titanik.ipynb

{"nbformat_minor": 0, "cells": [{"source": "\u0412 \u0437\u0430\u0434\u0430\u0447\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043d\u0430\u0443\u0447\u0438\u0442\u044c\u0441\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u0442\u044c, \u0443\u0442\u043e\u043d\u0435\u0442 \u043b\u0438 \u043f\u043e\u0441\u0435\u0442\u0438\u0442\u0435\u043b\u044c \u0422\u0438\u0442\u0430\u043d\u0438\u043a\u0430 \u0438\u043b\u0438 \u043d\u0435\u0442 \u043f\u043e \u043d\u0430\u043b\u0438\u0447\u0438\u044e (\u043d\u0435 \u0432\u0441\u0435\u0433\u0434\u0430 \u0445\u043e\u0440\u043e\u0448\u043e \u0437\u0430\u043f\u043e\u043b\u043d\u0435\u043d\u043d\u044b\u0445) \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432.\n\ntrain.csv \u2014 \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u0431\u0443\u0434\u0435\u0442 \u0441\u0442\u0440\u043e\u0438\u0442\u044c\u0441\u044f \u043c\u043e\u0434\u0435\u043b\u044c (\u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0430\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0430)<br>\ntest.csv \u2014 \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438 \u043c\u043e\u0434\u0435\u043b\u0438\n\n\u0414\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u044b \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438\n\nPassengerId \u2014 \u0438\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430<br>\nSurvival \u2014 \u043f\u043e\u043b\u0435 \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0443\u043a\u0430\u0437\u0430\u043d\u043e \u0441\u043f\u0430\u0441\u0441\u044f \u0447\u0435\u043b\u043e\u0432\u0435\u043a (1) \u0438\u043b\u0438 \u043d\u0435\u0442 (0)<br>\nPclass \u2014 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0441\u043e\u0446\u0438\u0430\u043b\u044c\u043d\u043e-\u044d\u043a\u043e\u043d\u043e\u043c\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0441\u0442\u0430\u0442\u0443\u0441 (\u0432\u044b\u0441\u043e\u043a\u0438\u0439, \u0441\u0440\u0435\u0434\u043d\u0438\u0439, \u043d\u0438\u0437\u043a\u0438\u0439)<br>\nName \u2014 \u0438\u043c\u044f \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430<br>\nSex \u2014 \u043f\u043e\u043b \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430<br>\nAge \u2014 \u0432\u043e\u0437\u0440\u0430\u0441\u0442<br>\nSibSp \u2014 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435 \u0440\u043e\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u0438\u043a\u043e\u0432 2-\u0433\u043e \u043f\u043e\u0440\u044f\u0434\u043a\u0430 (\u043c\u0443\u0436, \u0436\u0435\u043d\u0430, \u0431\u0440\u0430\u0442\u044c\u044f, \u0441\u0435\u0442\u0440\u044b)<br>\nParch \u2014 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435 \u0440\u043e\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u0438\u043a\u043e\u0432 \u043d\u0430 \u0431\u043e\u0440\u0442\u0443 1-\u0433\u043e \u043f\u043e\u0440\u044f\u0434\u043a\u0430 (\u043c\u0430\u0442\u044c, \u043e\u0442\u0435\u0446, \u0434\u0435\u0442\u0438)<br>\nTicket \u2014 \u043d\u043e\u043c\u0435\u0440 \u0431\u0438\u043b\u0435\u0442\u0430<br>\nFare \u2014 \u0446\u0435\u043d\u0430 \u0431\u0438\u043b\u0435\u0442\u0430<br>\nCabin \u2014 \u043a\u0430\u044e\u0442\u0430<br>\nEmbarked \u2014 \u043f\u043e\u0440\u0442 \u043f\u043e\u0441\u0430\u0434\u043a\u0438 (C \u2014 Cherbourg, Q \u2014 Queenstown, S \u2014 Southampton)<br>\n\n\u0414\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e Pandas \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043d\u0430 \u0434\u0430\u043d\u043d\u044b\u0435", "cell_type": "markdown", "metadata": {}}, {"execution_count": 193, "cell_type": "code", "source": "import pandas as pd\n\ntrain = pd.read_csv('train.csv')\ntest = pd.read_csv('test.csv')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 194, "cell_type": "code", "source": "train.head(10)", "outputs": [{"execution_count": 194, "output_type": "execute_result", "data": {"text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n5            6         0       3   \n6            7         0       1   \n7            8         0       3   \n8            9         1       3   \n9           10         1       2   \n\n                                                Name     Sex  Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male   22      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n2                             Heikkinen, Miss. Laina  female   26      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n4                           Allen, Mr. William Henry    male   35      0   \n5                                   Moran, Mr. James    male  NaN      0   \n6                            McCarthy, Mr. Timothy J    male   54      0   \n7                     Palsson, Master. Gosta Leonard    male    2      3   \n8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  \n5      0            330877   8.4583   NaN        Q  \n6      0             17463  51.8625   E46        S  \n7      1            349909  21.0750   NaN        S  \n8      2            347742  11.1333   NaN        S  \n9      0            237736  30.0708   NaN        C  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Name</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</th>\n      <th>Cabin</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>  1</td>\n      <td> 0</td>\n      <td> 3</td>\n      <td>                           Braund, Mr. Owen Harris</td>\n      <td>   male</td>\n      <td> 22</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>        A/5 21171</td>\n      <td>  7.2500</td>\n      <td>  NaN</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>  2</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n      <td> female</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>         PC 17599</td>\n      <td> 71.2833</td>\n      <td>  C85</td>\n      <td> C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>  3</td>\n      <td> 1</td>\n      <td> 3</td>\n      <td>                            Heikkinen, Miss. Laina</td>\n      <td> female</td>\n      <td> 26</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> STON/O2. 3101282</td>\n      <td>  7.9250</td>\n      <td>  NaN</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>  4</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n      <td> female</td>\n      <td> 35</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>           113803</td>\n      <td> 53.1000</td>\n      <td> C123</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  5</td>\n      <td> 0</td>\n      <td> 3</td>\n      <td>                          Allen, Mr. William Henry</td>\n      <td>   male</td>\n      <td> 35</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>           373450</td>\n      <td>  8.0500</td>\n      <td>  NaN</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>  6</td>\n      <td> 0</td>\n      <td> 3</td>\n      <td>                                  Moran, Mr. James</td>\n      <td>   male</td>\n      <td>NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>           330877</td>\n      <td>  8.4583</td>\n      <td>  NaN</td>\n      <td> Q</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  7</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td>                           McCarthy, Mr. Timothy J</td>\n      <td>   male</td>\n      <td> 54</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>            17463</td>\n      <td> 51.8625</td>\n      <td>  E46</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>  8</td>\n      <td> 0</td>\n      <td> 3</td>\n      <td>                    Palsson, Master. Gosta Leonard</td>\n      <td>   male</td>\n      <td>  2</td>\n      <td> 3</td>\n      <td> 1</td>\n      <td>           349909</td>\n      <td> 21.0750</td>\n      <td>  NaN</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>  9</td>\n      <td> 1</td>\n      <td> 3</td>\n      <td> Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n      <td> female</td>\n      <td> 27</td>\n      <td> 0</td>\n      <td> 2</td>\n      <td>           347742</td>\n      <td> 11.1333</td>\n      <td>  NaN</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td> 10</td>\n      <td> 1</td>\n      <td> 2</td>\n      <td>               Nasser, Mrs. Nicholas (Adele Achem)</td>\n      <td> female</td>\n      <td> 14</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>           237736</td>\n      <td> 30.0708</td>\n      <td>  NaN</td>\n      <td> C</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 195, "cell_type": "code", "source": "train.