</td>\n",
" <td>-0.7</td>\n",
" <td>-2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4156</th>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>L</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>W</td>\n",
" <td>...</td>\n",
" <td>1.190840</td>\n",
" <td>1.020175</td>\n",
" <td>196.1</td>\n",
" <td>807.0</td>\n",
" <td>6</td>\n",
" <td>0.194300</td>\n",
" <td>0.242999</td>\n",
" <td>1.5</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4157</th>\n",
" <td>29</td>\n",
" <td>44</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>L</td>\n",
" <td>L</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>...</td>\n",
" <td>1.600000</td>\n",
" <td>1.150000</td>\n",
" <td>180.5</td>\n",
" <td>807.0</td>\n",
" <td>-6</td>\n",
" <td>0.213135</td>\n",
" <td>0.223668</td>\n",
" <td>0.6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4158</th>\n",
" <td>70</td>\n",
" <td>62</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>L</td>\n",
" <td>...</td>\n",
" <td>2.352941</td>\n",
" <td>1.144231</td>\n",
" <td>1100.0</td>\n",
" <td>807.0</td>\n",
" <td>3</td>\n",
" <td>1.232218</td>\n",
" <td>1.363073</td>\n",
" <td>-0.4</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4159</th>\n",
" <td>44</td>\n",
" <td>56</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>W</td>\n",
" <td>D</td>\n",
" <td>D</td>\n",
" <td>L</td>\n",
" <td>W</td>\n",
" <td>W</td>\n",
" <td>...</td>\n",
" <td>0.784314</td>\n",
" <td>0.605505</td>\n",
" <td>59.4</td>\n",
" <td>807.0</td>\n",
" <td>-9</td>\n",
" <td>0.061462</td>\n",
" <td>0.073606</td>\n",
" <td>-1.0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 40 columns</p>\n",
"</div>"
],
"text/plain": [
" HTP ATP HM1 HM2 HM3 HM4 HM5 AM1 AM2 AM3 ... Mean_home_goals \\\n",
"4155 13 11 L W D W W W W D ... 6.315789 \n",
"4156 8 15 L L W D W W D W ... 1.190840 \n",
"4157 29 44 L W L L D W W W ... 1.600000 \n",
"4158 70 62 D W W W W W W L ... 2.352941 \n",
"4159 44 56 W D W D D L W W ... 0.784314 \n",
"\n",
" Mean_away_goals Total_value_H Total_value_A H2H_Diff Market_Diff \\\n",
"4155 0.852805 969.0 807.0 0 1.000743 \n",
"4156 1.020175 196.1 807.0 6 0.194300 \n",
"4157 1.150000 180.5 807.0 -6 0.213135 \n",
"4158 1.144231 1100.0 807.0 3 1.232218 \n",
"4159 0.605505 59.4 807.0 -9 0.061462 \n",
"\n",
" Total_Diff Age_diff LP_Diff draw \n",
"4155 1.200743 -0.7 -2 1 \n",
"4156 0.242999 1.5 13 1 \n",
"4157 0.223668 0.6 6 0 \n",
"4158 1.363073 -0.4 -1 0 \n",
"4159 0.073606 -1.0 3 0 \n",
"\n",
"[5 rows x 40 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = df_final[[ 'HTP', 'ATP', 'HM1', 'HM2', 'HM3', 'HM4', 'HM5', 'AM1',\n",
" 'AM2', 'AM3', 'AM4', 'AM5', \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', 'Market_Diff', \n",
" 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw']]\n",
"\n",
"\n",
"df_final.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Konwersja wybranych kolumn do typu integer"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"cols_to_int = [\"MW\", \"DiffFormPts\", \"DiffLP\", \"HTGD\", \"ATGD\", \"HT_LP\", \"AT_LP\", \"HTP\", \"ATP\", \"H2H_Home_pts\", \"H2H_Away_pts\"]\n",
"df_final[cols_to_int] = df_final[cols_to_int].astype('int')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dodanie dodatkowych zmiennych binarnych"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"df_final['HM1_Draw'] = np.where(df_final['HM1']=='D', 1, 0)\n",
"df_final['HM2_Draw'] = np.where(df_final['HM2']=='D', 1, 0)\n",
"df_final['AM1_Draw'] = np.where(df_final['AM1']=='D', 1, 0)\n",
"df_final['AM2_Draw'] = np.where(df_final['AM2']=='D', 1, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Eksploracja danych"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Typ zmiennych, ilość wartości "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 4160 entries, 0 to 4159\n",
"Data columns (total 44 columns):\n",
"HTP 4160 non-null int32\n",
"ATP 4160 non-null int32\n",
"HM1 4160 non-null category\n",
"HM2 4160 non-null category\n",
"HM3 4160 non-null category\n",
"HM4 4160 non-null category\n",
"HM5 4160 non-null category\n",
"AM1 4160 non-null category\n",
"AM2 4160 non-null category\n",
"AM3 4160 non-null category\n",
"AM4 4160 non-null category\n",
"AM5 4160 non-null category\n",
"MW 4160 non-null int32\n",
"HT_LP 4160 non-null int32\n",
"AT_LP 4160 non-null int32\n",
"HTWinStreak3 4160 non-null category\n",
"HTWinStreak5 4160 non-null category\n",
"HTLossStreak3 4160 non-null category\n",
"HTLossStreak5 4160 non-null category\n",
"ATWinStreak3 4160 non-null category\n",
"ATWinStreak5 4160 non-null category\n",
"ATLossStreak3 4160 non-null category\n",
"ATLossStreak5 4160 non-null category\n",
"HTGD 4160 non-null int32\n",
"ATGD 4160 non-null int32\n",
"DiffPts 4160 non-null int64\n",
"DiffFormPts 4160 non-null int32\n",
"DiffLP 4160 non-null int32\n",
"H2H_Home_pts 4160 non-null int32\n",
"H2H_Away_pts 4160 non-null int32\n",
"Mean_home_goals 4160 non-null float64\n",
"Mean_away_goals 4160 non-null float64\n",
"Total_value_H 4160 non-null float64\n",
"Total_value_A 4160 non-null float64\n",
