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ryogrid/titanic-self-submit-ryo_grid0190407.ipynb

Created April 7, 2019 00:33
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タイタニックコンペ@Kaggle Kernel環境。Kernelでの出力を手動で submit するためには、/kaggle/working の下に吐かないとだめだった・・・
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titanic-self-submit-ryo_grid0190407.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle\n"
]
}
],
"source": [
"%cd /kaggle"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gender_submission.csv test.csv train.csv\r\n"
]
}
],
"source": [
"!ls input"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"./input/test.csv\n",
"./input/train.csv\n",
"./input/gender_submission.csv\n"
]
}
],
"source": [
"# inputフォルダのファイル\n",
"import glob\n",
"for f in glob.glob(\"./input/*\"):\n",
" print(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 必要なファイルの読み込み"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_kg_hide-output": false
},
"outputs": [],
"source": [
"df = pd.read_csv(\"./input/train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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" <td>13.8583</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>868</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Roebling, Mr. Washington Augustus II</td>\n",
" <td>male</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17590</td>\n",
" <td>50.4958</td>\n",
" <td>A24</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>869</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>van Melkebeke, Mr. Philemon</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345777</td>\n",
" <td>9.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>870</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Master. Harold Theodor</td>\n",
" <td>male</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>871</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Balkic, Mr. Cerin</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349248</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>872</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11751</td>\n",
" <td>52.5542</td>\n",
" <td>D35</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>873</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Carlsson, Mr. Frans Olof</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>695</td>\n",
" <td>5.0000</td>\n",
" <td>B51 B53 B55</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>874</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Vander Cruyssen, Mr. Victor</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>345765</td>\n",
" <td>9.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>875</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
" <td>female</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>P/PP 3381</td>\n",
" <td>24.0000</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>876</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2667</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>877</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Gustafsson, Mr. Alfred Ossian</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7534</td>\n",
" <td>9.8458</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>878</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Petroff, Mr. Nedelio</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349212</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>879</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Laleff, Mr. Kristo</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349217</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>880</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
" <td>female</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>11767</td>\n",
" <td>83.1583</td>\n",
" <td>C50</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>881</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
" <td>female</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>230433</td>\n",
" <td>26.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>882</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Markun, Mr. Johann</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349257</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>883</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7552</td>\n",
" <td>10.5167</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>884</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Banfield, Mr. Frederick James</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>C.A./SOTON 34068</td>\n",
" <td>10.5000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>885</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Sutehall, Mr. Henry Jr</td>\n",