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId    891 non-null int64\nSurvived       891 non-null int64\nPclass         891 non-null int64\nName           891 non-null object\nSex            891 non-null object\nAge            714 non-null float64\nSibSp          891 non-null int64\nParch          891 non-null int64\nTicket         891 non-null object\nFare           891 non-null float64\nCabin          204 non-null object\nEmbarked       889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 90.5+ KB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 196, "cell_type": "code", "source": "train.describe()", "outputs": [{"execution_count": 196, "output_type": "execute_result", "data": {"text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 714.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td> 446.000000</td>\n      <td>   0.383838</td>\n      <td>   2.308642</td>\n      <td>  29.699118</td>\n      <td>   0.523008</td>\n      <td>   0.381594</td>\n      <td>  32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td> 257.353842</td>\n      <td>   0.486592</td>\n      <td>   0.836071</td>\n      <td>  14.526497</td>\n      <td>   1.102743</td>\n      <td>   0.806057</td>\n      <td>  49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>   1.000000</td>\n      <td>   0.000000</td>\n      <td>   1.000000</td>\n      <td>   0.420000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td> 223.500000</td>\n      <td>   0.000000</td>\n      <td>   2.000000</td>\n      <td>  20.125000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>   7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td> 446.000000</td>\n      <td>   0.000000</td>\n      <td>   3.000000</td>\n      <td>  28.000000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>  14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td> 668.500000</td>\n      <td>   1.000000</td>\n      <td>   3.000000</td>\n      <td>  38.000000</td>\n      <td>   1.000000</td>\n      <td>   0.000000</td>\n      <td>  31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td> 891.000000</td>\n      <td>   1.000000</td>\n      <td>   3.000000</td>\n      <td>  80.000000</td>\n      <td>   8.000000</td>\n      <td>   6.000000</td>\n      <td> 512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u041c\u043e\u0436\u043d\u043e \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u0442\u044c, \u0447\u0442\u043e \u0447\u0435\u043c \u0432\u044b\u0448\u0435 \u0441\u043e\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0439 \u0441\u0442\u0430\u0442\u0443\u0441, \u0442\u0435\u043c \u0431\u043e\u043b\u044c\u0448\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u0441\u043f\u0430\u0441\u0435\u043d\u0438\u044f. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043b\u0438 \u044d\u0442\u043e \u0442\u0430\u043a. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u043c \u0441\u0432\u043e\u0434\u043d\u0443\u044e. ", "cell_type": "markdown", "metadata": {}}, {"execution_count": 197, "cell_type": "code", "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\n\ntrain.pivot_table('PassengerId', 'Pclass', 'Survived', 'count').plot(kind='bar', stacked=True)", "outputs": [{"execution_count": 197, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10c651a10>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x10c651990>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u0432\u043b\u0438\u044f\u0435\u0442 \u043a\u043e\u043b-\u0432\u043e \u0440\u043e\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u0438\u043a\u043e\u0432 \u043d\u0430 \u0446\u0435\u043b\u0435\u0432\u0443\u044e \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u043c \u0442\u0430\u043a\u0436\u0435 \u0441\u0432\u043e\u0434\u043d\u044b\u0435:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 198, "cell_type": "code", "source": "fig, axes = plt.subplots(ncols=2)\ntrain.pivot_table('PassengerId', ['SibSp'], 'Survived', 'count').plot(ax=axes[0], title='SibSp')\ntrain.pivot_table('PassengerId', ['Parch'], 'Survived', 'count').plot(ax=axes[1], title='Parch')", "outputs": [{"execution_count": 198, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10c8369d0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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wfgWbJtW2vD5CVm/IKgB7NNi+oZelEYcuno489dSi7hmlaejl1ySFj55JZlkn\n2LakanGclc+dymi24Ouaqq2oXh8D+N0TcN4hcO7pQ50vaL2LzOpNkE99jG7+vhquj9FmDgKubKB9\n5KoHmVFXnrqSwkeXAJfZ/k567B5gku2FksYB19reVtJkANsnpO0uB0q2Z5fJM1NZm6Qg0lYu+Z/V\n9XMp8H/28uJKQVCRTsfUmcp8YAuX6psEIvEB4BCbQ9trXdDtZBJTTwsfnQHc3e/QU2YAR6TbRwAX\nlR0/XNIoSeOBbYAbB8t1yUuAy4D31bKByIAJuosXgR0aaB8j9SAz6omp9xc+2lMD6zbuB5wA7CPp\nPmCvdB/bdwP9hY8uo3rho/OAD9RhQ0WnXtS4bsjqnKwCMIMkBFMvDb0ojTh08XTkqacW9WS/DFX4\n6HLbT9je2/YE2/vaXlx2Tb2Fj64AXqc+1RqFx0g96CYupjGn/hiwqkQ92WBBUJWO135Rn04H7nHJ\n36rcnj2BqXbDWQXBMKQAMfVRJBlh27vkIRfSWPk67gA+bHNbWw0Muppuqf0yndohmBipB12DS36J\nJPR4YAOXRa56kAlFcOqzgI3VpwlV2iwANpFWtreocd2Q1TlZBaHRuHrdL0sjDl08HXnqqUXHnbpL\nfpnkxWrF0brN88BTwCvzsivobdpY06ify4Hd1ae16jQpRupBJnQ8pg6gPu0KnAW8plJur8QtwH/a\n3JSPlUG3Uk/csR01jQbrVp9mAqe65Atr28xBwFE272z4hoNhQ7fE1CEpOzCKZFZgJSKuHmRGG2sa\nldNICCZy1YNMKIRTT0fn51P9hemQTr2ocd2Q1TlZTejekuxqGpVzMXCg+jSiDjPmAePrKVwXceji\n6chTTy0K4dRTpgOHq0+VbIqRepA5g2salZ9LJ801VtOo/GTJ84CHgV1r2ZEWrnsaGFurbRBUo+6C\nXu3GJd+hPj0NvAW4fogm84E3rHTdQNGr1m0IWT0hq17Smka/Bn5mu7/MxSJJY8tqGj2aHn+IFQcV\nm6bHhpJ7Fv3FxXZiIZvyaeCP6blJULE42Vz45Hul/7sz52JqHdvvP9ZlxeeG3Lc9qw3yGy5WV4gX\npcuP9+krwMYu+dMrX8NbgZNs3pKHjUH3UueLUpHEzB+3/fmy4yelx05Mi9OtO+hF6RsZeFG69eAS\nGEMkAewCnOOSX1PbbqYDl9qcW/fNBsOKbnpR2s/5wPvVp6F+QURMPWRlSTtrGpVzC7CO+rRNHW3r\nelkacejeWSVGAAAe0klEQVTi6chTTy0K5dRd8gMk+bpvH+L0w8AYqTgho6B7aXNNowE9JS8jeWH6\nrjqaR6560DKFcuopQ5YNsHmJpPDRuBWPFzOuG7I6J6uA1Fvgq66Reh7fVV7/H3Ev2VNEp/4L4GD1\nabUhzkUGTNCNXA3spD5tUKNdQ8vaBcFQFM6pu+SHgVuBA4Y4vYBBTr2ocd2Q1TlZRcMlPwdcA+xf\no+l8YKzEqGqNIg5dPB156qlF4Zx6SqXKjTFSD7qVmrNLbZaSpElunotFQU9SqJTG5ef7tD7JT9HN\nXPLTA9fxX8AWNkfnYGbQpdST9pW3bvVpDHAvMMYlv1D5eq4GTrQbWrg6GCZ0Y0ojAC75CeD3JPU2\nyomRetCVuORFJOmQtRZ6iRowQUsU0qmnnAd8cNCxlZx6UeO6IatzsgpMPQW+ar4sjTh08XTkqacW\nNZ26pJ9KWiTpjrJjUyUtKJu0sX/ZuUZqTldjBvBm9WmjsmMxUg+6mRnAQepTtZ/PkasetETNmLqk\n3YElwDm2t0+PlYBnbJ8yqG3DNaer6u7TdOD3Lvm05DpGAM8Ba9tUjEsGw5sixtQBUmd+P/A+l5Ky\nvytfz5uAH9js0kYzgy4lk5i67T8ATw4lf4hjzdScrsYKWTA2LwOPULvkaRAUjrTEdK0QTIzUg5Zo\nJab+WUm3STpDA0t+NVNzuhpXAK9Vn8pDLiuEYIoa1w1ZnZNVcGZQvWTAY8CqEqMrNYg4dPF05Kmn\nFs069dNIXuZMJBk5n1ylbYXl6XRWGpufKumY8i9E0iRJk9LUrwuZzbFl5+fDN/cdqn2R9ilbxalV\necDETt9Pwb+vY8r7E8Xmj8Cr1KchBzs2JmaWBi1QV566kpVhLu6PqVc6p6RUKbZPSM9dDpRszx50\nTd0xT/Xp7cBJLvkNybWcBDxpc3w91wfDj6LG1Je36dPPSd4V/WhoGcwAzrSpubZpMLzIJKZeQXB5\nUa1DgP7MmBnA4ZJGSRoPbAPc2IyOMmYBG6tPE9L9yIAJup1acfXIVQ+app6UxunAn4BXS5ov6WPA\niZJul3QbyWSKz0NLNacr4pJfTmX2vzCNmHrI6nYuB3ZXn9aqcL7qy9KIQxdPR556alGzNrntoWqw\n/LRK+2nAtFaMGoLpwFnq0zfAMVIPuhqX/JT6NBvYB4YMsTwItDLHIxjGFHlGaTmzgVEkL9NWcOpF\nrekdsjonq17UuYl1UD0EU/VFadQgL56OPPXUoiuceprfez5JCOYxYE2JNTprVdADnAnsN+iYgVPK\nVkO6DJZPrDsM2C695lRJrTw/FwMHqk8jhjg3DxgvDTkXJAiq0hVOPWU6cDhTJcrqqhc1rhuyOier\nXjo5sc4lzyNZonHXle1iCfA0MHaoayMOXTwdeeqpRdc4dZd8B0lHfwuRARO0lzwm1kHtEEzMLA0a\nptsWce4vG7DcqRc1rhuyOierRU4DvpFuH0cyse7jFdpWnFhHEkIBWAzM6b+//tFcuj+D+/mVpMsG\nnwc/CIyX9Iqy9iuNBgfJ67r9/mPt1pfH92V7VhvkH0PyLnEedVLIRTIqXtenrYA/c9xzp/Pyas/b\nyx++IFhOQ5PbOjuxbhWSEf8kl3zfinL4JvC8zXH1yAqGB22bfNQpXPIDwFx2+9ZaREw9ZLWBPCfW\nueRlJC9Mh6oFUzH8EnHo4unIU08tusqpp0xnh59tR8TUgxbp9MS6lEoFvmJWadAUXRV+AVCfNmbZ\niHs4/qkFfnHN7bK2Leh+il77ZYX2fVodWASMd8mPD8hhPHCdHYtQBwP0XPgFwCU/jFeZw4RLt+y0\nLUHQKi75OeAaYP9Bp+YDYyRG5W9V0M10nVMHYJWl5/C66aMk1ilqXDdkdU5WF7JSaqPNUuAhWHmk\nHnHo4unIU08tutOpy7/mVVeJLa/dttOmBEEGXArsqz6tOuh45KoHDdN1MfXlMj72tkdZMuYc/+KX\nX8zKrqA36KaY+vLr+vQnYKpLvnJAFj8BbrYZsu56MPzoyZj6cu57512M+8vguh1B0K0MNbs0RupB\nw3SvU7/1o39i7Ye31sY6OCuRRY0Rh6xhwQzgIPWpfBQ2ZFpjxKGLpyNPPbXoXqf+7Csf5KE3Pcz2\n7NFpU4IgA/4KvAi8vuxYrFUaNEz3xtTFvrz+7P/lkCOfcslvy8q2oPvpxpg6gPp0CrDYJX8jkcUr\ngb/abJCljUH30tsxdZjPXYeuAbxWfdq008YEQQYMjqs/BqwqMbpD9gRdSHc79aWrb8Lt3AAcmoXA\nosaIQ9aw4Y/A+P5Bio0ZIgQTceji6chTTy3qWXh6qCW/1pc0U9J9kq4sqzmNsl3yqyLpQgIv8PeN\nZgOHt0tPEOSFS36JpK7MgWWHHyQyYIIGqGekPtSSX5OBmbYnAFen+yj7Jb9qMZ+77/4dsEValrcl\nilofPGQNKwZXbVxppB7rehZPR556alHT4VZY8usg4Ox0+2zg3el2pkt+1cF8nttwY+BXJH9MgqDb\nuRzYXX1aK92PXPWgIZodRY+xvSjdXgSMSbfbseRXNebDKXuRLErdcgimqDHikDV8cMlPAbOBfdJD\nK+WqRxy6eDry1FOLlpezs21J1fIiW13yq8oSVZ4P67yWbzCSDzFOfXqtS76rIEteTQRaub7859xE\nSYVZgqyA31fDS34VnP4smAuJXPWgQerKU9egJb8k3QNMsr0wXSnmWtvbqg1LflW3iw8Dh9i8V306\nGXjWJX+tVblBd9OteerLZfRpS+AmYCxTvTpJauMaaTZMMIxpZ576DOCIdPsI4KKy45ku+VWDy4C9\nJDYkWZT68EHTrIOgIoXN7Cp5HvAwsGua5fU0MLZd+oLeop6UxsFLfn0UOAHYR9J9wF7pfjuX/BoS\nm8dh+mzgY8AtgICdmpVX1BhxyGobRc7sKp+ItMLL0ohDF09HnnpqUU/2ywdsb2x7lO3NbJ9p+wnb\ne9ueYHtf24vL2k+zvbXtbW1f0V7zAWZeCPwnU70KGb0wDYYHBc/sKnfqsV5pUDfdPKMUAPunPwIe\nB95B4tQPU19zI6ii5l2HrFwpSmbXLcA66tMEBr0sjdzu4unIU08tWs5+KQinAp92ye9Un54G3kwy\n5ToImqaTmV22Z6lPFzObY+Ckf8GXNm70+tjvif3GM7ts5/5J1GYmaxJ4dfBj4Fcxla8ylR80KytL\nu0JWx2S5gbZbAneU7d8DjE23xwH3pNuTgcll7S4H3tSK7pq2TeWdTOU68F7gWe34rvL4/+i0nh67\nF9dq0/XhFwCb50jin/9JEoJ5v/rUK79CgnwpSmYXwDXARHb86ZPErNKgTrq2nvrKMtkKuAHYnKn6\nPTDFJV+VpY6gO6i3f6WZXXsAG5LEz78O/JYkg2tzkp+8hzpNBJB0LEmm1VLgaA+RCJB131afLmLp\nqN/wPy/8BFjb5sWsZAfdRz39q2dGszYPSNxMUoa3PwsmnHpQEdsfqHBq7wrtpwHT2mfRkMxg5IsH\nAg+R/KH5W876gy6j68Mvg3JDfwh8imSkdYj6NKoFWVnaFbJylNVjXArsy8hn55GGYCK3u3g68tRT\ni6536oO4DBjDVI8hmQDVtll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"text/plain": "<matplotlib.figure.Figure at 0x10c659150>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u0442\u0435\u043f\u0435\u0440\u044c, \u0432\u0441\u0435 \u043b\u0438 \u0445\u043e\u0440\u043e\u0448\u043e \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438, \u0435\u0441\u0442\u044c \u043b\u0438 \u0432 \u043d\u0438\u0445 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u0438. \u041e\u0447\u0435\u043d\u044c \u043f\u043e\u043b\u0435\u0437\u043d\u043e \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c info() \u0438 describe()", "cell_type": "markdown", "metadata": {}}, {"execution_count": 199, "cell_type": "code", "source": "train.info()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId    891 non-null int64\nSurvived       891 non-null int64\nPclass         891 non-null int64\nName           891 non-null object\nSex            891 non-null object\nAge            714 non-null float64\nSibSp          891 non-null int64\nParch          891 non-null int64\nTicket         891 non-null object\nFare           891 non-null float64\nCabin          204 non-null object\nEmbarked       889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 90.5+ KB\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 200, "cell_type": "code", "source": "train.describe()", "outputs": [{"execution_count": 200, "output_type": "execute_result", "data": {"text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 714.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n      <td> 891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td> 446.000000</td>\n      <td>   0.383838</td>\n      <td>   2.308642</td>\n      <td>  29.699118</td>\n      <td>   0.523008</td>\n      <td>   0.381594</td>\n      <td>  32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td> 257.353842</td>\n      <td>   0.486592</td>\n      <td>   0.836071</td>\n      <td>  14.526497</td>\n      <td>   1.102743</td>\n      <td>   0.806057</td>\n      <td>  49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>   1.000000</td>\n      <td>   0.000000</td>\n      <td>   1.000000</td>\n      <td>   0.420000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td> 223.500000</td>\n      <td>   0.000000</td>\n      <td>   2.000000</td>\n      <td>  20.125000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>   7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td> 446.000000</td>\n      <td>   0.000000</td>\n      <td>   3.000000</td>\n      <td>  28.000000</td>\n      <td>   0.000000</td>\n      <td>   0.000000</td>\n      <td>  14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td> 668.500000</td>\n      <td>   1.000000</td>\n      <td>   3.000000</td>\n      <td>  38.000000</td>\n      <td>   1.000000</td>\n      <td>   0.000000</td>\n      <td>  31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td> 891.000000</td>\n      <td>   1.000000</td>\n      <td>   3.000000</td>\n      <td>  80.000000</td>\n      <td>   8.000000</td>\n      <td>   6.000000</td>\n      <td> 512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 201, "cell_type": "code", "source": "train.groupby('Sex').median().Age.female, train.groupby('Sex').median().Age.male", "outputs": [{"execution_count": 201, "output_type": "execute_result", "data": {"text/plain": "(27.0, 29.0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0412\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043d\u043e\u043c\u0435\u0440\u0430 \u043a\u0430\u044e\u0442 \u0437\u0430\u043f\u043e\u043b\u043d\u0435\u043d\u044b \u043e\u0447\u0435\u043d\u044c \u043f\u043b\u043e\u0445\u043e, \u0432\u043e\u0437\u0440\u0430\u0441\u0442 \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u043f\u044b\u0442\u0430\u0442\u044c\u0441\u044f \u043a\u0430\u043a-\u0442\u043e \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c: \u0435\u0441\u0442\u044c 2 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u0430 - \u0437\u0430\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u0435\u0434\u0438\u0430\u043d\u043d\u044b\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c \u0438\u043b\u0438 \u0436\u0435 \u043d\u0430\u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u043f\u043e \u0434\u0440\u0443\u0433\u0438\u043c \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430\u043c. \u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043c \u043f\u0440\u043e\u0441\u0442\u044b\u043c \u043f\u0443\u0442\u0435\u043c - \u0437\u0430\u043f\u043e\u043b\u043d\u0438\u043c \u043c\u0435\u0434\u0438\u0430\u043d\u043d\u044b\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c", "cell_type": "markdown", "metadata": {}}, {"execution_count": 202, "cell_type": "code", "source": "#train[train.Sex=='female'].Age[train.Age.isnull()]\ntrain.Age[train.Age.isnull()] = train.median().Age", "outputs": [{"output_type": "stream", "name": "stderr", "text": "/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  from IPython.kernel.zmq import kernelapp as app\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u041e\u0441\u0442\u0430\u043b\u0441\u044f \u0435\u0449\u0435 \u043f\u043e\u0440\u0442 \u043f\u043e\u0441\u0430\u0434\u043a\u0438 - \u0442\u0430\u043c \u0432\u0441\u0435\u0433\u043e 2 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043d\u0435 \u0437\u0430\u043f\u043e\u043b\u043d\u0435\u043d\u044b. \u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u0440\u0438\u0441\u0432\u043e\u0438\u043c \u044d\u0442\u0438 \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430\u043c \u043f\u043e\u0440\u0442 \u0432 \u043a\u043e\u0442\u043e\u0440\u043e\u043c \u0441\u0435\u043b\u043e \u0431\u043e\u043b\u044c\u0448\u0435 \u0432\u0441\u0435\u0433\u043e \u043b\u044e\u0434\u0435\u0439:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 203, "cell_type": "code", "source": "MaxPassEmbarked = train.groupby('Embarked').count()['PassengerId']\ntrain.Embarked[train.Embarked.isnull()] = MaxPassEmbarked[MaxPassEmbarked == MaxPassEmbarked.max()].index[0]", "outputs": [{"output_type": "stream", "name": "stderr", "text": "/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  from IPython.kernel.zmq import kernelapp as app\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043d\u0430 \u043e\u0441\u0442\u0430\u0432\u0448\u0438\u0435\u0441\u044f \u0434\u0430\u043d\u043d\u044b\u0435. \u0412\u0430\u0436\u043d\u043e \u043f\u043e\u043d\u0438\u043c\u0430\u0442\u044c, \u0447\u0442\u043e \u043d\u0435\u043d\u0443\u0436\u043d\u044b\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0438 \u043d\u0443\u0436\u043d\u043e \u043e\u0442\u0431\u0440\u0430\u0441\u044b\u0432\u0430\u0442\u044c - \u0442.\u043a. \u043e\u043d\u0438 \u0434\u043e\u0431\u0430\u0432\u043b\u044f\u044e\u0442 \u0448\u0443\u043c. <br>\n\u0423 \u043d\u0430\u0441 \u043e\u0441\u0442\u0430\u043b\u0438\u0441\u044c: \u0418\u043c\u044f (\u0438\u0437 \u043d\u0435\u0433\u043e \u043c\u043e\u0436\u043d\u043e \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c \u0432\u044b\u0442\u0430\u0449\u0438\u0442\u044c \u043f\u043e\u043b - \u0443\u043f\u0440\u0430\u0436\u043d\u0435\u043d\u0438\u0435 \u043d\u0430 \u0434\u043e\u043c), \u041d\u043e\u043c\u0435\u0440 \u0431\u0438\u043b\u0435\u0442\u0430 (\u0442\u0443\u0442 \u0440\u0430\u0434\u0438 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0430 \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u043d\u0430 \u043a\u0430\u043a\u0443\u044e-\u043d\u0438\u0431\u0443\u0434\u044c \u0447\u0435\u0442\u043d\u043e\u0441\u0442\u044c, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440), \u041d\u043e\u043c\u0435\u0440 \u043a\u0430\u044e\u0442\u044b (\u0442\u043e\u0436\u0435 \u0432 \u043d\u0443\u043c\u0435\u0440\u043e\u043b\u043e\u0433\u0438\u044e \u043d\u0435 \u0432\u0435\u0440\u0438\u043c)    ", "cell_type": "markdown", "metadata": {}}, {"execution_count": 204, "cell_type": "code", "source": "train = train.drop(['PassengerId','Name','Ticket','Cabin'], axis=1)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u043e\u0441\u0442\u0430\u043b\u043e\u0441\u044c", "cell_type": "markdown", "metadata": {}}, {"execution_count": 205, "cell_type": "code", "source": "train.head()", "outputs": [{"execution_count": 205, "output_type": "execute_result", "data": {"text/plain": "   Survived  Pclass     Sex  Age  SibSp  Parch     Fare Embarked\n0         0       3    male   22      1      0   7.2500        S\n1         1       1  female   38      1      0  71.2833        C\n2         1       3  female   26      0      0   7.9250        S\n3         1       1  female   35      1      0  53.1000        S\n4         0       3    male   35      0      0   8.0500        S", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Sex</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td>   male</td>\n      <td> 22</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  7.2500</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> female</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 71.2833</td>\n      <td> C</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> female</td>\n      <td> 26</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  7.9250</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> female</td>\n      <td> 35</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 53.1000</td>\n      <td> S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td>   male</td>\n      <td> 35</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  8.0500</td>\n      <td> S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0427\u0442\u043e \u0434\u0435\u043b\u0430\u0442\u044c \u0441 \u043a\u0430\u0442\u0435\u0433\u043e\u0440\u0438\u0430\u043b\u044c\u043d\u044b\u043c\u0438 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u043c? \u0418\u0445 \u043d\u0443\u0436\u043d\u043e \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u0442\u044c - \u0432 sklearn \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u0443\u0436\u0435 \u0435\u0441\u0442\u044c LabelEncoder", "cell_type": "markdown", "metadata": {}}, {"execution_count": 206, "cell_type": "code", "source": "#from sklearn.preprocessing import LabelEncoder\n#label = LabelEncoder()\n#dicts = {}\n\n## \u043a\u043e\u0434\u0438\u0440\u0443\u0435\u043c \u043f\u043e\u043b \n#label.fit(train.Sex.drop_duplicates()) #\u0437\u0430\u0434\u0430\u0435\u043c \u0441\u043f\u0438\u0441\u043e\u043a \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043b\u044f \u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f\n#dicts['Sex'] = list(label.classes_)\n#train.Sex = label.transform(train.Sex) #\u0437\u0430\u043c\u0435\u043d\u044f\u0435\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0438\u0437 \u0441\u043f\u0438\u0441\u043a\u0430 \u043a\u043e\u0434\u0430\u043c\u0438 \u0437\u0430\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432 \n\n## \u043a\u043e\u0434\u0438\u0440\u0443\u0435\u043c \u043f\u043e\u0440\u0442 \u043f\u043e\u0441\u0430\u0434\u043a\u0438\n#label.fit(train.Embarked.drop_duplicates())\n#dicts['Embarked'] = list(label.classes_)\n#train.Embarked = label.transform(train.Embarked)\n\ntrain = pd.get_dummies(train, ['Sex', 'Embarked'])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 207, "cell_type": "code", "source": "train", "outputs": [{"execution_count": 207, "output_type": "execute_result", "data": {"text/plain": "     Survived  Pclass  Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n0           0       3   22      1      0    7.2500           0         1   \n1           1       1   38      1      0   71.2833           1         0   \n2           1       3   26      0      0    7.9250           1         0   \n3           1       1   35      1      0   53.1000           1         0   \n4           0       3   35      0      0    8.0500           0         1   \n5           0       3   28      0      0    8.4583           0         1   \n6           0       1   54      0      0   51.8625           0         1   \n7           0       3    2      3      1   21.0750           0         1   \n8           1       3   27      0      2   11.1333           1         0   \n9           1       2   14      1      0   30.0708           1         0   \n10          1       3    4      1      1   16.7000           1         0   \n11          1       1   58      0      0   26.5500           1         0   \n12          0       3   20      0      0    8.0500           0         1   \n13          0       3   39      1      5   31.2750           0         1   \n14          0       3   14      0      0    7.8542           1         0   \n15          1       2   55      0      0   16.0000           1         0   \n16          0       3    2      4      1   29.1250           0         1   \n17          1       2   28      0      0   13.0000           0         1   \n18          0       3   31      1      0   18.0000           1         0   \n19          1       3   28      0      0    7.2250           1         0   \n20          0       2   35      0      0   26.0000           0         1   \n21          1       2   34      0      0   13.0000           0         1   \n22          1       3   15      0      0    8.0292           1         0   \n23          1       1   28      0      0   35.5000           0         1   \n24          0       3    8      3      1   21.0750           1         0   \n25          1       3   38      1      5   31.3875           1         0   \n26          0       3   28      0      0    7.2250           0         1   \n27          0       1   19      3      2  263.0000           0         1   \n28          1       3   28      0      0    7.8792           1         0   \n29          0       3   28      0      0    7.8958           0         1   \n..        ...     ...  ...    ...    ...       ...         ...       ...   \n861         0       2   21      1      0   11.5000           0         1   \n862         1       1   48      0      0   25.9292           1         0   \n863         0       3   28      8      2   69.5500           1         0   \n864         0       2   24      0      0   13.0000           0         1   \n865         1       2   42      0      0   13.0000           1         0   \n866         1       2   27      1      0   13.8583           1         0   \n867         0       1   31      0      0   50.4958           0         1   \n868         0       3   28      0      0    9.5000           0         1   \n869         1       3    4      1      1   11.1333           0         1   \n870         0       3   26      0      0    7.8958           0         1   \n871         1       1   47      1      1   52.5542           1         0   \n872         0       1   33      0      0    5.0000           0         1   \n873         0       3   47      0      0    9.0000           0         1   \n874         1       2   28      1      0   24.0000           1         0   \n875         1       3   15      0      0    7.2250           1         0   \n876         0       3   20      0      0    9.8458           0         1   \n877         0       3   19      0      0    7.8958           0         1   \n878         0       3   28      0      0    7.8958           0         1   \n879         1       1   56      0      1   83.1583           1         0   \n880         1       2   25      0      1   26.0000           1         0   \n881         0       3   33      0      0    7.8958           0         1   \n882         0       3   22      0      0   10.5167           1         0   \n883         0       2   28      0      0   10.5000           0         1   \n884         0       3   25      0      0    7.0500           0         1   \n885         0       3   39      0      5   29.1250           1         0   \n886         0       2   27      0      0   13.0000           0         1   \n887         1       1   19      0      0   30.0000           1         0   \n888         0       3   28      1      2   23.4500           1         0   \n889         1       1   26      0      0   30.0000           0         1   \n890         0       3   32      0      0    7.7500           0         1   \n\n     Embarked_C  Embarked_Q  Embarked_S  \n0             0           0           1  \n1             1           0           0  \n2             0           0           1  \n3             0           0           1  \n4             0           0           1  \n5             0           1           0  \n6             0           0           1  \n7             0           0           1  \n8             0           0           1  \n9             1           0           0  \n10            0           0           1  \n11            0           0           1  \n12            0           0           1  \n13            0           0           1  \n14            0           0           1  \n15            0           0           1  \n16            0           1           0  \n17            0           0           1  \n18            0           0           1  \n19            1           0           0  \n20            0           0           1  \n21            0           0           1  \n22            0           1           0  \n23            0           0           1  \n24            0           0           1  \n25            0           0           1  \n26            1           0           0  \n27            0           0           1  \n28            0           1           0  \n29            0           0           1  \n..          ...         ...         ...  \n861           0           0           1  \n862           0           0           1  \n863           0           0           1  \n864           0           0           1  \n865           0           0           1  \n866           1           0           0  \n867           0           0           1  \n868           0           0           1  \n869           0           0           1  \n870           0           0           1  \n871           0           0           1  \n872           0           0           1  \n873           0           0           1  \n874           1           0           0  \n875           1           0           0  \n876           0           0           1  \n877           0           0           1  \n878           0           0           1  \n879           1           0           0  \n880           0           0           1  \n881           0           0           1  \n882           0           0           1  \n883           0           0           1  \n884           0           0           1  \n885           0           1           0  \n886           0           0           1  \n887           0           0           1  \n888           0           0           1  \n889           1           0           0  \n890           0           1           0  \n\n[891 rows x 11 columns]", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Sex_female</th>\n      <th>Sex_male</th>\n      <th>Embarked_C</th>\n      <th>Embarked_Q</th>\n      <th>Embarked_S</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0  </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 22</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>   7.2500</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1  </th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  71.2833</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>2  </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 26</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.9250</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>3  </th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 35</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  53.1000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>4  </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 35</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   8.0500</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>5  </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   8.4583</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>6  </th>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 54</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  51.8625</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>7  </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 3</td>\n      <td> 1</td>\n      <td>  21.0750</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>8  </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 27</td>\n      <td> 0</td>\n      <td> 2</td>\n      <td>  