"H2H_Diff 4160 non-null int64\n",
"Market_Diff 4160 non-null float64\n",
"Total_Diff 4160 non-null float64\n",
"Age_diff 4160 non-null float64\n",
"LP_Diff 4160 non-null int64\n",
"draw 4160 non-null category\n",
"HM1_Draw 4160 non-null int32\n",
"HM2_Draw 4160 non-null int32\n",
"AM1_Draw 4160 non-null int32\n",
"AM2_Draw 4160 non-null int32\n",
"dtypes: category(19), float64(7), int32(15), int64(3)\n",
"memory usage: 680.6 KB\n"
]
}
],
"source": [
"df_final.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zmiennych jest 44... Zdecydowanie za dużo. Sam fakt posiadania tak wielu zmiennych będzie znacznie utrudniał eksploracje, a nastepnie późniejsze tworzenie modelu."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zmienna wynikowa 'draw'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 3218\n",
"1 942\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": {
"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>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>...</th>\n",
" <th>Total_value_A</th>\n",
" <th>H2H_Diff</th>\n",
" <th>Market_Diff</th>\n",
" <th>Total_Diff</th>\n",
" <th>Age_diff</th>\n",
" <th>LP_Diff</th>\n",
" <th>HM1_Draw</th>\n",
" <th>HM2_Draw</th>\n",
" <th>AM1_Draw</th>\n",
" <th>AM2_Draw</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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>27.813549</td>\n",
" <td>27.653201</td>\n",
" <td>20.851771</td>\n",
" <td>10.185830</td>\n",
" <td>10.208204</td>\n",
" <td>0.664699</td>\n",
" <td>0.247669</td>\n",
" <td>0.160348</td>\n",
" <td>-0.203543</td>\n",
" <td>-0.416097</td>\n",
" <td>...</td>\n",
" <td>155.372004</td>\n",
" <td>-0.196706</td>\n",
" <td>2.195623</td>\n",
" <td>2.260934</td>\n",
" <td>-0.034027</td>\n",
" <td>-0.022374</td>\n",
" <td>0.230578</td>\n",
" <td>0.234929</td>\n",
" <td>0.222188</td>\n",
" <td>0.228403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>25.503185</td>\n",
" <td>27.790870</td>\n",
" <td>20.853503</td>\n",
" <td>11.544586</td>\n",
" <td>10.242038</td>\n",
" <td>-3.402335</td>\n",
" <td>0.402335</td>\n",
" <td>-2.287686</td>\n",
" <td>-0.945860</td>\n",
" <td>1.169851</td>\n",
" <td>...</td>\n",
" <td>154.228068</td>\n",
" <td>-0.436306</td>\n",
" <td>1.459853</td>\n",
" <td>1.461521</td>\n",
" <td>0.106900</td>\n",
" <td>1.302548</td>\n",
" <td>0.213376</td>\n",
" <td>0.236730</td>\n",
" <td>0.250531</td>\n",
" <td>0.194268</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" HTP ATP MW HT_LP AT_LP HTGD \\\n",
"draw \n",
"0 27.813549 27.653201 20.851771 10.185830 10.208204 0.664699 \n",
"1 25.503185 27.790870 20.853503 11.544586 10.242038 -3.402335 \n",
"\n",
" ATGD DiffPts DiffFormPts DiffLP ... Total_value_A \\\n",
"draw ... \n",
"0 0.247669 0.160348 -0.203543 -0.416097 ... 155.372004 \n",
"1 0.402335 -2.287686 -0.945860 1.169851 ... 154.228068 \n",
"\n",
" H2H_Diff Market_Diff Total_Diff Age_diff LP_Diff HM1_Draw \\\n",
"draw \n",
"0 -0.196706 2.195623 2.260934 -0.034027 -0.022374 0.230578 \n",
"1 -0.436306 1.459853 1.461521 0.106900 1.302548 0.213376 \n",
"\n",
" HM2_Draw AM1_Draw AM2_Draw \n",
"draw \n",
"0 0.234929 0.222188 0.228403 \n",
"1 0.236730 0.250531 0.194268 \n",
"\n",
"[2 rows x 25 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"warnings.simplefilter('ignore')\n",
"df_final.groupby('draw').mean() "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='draw', data=df_final , palette = \"hls\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"percentage of no draws is 77.35576923076923\n",
"percentage of draws 22.64423076923077\n"
]
}
],
"source": [
"count_no_sub = len(df_final[df_final['draw']==0])\n",
"count_sub = len(df_final[df_final['draw']==1])\n",
"pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
"print(\"Percentage of no draws is\", pct_of_no_sub*100)\n",
"pct_of_sub = count_sub/(count_no_sub+count_sub)\n",
"print(\"Percentage of draws\", pct_of_sub*100)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x1080 with 25 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_final.hist(bins=50, figsize=(20,15))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC6187B8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC3F50B8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DBC97978>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DBB6D6A0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DE7B4710>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DE7B4588>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC49C208>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC634898>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC775F28>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC94C5F8>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC920C88>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DCAB2358>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC51C9E8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DDE680B8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DDE44748>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC6D7DD8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC4D64A8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC5F5B38>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC711208>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC730898>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DCA51F28>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC9D65F8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC8B7C88>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC8BF358>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000001A4DC3DE9E8>]],\n",