" <td>male</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>SOTON/OQ 392076</td>\n",
" <td>7.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>886</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Rice, Mrs. William (Margaret Norton)</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>382652</td>\n",
" <td>29.1250</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.4500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass ... Fare Cabin Embarked\n",
"0 1 0 3 ... 7.2500 NaN S\n",
"1 2 1 1 ... 71.2833 C85 C\n",
"2 3 1 3 ... 7.9250 NaN S\n",
"3 4 1 1 ... 53.1000 C123 S\n",
"4 5 0 3 ... 8.0500 NaN S\n",
"5 6 0 3 ... 8.4583 NaN Q\n",
"6 7 0 1 ... 51.8625 E46 S\n",
"7 8 0 3 ... 21.0750 NaN S\n",
"8 9 1 3 ... 11.1333 NaN S\n",
"9 10 1 2 ... 30.0708 NaN C\n",
"10 11 1 3 ... 16.7000 G6 S\n",
"11 12 1 1 ... 26.5500 C103 S\n",
"12 13 0 3 ... 8.0500 NaN S\n",
"13 14 0 3 ... 31.2750 NaN S\n",
"14 15 0 3 ... 7.8542 NaN S\n",
"15 16 1 2 ... 16.0000 NaN S\n",
"16 17 0 3 ... 29.1250 NaN Q\n",
"17 18 1 2 ... 13.0000 NaN S\n",
"18 19 0 3 ... 18.0000 NaN S\n",
"19 20 1 3 ... 7.2250 NaN C\n",
"20 21 0 2 ... 26.0000 NaN S\n",
"21 22 1 2 ... 13.0000 D56 S\n",
"22 23 1 3 ... 8.0292 NaN Q\n",
"23 24 1 1 ... 35.5000 A6 S\n",
"24 25 0 3 ... 21.0750 NaN S\n",
"25 26 1 3 ... 31.3875 NaN S\n",
"26 27 0 3 ... 7.2250 NaN C\n",
"27 28 0 1 ... 263.0000 C23 C25 C27 S\n",
"28 29 1 3 ... 7.8792 NaN Q\n",
"29 30 0 3 ... 7.8958 NaN S\n",
".. ... ... ... ... ... ... ...\n",
"861 862 0 2 ... 11.5000 NaN S\n",
"862 863 1 1 ... 25.9292 D17 S\n",
"863 864 0 3 ... 69.5500 NaN S\n",
"864 865 0 2 ... 13.0000 NaN S\n",
"865 866 1 2 ... 13.0000 NaN S\n",
"866 867 1 2 ... 13.8583 NaN C\n",
"867 868 0 1 ... 50.4958 A24 S\n",
"868 869 0 3 ... 9.5000 NaN S\n",
"869 870 1 3 ... 11.1333 NaN S\n",
"870 871 0 3 ... 7.8958 NaN S\n",
"871 872 1 1 ... 52.5542 D35 S\n",
"872 873 0 1 ... 5.0000 B51 B53 B55 S\n",
"873 874 0 3 ... 9.0000 NaN S\n",
"874 875 1 2 ... 24.0000 NaN C\n",
"875 876 1 3 ... 7.2250 NaN C\n",
"876 877 0 3 ... 9.8458 NaN S\n",
"877 878 0 3 ... 7.8958 NaN S\n",
"878 879 0 3 ... 7.8958 NaN S\n",
"879 880 1 1 ... 83.1583 C50 C\n",
"880 881 1 2 ... 26.0000 NaN S\n",
"881 882 0 3 ... 7.8958 NaN S\n",
"882 883 0 3 ... 10.5167 NaN S\n",
"883 884 0 2 ... 10.5000 NaN S\n",
"884 885 0 3 ... 7.0500 NaN S\n",
"885 886 0 3 ... 29.1250 NaN Q\n",
"886 887 0 2 ... 13.0000 NaN S\n",
"887 888 1 1 ... 30.0000 B42 S\n",
"888 889 0 3 ... 23.4500 NaN S\n",
"889 890 1 1 ... 30.0000 C148 C\n",
"890 891 0 3 ... 7.7500 NaN Q\n",
"\n",
"[891 rows x 12 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nameカラムの削除"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 文字列は扱えないので値に置換"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.Embarked = df.Embarked.replace(['C', 'S', 'Q'], [0, 1, 2])\n",
"#df.Cabin = df.Cabin.replace('NaN', 0)\n",
"df.Sex = df.Sex.replace(['male', 'female'], [0, 1])\n",
"df.Age = df.Age.replace('NaN', 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 変換後の値"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
" 'Embarked'],\n",
" dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>0</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.4583</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>51.8625</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21.0750</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>11.1333</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>30.0708</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>16.7000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>58.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.5500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>39.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>31.2750</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8542</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>29.1250</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>18.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>34.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0292</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>35.5000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>8.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21.0750</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>31.3875</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>19.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>263.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8792</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>1.0</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",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>21.