11.1333</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>9  </th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 14</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  30.0708</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>10 </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td>  4</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td>  16.7000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>11 </th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 58</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  26.5500</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>12 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 20</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   8.0500</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>13 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 39</td>\n      <td> 1</td>\n      <td> 5</td>\n      <td>  31.2750</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>14 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 14</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8542</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>15 </th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 55</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  16.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>16 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td>  2</td>\n      <td> 4</td>\n      <td> 1</td>\n      <td>  29.1250</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>17 </th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  13.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>18 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 31</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  18.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>19 </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.2250</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>20 </th>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 35</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  26.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>21 </th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 34</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  13.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>22 </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 15</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   8.0292</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>23 </th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  35.5000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>24 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td>  8</td>\n      <td> 3</td>\n      <td> 1</td>\n      <td>  21.0750</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>25 </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 5</td>\n      <td>  31.3875</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>26 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.2250</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>27 </th>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 19</td>\n      <td> 3</td>\n      <td> 2</td>\n      <td> 263.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>28 </th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8792</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>29 </th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>861</th>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 21</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  11.5000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>862</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 48</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  25.9292</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>863</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 8</td>\n      <td> 2</td>\n      <td>  69.5500</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>864</th>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 24</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  13.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>865</th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 42</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  13.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>866</th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 27</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  13.8583</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>867</th>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 31</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  50.4958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>868</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   9.5000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>869</th>\n      <td> 1</td>\n      <td> 3</td>\n      <td>  4</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td>  11.1333</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>870</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 26</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>871</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 47</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td>  52.5542</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>872</th>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 33</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   5.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>873</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 47</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   9.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>874</th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 28</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  24.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>875</th>\n      <td> 1</td>\n      <td> 3</td>\n      <td> 15</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.2250</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>876</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 20</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   9.8458</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>877</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 19</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>878</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>879</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 56</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td>  83.1583</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>880</th>\n      <td> 1</td>\n      <td> 2</td>\n      <td> 25</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td>  26.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>881</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 33</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.8958</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>882</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 22</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  10.5167</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>883</th>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 28</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  10.5000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>884</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 25</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.0500</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>885</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 39</td>\n      <td> 0</td>\n      <td> 5</td>\n      <td>  29.1250</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 27</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  13.