" dtype=object)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 25 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import scatter_matrix\n",
"attributes = [\"DiffLP\", \"LP_Diff\", \"HTGD\", \"ATGD\", \n",
" \"Mean_home_goals\"]\n",
"scatter_matrix(df_final[attributes], figsize=(12, 8))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a4dc670da0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_final.plot(kind=\"scatter\", x=\"HTGD\", y=\"Mean_home_goals\", alpha=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modyfikacja zmiennych które rosną wraz z kolejkami"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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",
"\n",
"df_final[\"Goals_mean_diff\"] = df_final[\"Mean_home_goals\"] - df_final[\"Mean_away_goals\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Korelacja zmiennych"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>H2H_Home_pts</th>\n",
" <th>H2H_Away_pts</th>\n",
" <th>Mean_home_goals</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>HM1_Draw</th>\n",
" <th>HM2_Draw</th>\n",
" <th>AM1_Draw</th>\n",
" <th>AM2_Draw</th>\n",
" <th>HTGD_by_MW</th>\n",
" <th>ATGD_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",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>H2H_Home_pts</th>\n",
" <td>NaN</td>\n",
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" <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>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>Mean_home_goals</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.882361</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>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>H2H_Diff</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>LP_Diff</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.845129</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>HM2_Draw</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>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",
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" <tr>\n",
" <th>AM1_Draw</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",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>AM2_Draw</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>1.0</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>-0.852117</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>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>ATGD_by_MW</th>\n",
" <td>NaN</td>\n",
" <td>-0.847128</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>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",
" </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>0.882361</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>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",
" </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",
"H2H_Home_pts NaN NaN NaN NaN NaN \n",
"H2H_Away_pts NaN NaN NaN NaN NaN \n",
"Mean_home_goals NaN NaN NaN NaN NaN \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 -0.845129 NaN NaN \n",
"HM1_Draw NaN NaN NaN NaN NaN \n",
"HM2_Draw NaN NaN NaN NaN NaN \n",
"AM1_Draw NaN NaN NaN NaN NaN \n",
"AM2_Draw NaN NaN NaN NaN NaN \n",
"HTGD_by_MW -0.852117 NaN NaN NaN NaN \n",
"ATGD_by_MW NaN -0.847128 NaN NaN NaN \n",
"Goals_mean_diff NaN NaN NaN NaN NaN \n",
"\n",
" H2H_Home_pts H2H_Away_pts Mean_home_goals Mean_away_goals \\\n",
"HT_LP NaN NaN NaN NaN \n",
"AT_LP NaN NaN NaN NaN \n",
"DiffPts NaN NaN NaN NaN \n",
"DiffFormPts NaN NaN NaN NaN \n",
"DiffLP NaN NaN NaN NaN \n",
"H2H_Home_pts 1.000000 NaN NaN NaN \n",
"H2H_Away_pts NaN 1.000000 NaN NaN \n",
"Mean_home_goals NaN NaN 1.000000 NaN \n",
"Mean_away_goals NaN NaN NaN 1.0 \n",
"H2H_Diff 0.876546 -0.881308 NaN NaN \n",
"Total_Diff NaN NaN NaN NaN \n",
"Age_diff NaN NaN NaN NaN \n",
"LP_Diff NaN NaN NaN NaN \n",
"HM1_Draw NaN NaN NaN NaN \n",
"HM2_Draw NaN NaN NaN NaN \n",
"AM1_Draw NaN NaN NaN NaN \n",
"AM2_Draw NaN NaN NaN NaN \n",
"HTGD_by_MW NaN NaN NaN NaN \n",
"ATGD_by_MW NaN NaN NaN NaN \n",
"Goals_mean_diff NaN NaN 0.882361 NaN \n",
"\n",
" H2H_Diff Total_Diff Age_diff LP_Diff HM1_Draw HM2_Draw \\\n",
"HT_LP NaN NaN NaN NaN NaN NaN \n",
"AT_LP NaN NaN NaN NaN NaN NaN \n",
"DiffPts NaN NaN NaN -0.845129 NaN NaN \n",
"DiffFormPts NaN NaN NaN NaN NaN NaN \n",
"DiffLP NaN NaN NaN NaN NaN NaN \n",
"H2H_Home_pts 0.876546 NaN NaN NaN NaN NaN \n",
"H2H_Away_pts -0.881308 NaN NaN NaN NaN NaN \n",
"Mean_home_goals NaN NaN NaN NaN NaN NaN \n",