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>11.5000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.9292</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>69.5500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>42.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>13.8583</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50.4958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.5000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11.1333</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>52.5542</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>24.0000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.8458</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>83.1583</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>26.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.5167</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.5000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.0500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>29.1250</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.0000</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>23.4500</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.0000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7500</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
"0 0 3 0 22.0 1 0 7.2500 1.0\n",
"1 1 1 1 38.0 1 0 71.2833 0.0\n",
"2 1 3 1 26.0 0 0 7.9250 1.0\n",
"3 1 1 1 35.0 1 0 53.1000 1.0\n",
"4 0 3 0 35.0 0 0 8.0500 1.0\n",
"5 0 3 0 NaN 0 0 8.4583 2.0\n",
"6 0 1 0 54.0 0 0 51.8625 1.0\n",
"7 0 3 0 2.0 3 1 21.0750 1.0\n",
"8 1 3 1 27.0 0 2 11.1333 1.0\n",
"9 1 2 1 14.0 1 0 30.0708 0.0\n",
"10 1 3 1 4.0 1 1 16.7000 1.0\n",
"11 1 1 1 58.0 0 0 26.5500 1.0\n",
"12 0 3 0 20.0 0 0 8.0500 1.0\n",
"13 0 3 0 39.0 1 5 31.2750 1.0\n",
"14 0 3 1 14.0 0 0 7.8542 1.0\n",
"15 1 2 1 55.0 0 0 16.0000 1.0\n",
"16 0 3 0 2.0 4 1 29.1250 2.0\n",
"17 1 2 0 NaN 0 0 13.0000 1.0\n",
"18 0 3 1 31.0 1 0 18.0000 1.0\n",
"19 1 3 1 NaN 0 0 7.2250 0.0\n",
"20 0 2 0 35.0 0 0 26.0000 1.0\n",
"21 1 2 0 34.0 0 0 13.0000 1.0\n",
"22 1 3 1 15.0 0 0 8.0292 2.0\n",
"23 1 1 0 28.0 0 0 35.5000 1.0\n",
"24 0 3 1 8.0 3 1 21.0750 1.0\n",
"25 1 3 1 38.0 1 5 31.3875 1.0\n",
"26 0 3 0 NaN 0 0 7.2250 0.0\n",
"27 0 1 0 19.0 3 2 263.0000 1.0\n",
"28 1 3 1 NaN 0 0 7.8792 2.0\n",
"29 0 3 0 NaN 0 0 7.8958 1.0\n",
".. ... ... ... ... ... ... ... ...\n",
"861 0 2 0 21.0 1 0 11.5000 1.0\n",
"862 1 1 1 48.0 0 0 25.9292 1.0\n",
"863 0 3 1 NaN 8 2 69.5500 1.0\n",
"864 0 2 0 24.0 0 0 13.0000 1.0\n",
"865 1 2 1 42.0 0 0 13.0000 1.0\n",
"866 1 2 1 27.0 1 0 13.8583 0.0\n",
"867 0 1 0 31.0 0 0 50.4958 1.0\n",
"868 0 3 0 NaN 0 0 9.5000 1.0\n",
"869 1 3 0 4.0 1 1 11.1333 1.0\n",
"870 0 3 0 26.0 0 0 7.8958 1.0\n",
"871 1 1 1 47.0 1 1 52.5542 1.0\n",
"872 0 1 0 33.0 0 0 5.0000 1.0\n",
"873 0 3 0 47.0 0 0 9.0000 1.0\n",
"874 1 2 1 28.0 1 0 24.0000 0.0\n",
"875 1 3 1 15.0 0 0 7.2250 0.0\n",
"876 0 3 0 20.0 0 0 9.8458 1.0\n",
"877 0 3 0 19.0 0 0 7.8958 1.0\n",
"878 0 3 0 NaN 0 0 7.8958 1.0\n",
"879 1 1 1 56.0 0 1 83.1583 0.0\n",
"880 1 2 1 25.0 0 1 26.0000 1.0\n",
"881 0 3 0 33.0 0 0 7.8958 1.0\n",
"882 0 3 1 22.0 0 0 10.5167 1.0\n",
"883 0 2 0 28.0 0 0 10.5000 1.0\n",
"884 0 3 0 25.0 0 0 7.0500 1.0\n",
"885 0 3 1 39.0 0 5 29.1250 2.0\n",
"886 0 2 0 27.0 0 0 13.0000 1.0\n",
"887 1 1 1 19.0 0 0 30.0000 1.0\n",
"888 0 3 1 NaN 1 2 23.4500 1.0\n",
"889 1 1 0 26.0 0 0 30.0000 0.0\n",
"890 0 3 0 32.0 0 0 7.7500 2.0\n",
"\n",
"[891 rows x 8 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 解析を始める"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 相関関係を算出する"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<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>Survived</th>\n",
" <td>1.000000</td>\n",
" <td>-0.338481</td>\n",
" <td>0.543351</td>\n",
" <td>-0.077221</td>\n",
" <td>-0.035322</td>\n",
" <td>0.081629</td>\n",
" <td>0.257307</td>\n",
" <td>-0.126753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <td>-0.338481</td>\n",
" <td>1.000000</td>\n",
" <td>-0.131900</td>\n",
" <td>-0.369226</td>\n",
" <td>0.083081</td>\n",
" <td>0.018443</td>\n",
" <td>-0.549500</td>\n",
" <td>0.307324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sex</th>\n",
" <td>0.543351</td>\n",
" <td>-0.131900</td>\n",
" <td>1.000000</td>\n",
" <td>-0.093254</td>\n",
" <td>0.114631</td>\n",
" <td>0.245489</td>\n",
" <td>0.182333</td>\n",
" <td>-0.023175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>-0.077221</td>\n",
" <td>-0.369226</td>\n",
" <td>-0.093254</td>\n",
" <td>1.000000</td>\n",
" <td>-0.308247</td>\n",
" <td>-0.189119</td>\n",
" <td>0.096067</td>\n",