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 19</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  30.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 28</td>\n      <td> 1</td>\n      <td> 2</td>\n      <td>  23.4500</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 26</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  30.0000</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td> 0</td>\n      <td> 3</td>\n      <td> 32</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>   7.7500</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n  </tbody>\n</table>\n<p>891 rows \u00d7 11 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0422\u0435\u043f\u0435\u0440\u044c, \u0442\u043e\u0436\u0435 \u0441\u0430\u043c\u043e\u0435 \u043d\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0441 test.csv - \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0442\u0435 \u0436\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0438 \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0442\u0435 \u0436\u0435 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f. \u041d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u043b\u0443\u0447\u0448\u0435 \u0441\u0440\u0430\u0437\u0443 \u043f\u0438\u0441\u0430\u0442\u044c \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u0443\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u044e", "cell_type": "markdown", "metadata": {}}, {"execution_count": 208, "cell_type": "code", "source": "test.Age[test.Age.isnull()] = test.Age.mean()\ntest.Fare[test.Fare.isnull()] = test.Fare.median() #\u0437\u0430\u043f\u043e\u043b\u043d\u044f\u0435\u043c \u043f\u0443\u0441\u0442\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0441\u0440\u0435\u0434\u043d\u0435\u0439 \u0446\u0435\u043d\u043e\u0439 \u0431\u0438\u043b\u0435\u0442\u0430\n\nMaxPassEmbarked = test.groupby('Embarked').count()['PassengerId']\ntest.Embarked[test.Embarked.isnull()] = MaxPassEmbarked[MaxPassEmbarked == MaxPassEmbarked.max()].index[0]\n\nresult = pd.DataFrame(test.PassengerId)\n\ntest = test.drop(['Name','Ticket','Cabin','PassengerId'],axis=1)\n\ntest = pd.get_dummies(test, ['Sex', 'Embarked'])", "outputs": [{"output_type": "stream", "name": "stderr", "text": "/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  if __name__ == '__main__':\n/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n  from IPython.kernel.zmq import kernelapp as app\n/Library/Python/2.7/site-packages/IPython/kernel/__main__.py:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 209, "cell_type": "code", "source": "test.head()", "outputs": [{"execution_count": 209, "output_type": "execute_result", "data": {"text/plain": "   Pclass   Age  SibSp  Parch     Fare  Sex_female  Sex_male  Embarked_C  \\\n0       3  34.5      0      0   7.8292           0         1           0   \n1       3  47.0      1      0   7.0000           1         0           0   \n2       2  62.0      0      0   9.6875           0         1           0   \n3       3  27.0      0      0   8.6625           0         1           0   \n4       3  22.0      1      1  12.2875           1         0           0   \n\n   Embarked_Q  Embarked_S  \n0           1           0  \n1           0           1  \n2           1           0  \n3           0           1  \n4           0           1  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n      <th>Sex_female</th>\n      <th>Sex_male</th>\n      <th>Embarked_C</th>\n      <th>Embarked_Q</th>\n      <th>Embarked_S</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 3</td>\n      <td> 34.5</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  7.8292</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 3</td>\n      <td> 47.0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td>  7.0000</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2</td>\n      <td> 62.0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  9.6875</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 3</td>\n      <td> 27.0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td>  8.6625</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 3</td>\n      <td> 22.0</td>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 12.2875</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0412\u0441\u0435, 80% \u0440\u0430\u0431\u043e\u0442\u044b \u043c\u044b \u0441\u0434\u0435\u043b\u0430\u043b\u0438: \u0442\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u0438\u043c\u0435\u0435\u043c \u043c\u0430\u0442\u0440\u0438\u0446\u0443 \u043e\u0431\u044c\u0435\u043a\u0442-\u043f\u0440\u0438\u0437\u043d\u0430\u043a, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u043c\u043e\u0436\u043d\u043e \"\u0441\u043a\u0430\u0440\u043c\u043b\u0438\u0432\u0430\u0442\u044c\" \u043d\u0430 \u0432\u0445\u043e\u0434 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u043c \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430\u043c \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f<br>\n\u0411\u0443\u0434\u0435\u043c \u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c SVM, KNeighbors, RandomForest, log-regression", "cell_type": "markdown", "metadata": {}}, {"execution_count": 210, "cell_type": "code", "source": "from sklearn import cross_validation, svm\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import roc_curve, auc\nimport pylab as pl", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0414\u043b\u044f \u0443\u0434\u043e\u0431\u0441\u0442\u0432\u0430 (\u0442\u0443\u0442 \u043a\u043e\u043c\u0443 \u043a\u0430\u043a \u043d\u0440\u0430\u0432\u0438\u0442\u0441\u044f, \u0435\u0441\u043b\u0438 \u0432 \u0433\u043e\u043b\u043e\u0432\u0435 \u0432\u0441\u0435 \u0434\u0435\u0440\u0436\u0438\u0442\u0435 - \u043c\u043e\u0436\u043d\u043e \u043d\u0435 \u0434\u0435\u043b\u0430\u0442\u044c) \u0432\u044b\u0434\u0435\u043b\u0438\u043c \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e \u0446\u0435\u043b\u0435\u0432\u0443\u044e \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 211, "cell_type": "code", "source": "train_target = train.Survived\ntrain_features = train.drop(['Survived'], axis=1) #\u0438\u0437 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0443\u0431\u0438\u0440\u0430\u0435\u043c Id \u043f\u0430\u0441\u0441\u0430\u0436\u0438\u0440\u0430 \u0438 \u0444\u043b\u0430\u0433 \u0441\u043f\u0430\u0441\u0441\u044f \u043e\u043d \u0438\u043b\u0438 \u043d\u0435\u0442\nkfold = 5 #\u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u043e\u0434\u0432\u044b\u0431\u043e\u0440\u043e\u043a \u0434\u043b\u044f \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u0438\nitog_val = {} #\u0441\u043f\u0438\u0441\u043e\u043a \u0434\u043b\u044f \u0437\u0430\u043f\u0438\u0441\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043a\u0440\u043e\u0441\u0441 \u0432\u0430\u043b\u0438\u0434\u0430\u0446\u0438\u0438 \u0440\u0430\u0437\u043d\u044b\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 212, "cell_type": "code", "source": "model_rfc = RandomForestClassifier(n_estimators = 70) #\u0432 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0435 \u043f\u0435\u0440\u0435\u0434\u0430\u0435\u043c \u043a\u043e\u043b-\u0432\u043e \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432\nmodel_knc = KNeighborsClassifier(n_neighbors = 18) #\u0432 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0435 \u043f\u0435\u0440\u0435\u0434\u0430\u0435\u043c \u043a\u043e\u043b-\u0432\u043e \u0441\u043e\u0441\u0435\u0434\u0435\u0439\nmodel_lr = LogisticRegression(penalty='l1', tol=0.01) \nmodel_svc = svm.SVC() #\u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e kernek='rbf'", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0414\u0435\u043b\u0430\u0435\u043c \u0441\u043a\u043e\u043b\u044c\u0437\u044f\u0449\u0438\u0439 \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u044c", "cell_type": "markdown", "metadata": {}}, {"execution_count": 213, "cell_type": "code", "source": "scores = cross_validation.cross_val_score(model_rfc, train_features, train_target, cv = kfold)\nitog_val['RandomForestClassifier'] = scores.mean()\nscores = cross_validation.cross_val_score(model_knc, train_features, train_target, cv = kfold)\nitog_val['KNeighborsClassifier'] = scores.mean()\nscores = cross_validation.cross_val_score(model_lr, train_features, train_target, cv = kfold)\nitog_val['LogisticRegression'] = scores.mean()\nscores = cross_validation.cross_val_score(model_svc, train_features, train_target, cv = kfold)\nitog_val['SVC'] = scores.mean()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 214, "cell_type": "code", "source": "pd.DataFrame.from_dict(data = itog_val, orient='index').plot(kind='bar', legend=False)", "outputs": [{"execution_count": 214, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10bdb0310>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x10c1b9e10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0414\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f ROC-\u043a\u0440\u0438\u0432\u044b\u0445 \u043d\u0430\u043c \u043d\u0430\u0434\u043e \u0440\u0443\u043a\u0430\u043c\u0438 (\u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 train_test_split \u043c\u043e\u0434\u0443\u043b\u044f cross_validation) \u0434\u0435\u043b\u0438\u0442\u044c \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u043f\u0440\u043e\u0432\u0435\u0440\u043e\u0447\u043d\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438<br>\n\u041d\u0430 \u0432\u044b\u0445\u043e\u0434\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u043c:<br>\n\u043d\u043e\u0432\u044b\u0439 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432<br>\n\u043f\u0440\u043e\u0432\u0435\u0440\u043e\u0447\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432<br>\n\u043d\u043e\u0432\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439<br>\n\u043f\u0440\u043e\u0432\u0435\u0440\u043e\u0447\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439", "cell_type": "markdown", "metadata": {}}, {"execution_count": 215, "cell_type": "code", "source": "ROCtrainTRN, ROCtestTRN, ROCtrainTRG, ROCtestTRG = cross_validation.train_test_split(train_features, train_target, test_size=0.25) ", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 217, "cell_type": "code", "source": "pl.clf()\nplt.figure(figsize=(8,6))\n#SVC\nmodel_svc.probability = True\nprobas = model_svc.fit(ROCtrainTRN, ROCtrainTRG).predict_proba(ROCtestTRN)\nfpr, tpr, thresholds = roc_curve(ROCtestTRG, probas[:, 1])\nroc_auc  = auc(fpr, tpr)\npl.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % ('SVC', roc_auc))\n#RandomForestClassifier\nprobas = model_rfc.fit(ROCtrainTRN, ROCtrainTRG).predict_proba(ROCtestTRN)\nfpr, tpr, thresholds = roc_curve(ROCtestTRG, probas[:, 1])\nroc_auc  = auc(fpr, tpr)\npl.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % ('RandonForest',roc_auc))\n#KNeighborsClassifier\nprobas = model_knc.fit(ROCtrainTRN, ROCtrainTRG).predict_proba(ROCtestTRN)\nfpr, tpr, thresholds = roc_curve(ROCtestTRG, probas[:, 1])\nroc_auc  = auc(fpr, tpr)\npl.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % ('KNeighborsClassifier',roc_auc))\n#LogisticRegression\nprobas = model_lr.fit(ROCtrainTRN, ROCtrainTRG).predict_proba(ROCtestTRN)\nfpr, tpr, thresholds = roc_curve(ROCtestTRG, probas[:, 1])\nroc_auc  = auc(fpr, tpr)\npl.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % ('LogisticRegression',roc_auc))\npl.plot([0, 1], [0, 1], 'k--')\npl.xlim([0.0, 1.0])\npl.ylim([0.0, 1.0])\npl.xlabel('False Positive Rate')\npl.ylabel('True Positive Rate')\npl.legend(loc=0, fontsize='small')\npl.show()", "outputs": [{"output_type": "display_data", "data": {"text/plain": "<matplotlib.figure.Figure at 0x10c46ea10>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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OpCaZrQdcAOwOnIF7pfwxJ3XA3WexaiAh0i7apy7SxKwTZqcCLxN+qW6lhC4i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\nkiRd02dJvcgt7kc4BI0ALrD9sKS9y/6fOywLt1S4JL1F2AU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"text/plain": "<matplotlib.figure.Figure at 0x10c46e990>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u0427\u0435\u043c \u0432\u044b\u0448\u0435 \u043b\u0435\u0436\u0438\u0442 ROC-\u043a\u0440\u0438\u0432\u0430\u044f - \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435 - \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u0442\u0443\u0442 \u0442\u043e\u0436\u0435 RandomForest \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043e\u0447\u0435\u043d\u044c \u0445\u043e\u0440\u043e\u0448\u043e - \u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u043c \u0435\u0433\u043e \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430", "cell_type": "markdown", "metadata": {}}, {"source": "\u0412\u0441\u0435, \u043e\u0441\u0442\u0430\u043b\u043e\u0441\u044c \u0432\u044b\u0437\u0432\u0430\u0442\u044c predict \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u043e\u0431\u044c\u0435\u043a\u0442\u0430 \u0438\u0437 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0438 \u0438 \u0437\u0430\u0441\u0430\u0431\u043c\u0438\u0442\u0438\u0442\u044c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442", "cell_type": "markdown", "metadata": {}}, {"execution_count": 218, "cell_type": "code", "source": "model_rfc.fit(train_features, train_target)\nresult.insert(1,'Survived', model_rfc.predict(test))\nresult.to_csv('ans.csv', index=False)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u041e\u0431\u0443\u0447\u0438\u043c \u0437\u0430\u043d\u043e\u0432\u043e \u043b\u043e\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u043d\u0430 \u0432\u0441\u0435\u0445 \u0444\u0438\u0447\u0430\u0445", "cell_type": "markdown", "metadata": {}}, {"execution_count": 223, "cell_type": "code", "source": "model_lr.fit(train_features, train_target)", "outputs": [{"execution_count": 223, "output_type": "execute_result", "data": {"text/plain": "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, penalty='l1', random_state=None, tol=0.01)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "\u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043a\u0430\u043a\u0438\u0435 \u0444\u0438\u0447\u0438 \u043a\u0430\u043a\u043e\u0439 \u0432\u043a\u043b\u0430\u0434 \u0432\u043d\u043e\u0441\u044f\u0442 \u0432 \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u043e\u0433\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438", "cell_type": "markdown", "metadata": {}}, {"execution_count": 228, "cell_type": "code", "source": "import numpy\nfor idx in numpy.argsort(model_lr.coef_)[0][:10]: # \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 10 \u0441 \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u043c \u043a\u043e\u044d\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u043c\n    print '{}, importance = {:.5f}'.format(train_features.columns[idx], model_lr.coef_[0][idx])", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Pclass, importance = -0.94786\nSibSp, importance = -0.30920\nParch, importance = -0.08777\nAge, importance = -0.03270\nEmbarked_S, importance = 0.00000\nFare, importance = 0.00321\nEmbarked_Q, importance = 0.19364\nSex_male, importance = 0.27526\nEmbarked_C, importance = 0.37437\nSex_female, importance = 2.95560\n"}], "metadata": {"collapsed": false, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.6", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}