"Mean_away_goals NaN NaN NaN NaN NaN NaN \n",
"H2H_Diff 1.000000 NaN NaN NaN NaN NaN \n",
"Total_Diff NaN 1.0 NaN NaN NaN NaN \n",
"Age_diff NaN NaN 1.0 NaN NaN NaN \n",
"LP_Diff NaN NaN NaN 1.000000 NaN NaN \n",
"HM1_Draw NaN NaN NaN NaN 1.0 NaN \n",
"HM2_Draw NaN NaN NaN NaN NaN 1.0 \n",
"AM1_Draw NaN NaN NaN NaN NaN NaN \n",
"AM2_Draw NaN NaN NaN NaN NaN NaN \n",
"HTGD_by_MW NaN NaN NaN NaN NaN NaN \n",
"ATGD_by_MW NaN NaN NaN NaN NaN NaN \n",
"Goals_mean_diff NaN NaN NaN NaN NaN NaN \n",
"\n",
" AM1_Draw AM2_Draw HTGD_by_MW ATGD_by_MW Goals_mean_diff \n",
"HT_LP NaN NaN -0.852117 NaN NaN \n",
"AT_LP NaN NaN NaN -0.847128 NaN \n",
"DiffPts NaN NaN NaN NaN NaN \n",
"DiffFormPts NaN NaN NaN NaN NaN \n",
"DiffLP NaN NaN NaN NaN NaN \n",
"H2H_Home_pts NaN NaN NaN NaN NaN \n",
"H2H_Away_pts NaN NaN NaN NaN NaN \n",
"Mean_home_goals NaN NaN NaN NaN 0.882361 \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 NaN NaN NaN \n",
"HM1_Draw NaN NaN NaN NaN NaN \n",
"HM2_Draw NaN NaN NaN NaN NaN \n",
"AM1_Draw 1.0 NaN NaN NaN NaN \n",
"AM2_Draw NaN 1.0 NaN NaN NaN \n",
"HTGD_by_MW NaN NaN 1.000000 NaN NaN \n",
"ATGD_by_MW NaN NaN NaN 1.000000 NaN \n",
"Goals_mean_diff NaN NaN NaN NaN 1.000000 "
]
},
"execution_count": 34,
"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', 'H2H_Home_pts',\n",
" 'H2H_Away_pts', 'Mean_home_goals',\n",
" 'Mean_away_goals', 'Total_value_H', 'Total_value_A', 'H2H_Diff',\n",
" 'Market_Diff', 'Total_Diff', 'Age_diff', 'LP_Diff', 'draw', 'HM1_Draw',\n",
" 'HM2_Draw', 'AM1_Draw', 'AM2_Draw', 'HTGD_by_MW', 'ATGD_by_MW', \"H2H_Home_pts\", \"H2H_Away_pts\", \n",
" 'Goals_mean_diff']\n",
"\n",
"df_final = df_final[cols_selected]\n",
"corr_matrix = df_final.corr()\n",
"corr_matrix[(corr_matrix>0.8) | (corr_matrix< -0.8) ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Usuniecie wysoce skorelowanych zmiennych ze soba"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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" <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>DiffPts</th>\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>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",
" </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",
" </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",
" </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",
" </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",
" </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",
" </tr>\n",
" <tr>\n",
" <th>LP_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>1.0</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",
" </tr>\n",
" <tr>\n",
" <th>HM1_Draw</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",
" </tr>\n",
" <tr>\n",
" <th>HM2_Draw</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",
" </tr>\n",
" <tr>\n",
" <th>AM1_Draw</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>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>AM2_Draw</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>1.0</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>-0.852117</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>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>ATGD_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>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>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 1.0 NaN NaN NaN \n",
"DiffPts NaN NaN 1.0 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 NaN NaN NaN \n",
"HM1_Draw NaN NaN NaN NaN NaN \n",
"HM2_Draw NaN NaN NaN NaN NaN \n",
"AM1_Draw NaN NaN NaN NaN NaN \n",
"AM2_Draw NaN NaN NaN NaN NaN \n",
"HTGD_by_MW -0.852117 NaN NaN NaN NaN \n",
"ATGD_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 NaN \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 1.0 \n",
"HM1_Draw NaN NaN NaN NaN NaN \n",
"HM2_Draw NaN NaN NaN NaN NaN \n",
"AM1_Draw NaN NaN NaN NaN NaN \n",
"AM2_Draw NaN NaN NaN NaN NaN \n",
"HTGD_by_MW NaN NaN NaN NaN NaN \n",
"ATGD_by_MW NaN NaN NaN NaN NaN \n",
"Goals_mean_diff NaN NaN NaN NaN NaN \n",
"\n",
" HM1_Draw HM2_Draw AM1_Draw AM2_Draw HTGD_by_MW \\\n",
"HT_LP NaN NaN NaN NaN -0.852117 \n",
"AT_LP NaN NaN NaN NaN NaN \n",
"DiffPts NaN NaN NaN NaN NaN \n",
"DiffFormPts NaN NaN NaN NaN NaN \n",
"DiffLP NaN NaN NaN NaN NaN \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 NaN NaN NaN \n",
"HM1_Draw 1.0 NaN NaN NaN NaN \n",
"HM2_Draw NaN 1.0 NaN NaN NaN \n",
"AM1_Draw NaN NaN 1.0 NaN NaN \n",
"AM2_Draw NaN NaN NaN 1.0 NaN \n",
"HTGD_by_MW NaN NaN NaN NaN 1.000000 \n",
"ATGD_by_MW NaN NaN NaN NaN NaN \n",
"Goals_mean_diff NaN NaN NaN NaN NaN \n",
"\n",
" ATGD_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",
"HM1_Draw NaN NaN \n",
"HM2_Draw NaN NaN \n",
"AM1_Draw NaN NaN \n",
"AM2_Draw NaN NaN \n",
"HTGD_by_MW NaN NaN \n",
"ATGD_by_MW 1.0 NaN \n",
"Goals_mean_diff NaN 1.0 "
]
},
"execution_count": 38,
"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', 'HM1_Draw',\n",
" 'HM2_Draw', 'AM1_Draw', 'AM2_Draw', 'HTGD_by_MW', 'ATGD_by_MW',\"H2H_Home_pts\", \"H2H_Away_pts\", \n",