" <td>-0.042340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SibSp</th>\n",
" <td>-0.035322</td>\n",
" <td>0.083081</td>\n",
" <td>0.114631</td>\n",
" <td>-0.308247</td>\n",
" <td>1.000000</td>\n",
" <td>0.414838</td>\n",
" <td>0.159651</td>\n",
" <td>0.031095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parch</th>\n",
" <td>0.081629</td>\n",
" <td>0.018443</td>\n",
" <td>0.245489</td>\n",
" <td>-0.189119</td>\n",
" <td>0.414838</td>\n",
" <td>1.000000</td>\n",
" <td>0.216225</td>\n",
" <td>-0.035756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fare</th>\n",
" <td>0.257307</td>\n",
" <td>-0.549500</td>\n",
" <td>0.182333</td>\n",
" <td>0.096067</td>\n",
" <td>0.159651</td>\n",
" <td>0.216225</td>\n",
" <td>1.000000</td>\n",
" <td>-0.269588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Embarked</th>\n",
" <td>-0.126753</td>\n",
" <td>0.307324</td>\n",
" <td>-0.023175</td>\n",
" <td>-0.042340</td>\n",
" <td>0.031095</td>\n",
" <td>-0.035756</td>\n",
" <td>-0.269588</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex ... Parch Fare Embarked\n",
"Survived 1.000000 -0.338481 0.543351 ... 0.081629 0.257307 -0.126753\n",
"Pclass -0.338481 1.000000 -0.131900 ... 0.018443 -0.549500 0.307324\n",
"Sex 0.543351 -0.131900 1.000000 ... 0.245489 0.182333 -0.023175\n",
"Age -0.077221 -0.369226 -0.093254 ... -0.189119 0.096067 -0.042340\n",
"SibSp -0.035322 0.083081 0.114631 ... 0.414838 0.159651 0.031095\n",
"Parch 0.081629 0.018443 0.245489 ... 1.000000 0.216225 -0.035756\n",
"Fare 0.257307 -0.549500 0.182333 ... 0.216225 1.000000 -0.269588\n",
"Embarked -0.126753 0.307324 -0.023175 ... -0.035756 -0.269588 1.000000\n",
"\n",
"[8 rows x 8 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrmat = df.corr()\n",
"corrmat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 相関関係のヒートマップを確認する"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4a06b612b0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x648 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(12,9))\n",
"sns.heatmap(corrmat, vmax=.8, square=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 42 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set()\n",
"cols = ['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Fare']\n",
"sns.pairplot(df[cols], size = 2.5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 学習"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"train_labels = df['Survived'].values\n",
"train_features = df\n",
"train_features.drop('Survived', axis=1, inplace=True)\n",
"train_features = train_features.values.astype(np.int64)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" \"the number of iterations.\", ConvergenceWarning)\n"
]
},
{
"data": {
"text/plain": [
"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
" multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
" verbose=0)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import svm\n",
"\n",
"#Standard = svm.LinearSVC(C=1.0, intercept_scaling=1, multi_class=False , loss=\"l1\", penalty=\"l2\", dual=True)\n",
"svm = svm.LinearSVC()\n",
"svm.fit(train_features, train_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## テスト"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"df_test = pd.read_csv(\"./input/test.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 事前準備"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df_test.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"df_test.Embarked = df_test.Embarked.replace(['C', 'S', 'Q'], [0, 1, 2])\n",
"#df.Cabin = df.Cabin.replace('NaN', 0)\n",
"df_test.Sex = df_test.Sex.replace(['male', 'female'], [0, 1])\n",
"df_test.Age = df_test.Age.replace('NaN', 0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"test_features = df_test.values.astype(np.int64)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
" # 各点を分類する\n",
"y_test_pred = svm.predict(test_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 出力できる形式に変更"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df_out = pd.read_csv(\"./input/test.csv\")\n",
"df_out[\"Survived\"] = y_test_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"submission = df_out[[\"PassengerId\",\"Survived\"]]\n",
"submission.to_csv(\"./working/submission.csv\",index=False)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"input lib src working\r\n",
"__notebook__.ipynb __output__.json submission.csv\r\n"
]
}
],
"source": [
"!ls\n",
"!ls working"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
@ryogrid
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ryogrid commented Apr 7, 2019

使わせていただいたコードは以下の記事の冒頭でリンクされているnotebookより。
https://qiita.com/teru855/items/02bd885179bd8e39ba43

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