" 'Goals_mean_diff']\n",
"df_final = df_final[cols_selected]\n",
"corr_matrix = df_final.corr()\n",
"corr_matrix[(corr_matrix>0.85) | (corr_matrix< -0.85) ]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <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",
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" <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>Market_Diff</th>\n",
" <th>Total_Diff</th>\n",
" <th>Age_diff</th>\n",
" <th>LP_Diff</th>\n",
" <th>draw</th>\n",
" <th>HM1_Draw</th>\n",
" <th>HM2_Draw</th>\n",
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" <th>AM2_Draw</th>\n",
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" <th>4155</th>\n",
" <td>2</td>\n",
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" <td>6.315789</td>\n",
" <td>0.852805</td>\n",
" <td>969.0</td>\n",
" <td>807.0</td>\n",
" <td>0</td>\n",
" <td>1.000743</td>\n",
" <td>1.200743</td>\n",
" <td>-0.7</td>\n",
" <td>-2</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0.571429</td>\n",
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" <td>5.462984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4156</th>\n",
" <td>16</td>\n",
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" <td>-4</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>1.190840</td>\n",
" <td>1.020175</td>\n",
" <td>196.1</td>\n",
" <td>807.0</td>\n",
" <td>6</td>\n",
" <td>0.194300</td>\n",
" <td>0.242999</td>\n",
" <td>1.5</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>0.555556</td>\n",
" <td>0.170664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4157</th>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1.600000</td>\n",
" <td>1.150000</td>\n",
" <td>180.5</td>\n",
" <td>807.0</td>\n",
" <td>-6</td>\n",
" <td>0.213135</td>\n",
" <td>0.223668</td>\n",
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" <td>0</td>\n",
" <td>0.136364</td>\n",
" <td>0.818182</td>\n",
" <td>0.450000</td>\n",
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" <tr>\n",
" <th>4158</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1.144231</td>\n",
" <td>1100.0</td>\n",
" <td>807.0</td>\n",
" <td>3</td>\n",
" <td>1.232218</td>\n",
" <td>1.363073</td>\n",
" <td>-0.4</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>1.516129</td>\n",
" <td>0.774194</td>\n",
" <td>1.208710</td>\n",
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" <tr>\n",
" <th>4159</th>\n",
" <td>5</td>\n",
" <td>2</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",
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" <td>0</td>\n",
" <td>-12</td>\n",
" <td>-3</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>0.784314</td>\n",
" <td>0.605505</td>\n",
" <td>59.4</td>\n",
" <td>807.0</td>\n",
" <td>-9</td>\n",
" <td>0.061462</td>\n",
" <td>0.073606</td>\n",
" <td>-1.0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.034483</td>\n",
" <td>0.827586</td>\n",
" <td>0.178809</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HT_LP AT_LP HTWinStreak3 HTWinStreak5 HTLossStreak3 HTLossStreak5 \\\n",
"4155 2 4 0 0 0 0 \n",
"4156 16 3 0 0 0 0 \n",
"4157 8 2 0 0 0 0 \n",
"4158 1 2 1 0 0 0 \n",
"4159 5 2 0 0 0 0 \n",
"\n",
" ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 DiffPts \\\n",
"4155 0 0 0 0 2 \n",
"4156 0 0 0 0 -7 \n",
"4157 0 0 0 0 -15 \n",
"4158 0 0 0 0 8 \n",
"4159 1 0 0 0 -12 \n",
"\n",
" DiffFormPts DiffLP H2H_Home_pts H2H_Away_pts Mean_home_goals \\\n",
"4155 0 1 3 3 6.315789 \n",
"4156 -4 3 7 1 1.190840 \n",
"4157 -9 4 1 7 1.600000 \n",
"4158 1 -1 5 2 2.352941 \n",
"4159 -3 12 0 9 0.784314 \n",
"\n",
" Mean_away_goals Total_value_H Total_value_A H2H_Diff Market_Diff \\\n",
"4155 0.852805 969.0 807.0 0 1.000743 \n",
"4156 1.020175 196.1 807.0 6 0.194300 \n",
"4157 1.150000 180.5 807.0 -6 0.213135 \n",
"4158 1.144231 1100.0 807.0 3 1.232218 \n",
"4159 0.605505 59.4 807.0 -9 0.061462 \n",
"\n",
" Total_Diff Age_diff LP_Diff draw HM1_Draw HM2_Draw AM1_Draw \\\n",
"4155 1.200743 -0.7 -2 1 0 0 0 \n",
"4156 0.242999 1.5 13 1 0 0 0 \n",
"4157 0.223668 0.6 6 0 0 0 0 \n",
"4158 1.363073 -0.4 -1 0 1 0 0 \n",
"4159 0.073606 -1.0 3 0 0 1 0 \n",
"\n",
" AM2_Draw HTGD_by_MW ATGD_by_MW Goals_mean_diff \n",
"4155 0 0.571429 0.571429 5.462984 \n",
"4156 1 0.000000 0.555556 0.170664 \n",
"4157 0 0.136364 0.818182 0.450000 \n",
"4158 0 1.516129 0.774194 1.208710 \n",
"4159 0 -0.034483 0.827586 0.178809 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_final = df_final\n",
"data_final.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Over-sampling z wykorzystaniem SMOTE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our training data created, I’ll up-sample the no-subscription using the SMOTE algorithm(Synthetic Minority Oversampling Technique). At a high level, SMOTE:\n",
" Works by creating synthetic samples from the minor class (no-subscription) instead of creating copies.\n",
" Randomly choosing one of the k-nearest-neighbors and using it to create a similar, but randomly tweaked, new observations.\n",
"We are going to implement SMOTE in Python."
]
},
{
"cell_type": "code",
"execution_count": 39,
"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": 40,
"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": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"length of oversampled data is 4518\n",
"Number of no draw in oversampled data 2259\n",
"Number of draws 2259\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 draws\",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": [
"Now we have a perfect balanced data! \n",
"You may have noticed that I over-sampled only on the training data, because by oversampling only on the training data, \n",
"none of the information in the test data is being used to create synthetic observations, therefore,\n",
"no information will bleed from test data into the model training."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
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" 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>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>Market_Diff</th>\n",
" <th>Total_Diff</th>\n",
" <th>Age_diff</th>\n",
" <th>LP_Diff</th>\n",
" <th>HM1_Draw</th>\n",
" <th>HM2_Draw</th>\n",
" <th>AM1_Draw</th>\n",
" <th>AM2_Draw</th>\n",
" <th>HTGD_by_MW</th>\n",
" <th>ATGD_by_MW</th>\n",
" <th>Goals_mean_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4513</th>\n",
" <td>7.460516</td>\n",
" <td>2.327307</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-3.902951</td>\n",
" <td>0.000000</td>\n",
" <td>11.212178</td>\n",
" <td>0.442436</td>\n",
" <td>8.115129</td>\n",
" <td>0.135167</td>\n",
" <td>2.283222</td>\n",
" <td>62.549096</td>\n",
" <td>602.055850</td>\n",
" <td>-7.672693</td>\n",
" <td>0.115642</td>\n",
" <td>0.103895</td>\n",
" <td>0.447860</td>\n",
" <td>5.133209</td>\n",
" <td>0.557564</td>\n",
" <td>0.557564</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.330019</td>\n",
" <td>1.669077</td>\n",
" <td>-2.148055</td>\n",
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" <th>4514</th>\n",
" <td>6.910712</td>\n",
" <td>17.821423</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>18.357153</td>\n",
" <td>4.464270</td>\n",
" <td>-11.553559</td>\n",
" <td>2.089288</td>\n",
" <td>4.553559</td>\n",
" <td>2.143520</td>\n",
" <td>0.780114</td>\n",
" <td>110.500000</td>\n",
" <td>48.483925</td>\n",
" <td>-2.464270</td>\n",
" <td>2.760594</td>\n",
" <td>2.279242</td>\n",
" <td>-0.335715</td>\n",
" <td>-10.910712</td>\n",
" <td>0.910712</td>\n",
" <td>0.910712</td>\n",
" <td>1.00000</td>\n",
" <td>0.089288</td>\n",
" <td>-0.091953</td>\n",
" <td>-0.741533</td>\n",
" <td>1.363406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4515</th>\n",
" <td>3.941090</td>\n",
" <td>19.764361</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.05891</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>13.648007</td>\n",
" <td>4.882181</td>\n",
" <td>-11.058910</td>\n",
" <td>3.117819</td>\n",
" <td>2.941090</td>\n",
" <td>1.282474</td>\n",
" <td>1.063535</td>\n",
" <td>178.100000</td>\n",
" <td>66.446249</td>\n",
" <td>0.176729</td>\n",
" <td>2.538117</td>\n",
" <td>2.683764</td>\n",
" <td>0.317673</td>\n",
" <td>-15.823271</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.05891</td>\n",
" <td>0.000000</td>\n",
" <td>0.554737</td>\n",
" <td>-0.856161</td>\n",
" <td>0.218939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4516</th>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-48.000000</td>\n",
" <td>-5.000000</td>\n",
" <td>15.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.400000</td>\n",
" <td>1.160000</td>\n",
" <td>27.300000</td>\n",
" <td>561.100000</td>\n",
" <td>-3.000000</td>\n",
" <td>0.050121</td>\n",
" <td>0.048654</td>\n",
" <td>1.900000</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.277778</td>\n",
" <td>2.000000</td>\n",
" <td>0.240000</td>\n",
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" <tr>\n",
" <th>4517</th>\n",
" <td>4.036539</td>\n",
" <td>5.036539</td>\n",
" <td>0.0</td>\n",
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" <td>1.408653</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.777404</td>\n",
" <td>2.232342</td>\n",
" <td>0.995236</td>\n",
" <td>145.618269</td>\n",
" <td>178.760794</td>\n",
" <td>-0.777404</td>\n",
" <td>0.749559</td>\n",
" <td>0.814600</td>\n",
" <td>-0.944519</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.689924</td>\n",
" <td>0.319491</td>\n",
" <td>1.237105</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HT_LP AT_LP HTWinStreak3 HTWinStreak5 HTLossStreak3 \\\n",
"4513 7.460516 2.327307 0.0 0.0 0.0 \n",
"4514 6.910712 17.821423 0.0 0.0 0.0 \n",
"4515 3.941090 19.764361 0.0 0.0 0.0 \n",
"4516 10.000000 1.000000 0.0 0.0 0.0 \n",
"4517 4.036539 5.036539 0.0 0.0 0.0 \n",
"\n",
" HTLossStreak5 ATWinStreak3 ATWinStreak5 ATLossStreak3 ATLossStreak5 \\\n",
"4513 0.0 0.00000 0.0 0.0 0.0 \n",
"4514 0.0 0.00000 0.0 0.0 0.0 \n",
"4515 0.0 0.05891 0.0 0.0 0.0 \n",
"4516 0.0 0.00000 0.0 0.0 0.0 \n",
"4517 0.0 0.00000 0.0 0.0 0.0 \n",
"\n",
" DiffPts DiffFormPts DiffLP H2H_Home_pts H2H_Away_pts \\\n",
"4513 -3.902951 0.000000 11.212178 0.442436 8.115129 \n",
"4514 18.357153 4.464270 -11.553559 2.089288 4.553559 \n",
"4515 13.648007 4.882181 -11.058910 3.117819 2.941090 \n",
"4516 -48.000000 -5.000000 15.000000 0.000000 3.000000 \n",
"4517 2.222596 1.408653 4.000000 3.000000 3.777404 \n",
"\n",
" Mean_home_goals Mean_away_goals Total_value_H Total_value_A \\\n",
"4513 0.135167 2.283222 62.549096 602.055850 \n",
"4514 2.143520 0.780114 110.500000 48.483925 \n",
"4515 1.282474 1.063535 178.100000 66.446249 \n",
"4516 1.400000 1.160000 27.300000 561.100000 \n",
"4517 2.232342 0.995236 145.618269 178.760794 \n",
"\n",
" H2H_Diff Market_Diff Total_Diff Age_diff LP_Diff HM1_Draw \\\n",
"4513 -7.672693 0.115642 0.103895 0.447860 5.133209 0.557564 \n",
"4514 -2.464270 2.760594 2.279242 -0.335715 -10.910712 0.910712 \n",
"4515 0.176729 2.538117 2.683764 0.317673 -15.823271 0.000000 \n",
"4516 -3.000000 0.050121 0.048654 1.900000 9.000000 0.000000 \n",
"4517 -0.777404 0.749559 0.814600 -0.944519 -1.000000 0.000000 \n",
"\n",
" HM2_Draw AM1_Draw AM2_Draw HTGD_by_MW ATGD_by_MW Goals_mean_diff \n",
"4513 0.557564 0.00000 0.000000 0.330019 1.669077 -2.148055 \n",
"4514 0.910712 1.00000 0.089288 -0.091953 -0.741533 1.363406 \n",
"4515 0.000000 0.05891 0.000000 0.554737 -0.856161 0.218939 \n",
"4516 1.000000 0.00000 0.000000 -0.277778 2.000000 0.240000 \n",
"4517 0.000000 0.00000 0.000000 0.689924 0.319491 1.237105 "
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os_data_X.tail()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"cols = ['HT_LP', 'AT_LP', 'HTWinStreak3',\n",
" 'HTWinStreak5', 'HTLossStreak3', 'HTLossStreak5', 'ATWinStreak3',\n",
" 'ATWinStreak5', 'ATLossStreak3', 'ATLossStreak5', 'DiffPts',\n",
" 'DiffFormPts', 'DiffLP', 'H2H_Diff']\n",
"os_data_X[cols] = os_data_X[cols].round(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wybór zmiennych"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recursive Feature Elimination\n",
"Recursive Feature Elimination (RFE) is based on the idea to repeatedly \n",
"construct a model and choose either the best or worst performing feature, \n",
"setting the feature aside and then repeating the process with the rest of the features. \n",
"This process is applied until all features in the dataset are exhausted. \n",
"The goal of RFE is to select features by recursively considering smaller and smaller sets of features."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"HT_LP True\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 False\n",
"DiffPts False\n",
"DiffFormPts False\n",
"DiffLP False\n",
"H2H_Home_pts True\n",
"H2H_Away_pts True\n",
"Mean_home_goals False\n",
"Mean_away_goals False\n",
"Total_value_H False\n",
"Total_value_A False\n",
"H2H_Diff True\n",
"Market_Diff True\n",
"Total_Diff True\n",
"Age_diff False\n",
"LP_Diff True\n",
"HM1_Draw True\n",
"HM2_Draw True\n",
"AM1_Draw True\n",
"AM2_Draw True\n",
"HTGD_by_MW True\n",
"ATGD_by_MW False\n",
"Goals_mean_diff False\n",
"dtype: bool"
]
},
"execution_count": 64,
"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, 20)\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": 65,
"metadata": {},
"outputs": [],
"source": [
"X=os_data_X.loc[:, mask]\n",
"y=os_data_y['draw']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementacja modelu regresji logistycznej"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.670948\n",
" Iterations 6\n",
" Results: Logit\n",
"=================================================================\n",
"Model: Logit Pseudo R-squared: 0.032 \n",
"Dependent Variable: draw AIC: 6102.6855 \n",
"Date: 2019-04-30 15:57 BIC: 6231.0020 \n",
"No. Observations: 4518 Log-Likelihood: -3031.3 \n",
"Df Model: 19 LL-Null: -3131.6 \n",
"Df Residuals: 4498 LLR p-value: 2.5957e-32\n",
"Converged: 1.0000 Scale: 1.0000 \n",
"No. Iterations: 6.0000 \n",
"-----------------------------------------------------------------\n",
" Coef. Std.Err. z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------\n",
"HT_LP -0.1627 0.1487 -1.0940 0.2740 -0.4541 0.1288\n",
"AT_LP 0.1753 0.1487 1.1786 0.2386 -0.1162 0.4668\n",
"HTWinStreak3 -0.0601 0.1666 -0.3604 0.7186 -0.3867 0.2666\n",
"HTWinStreak5 -1.2485 0.4190 -2.9793 0.0029 -2.0698 -0.4272\n",
"HTLossStreak3 -0.5189 0.1515 -3.4259 0.0006 -0.8158 -0.2220\n",
"HTLossStreak5 0.3959 0.2985 1.3261 0.1848 -0.1892 0.9810\n",
"ATWinStreak3 0.1617 0.1385 1.1672 0.2431 -0.1098 0.4331\n",
"ATWinStreak5 0.2663 0.2075 1.2835 0.1993 -0.1403 0.6730\n",
"ATLossStreak3 -0.3917 0.1451 -2.7001 0.0069 -0.6760 -0.1074\n",
"H2H_Home_pts 0.2797 0.2260 1.2375 0.2159 -0.1633 0.7226\n",
"H2H_Away_pts -0.2355 0.2257 -1.0430 0.2969 -0.6779 0.2070\n",
"H2H_Diff -0.2387 0.2252 -1.0600 0.2892 -0.6802 0.2027\n",
"Market_Diff 0.2101 0.0741 2.8366 0.0046 0.0649 0.3552\n",
"Total_Diff -0.2985 0.0759 -3.9348 0.0001 -0.4472 -0.1498\n",
"LP_Diff 0.1614 0.1489 1.0837 0.2785 -0.1305 0.4532\n",
"HM1_Draw -0.1794 0.0777 -2.3093 0.0209 -0.3317 -0.0271\n",
"HM2_Draw -0.1678 0.0771 -2.1758 0.0296 -0.3190 -0.0166\n",
"AM1_Draw 0.1224 0.0774 1.5823 0.1136 -0.0292 0.2740\n",
"AM2_Draw -0.1932 0.0802 -2.4087 0.0160 -0.3503 -0.0360\n",
"HTGD_by_MW -0.2395 0.0883 -2.7111 0.0067 -0.4127 -0.0664\n",
"=================================================================\n",
"\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": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimization terminated successfully.\n",
" Current function value: 0.676143\n",
" Iterations 6\n",
" Results: Logit\n",
"=================================================================\n",
"Model: Logit Pseudo R-squared: 0.025 \n",
"Dependent Variable: draw AIC: 6129.6291 \n",
"Date: 2019-04-30 15:59 BIC: 6193.7873 \n",
"No. Observations: 4518 Log-Likelihood: -3054.8 \n",
"Df Model: 9 LL-Null: -3131.6 \n",
"Df Residuals: 4508 LLR p-value: 1.5458e-28\n",
"Converged: 1.0000 Scale: 1.0000 \n",
"No. Iterations: 6.0000 \n",
"-----------------------------------------------------------------\n",
" Coef. Std.Err. z P>|z| [0.025 0.975]\n",
"-----------------------------------------------------------------\n",
"HTWinStreak5 -1.1632 0.3924 -2.9643 0.0030 -1.9322 -0.3941\n",
"HTLossStreak3 -0.3608 0.1310 -2.7541 0.0059 -0.6176 -0.1040\n",
"ATWinStreak3 0.2988 0.1019 2.9323 0.0034 0.0991 0.4985\n",
"ATLossStreak3 -0.2473 0.1407 -1.7575 0.0788 -0.5230 0.0285\n",
"Market_Diff 0.2094 0.0705 2.9706 0.0030 0.0712 0.3476\n",
"Total_Diff -0.2358 0.0710 -3.3199 0.0009 -0.3750 -0.0966\n",
"HM1_Draw -0.0249 0.0719 -0.3467 0.7288 -0.1658 0.1159\n",
"HM2_Draw -0.0540 0.0722 -0.7479 0.4545 -0.1956 0.0876\n",
"AM2_Draw -0.0827 0.0757 -1.0915 0.2751 -0.2311 0.0658\n",
"HTGD_by_MW -0.3303 0.0513 -6.4435 0.0000 -0.4308 -0.2298\n",
"=================================================================\n",
"\n"
]
}
],
"source": [
"cols_to_df = [\"HTWinStreak5\", \"HTLossStreak3\", \"ATWinStreak3\", \n",
" \"ATLossStreak3\", \"Market_Diff\",\n",
" \"Total_Diff\", \"HM1_Draw\", \"HM2_Draw\",\"AM2_Draw\", \"HTGD_by_MW\",\n",
" ]\n",
"\n",
"X=os_data_X[cols_to_df]\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": [
"## Weryfikacja modelu "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of logistic regression classifier on test set: 0.55\n",
"[[283 413]\n",
" [195 465]]\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": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.59 0.41 0.48 696\n",
" 1 0.53 0.70 0.60 660\n",
"\n",
" micro avg 0.55 0.55 0.55 1356\n",
" macro avg 0.56 0.56 0.54 1356\n",
"weighted avg 0.56 0.55 0.54 1356\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"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()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nowe dane"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>Ath Bilbao</td>\n",
" <td>Alaves</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ath Madrid</td>\n",
" <td>Valladolid</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Leganes</td>\n",
" <td>Celta</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Barcelona</td>\n",
" <td>Levante</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Valencia</td>\n",
" <td>Eibar</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Girona</td>\n",
" <td>Sevilla</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Sociedad</td>\n",
" <td>Getafe</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Villarreal</td>\n",
" <td>Huesca</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Vallecano</td>\n",
" <td>Real Madrid</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Betis</td>\n",
" <td>Espanol</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HomeTeam AwayTeam 0\n",
"0 Ath Bilbao Alaves 0\n",
"1 Ath Madrid Valladolid 0\n",
"2 Leganes Celta 1\n",
"3 Barcelona Levante 0\n",
"4 Valencia Eibar 0\n",
"5 Girona Sevilla 0\n",
"6 Sociedad Getafe 0\n",
"7 Villarreal Huesca 0\n",
"8 Vallecano Real Madrid 1\n",
"9 Betis Espanol 1"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loc = dics + \":/data_football/laliga/\"\n",
"file = \"predict\"\n",
"predict = pd.read_csv(loc + 'predict.csv', index_col = 0)\n",
"loc_to_functions = dics + \":/data_football/\"\n",
"\n",
"\n",
"import os, re\n",
"os.chdir(loc_to_functions)\n",
"\n",
"import functions_to_mlpy as f1\n",
"\n",
"\n",
"df = f1.dataset_prepering(loc, file)\n",
"\n",
"df = df[cols_to_df ]\n",
"y_pred_new = logreg.predict(df)\n",
"\n",
"df = f1.dataset_prepering(loc, file)\n",
"\n",
"\n",
"final = pd.concat([df[[\"HomeTeam\", \"AwayTeam\"]].reset_index(drop=True), pd.DataFrame(y_pred_new)], axis=1)\n",
"final"
]
}
],
"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": "3.6.5"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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