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yehjames/[資料分析&機器學習] 第4.1講 鐵達尼號災難生存預測.ipynb

Created November 6, 2017 17:10
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[資料分析&機器學習] 第4.1講 鐵達尼號災難生存預測.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3a0cf604-d311-4bf7-bdb1-3e19de0cf1ff",
"_execution_state": "busy",
"_uuid": "8b8cda16cd629eae909fd1108ef12a4b55b416a3"
},
"outputs": [],
"source": [
"from sklearn import preprocessing \n",
"from sklearn.model_selection import GridSearchCV \n",
"from sklearn.ensemble import RandomForestClassifier \n",
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"import warnings\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline\n",
"pd.options.mode.chained_assignment = None\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "e53ae0c8-ac83-4705-b959-c799836153c9",
"_execution_state": "busy",
"_uuid": "541ad1c5e02e65ce56db289646bd5c744d2bfb5e",
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv(\"Titanic/train.csv\")\n",
"test = pd.read_csv(\"Titanic/test.csv\")\n",
"submit = pd.read_csv('Titanic/gender_submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
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" <th>0</th>\n",
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" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</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>38.0</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>26.0</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>35.0</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>35.0</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",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 有空值需要處理"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
]
}
],
"source": [
"train.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 417 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 36.0+ KB\n"
]
}
],
"source": [
"test.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "9e0f1199-22f4-4a36-bf2f-f35846d045c0",
"_execution_state": "busy",
"_uuid": "1187eeac4b61c67e853824f152bbe9864a128fa6"
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe tbody tr th {\n",
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"</style>\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>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>418.000000</td>\n",
" <td>418.000000</td>\n",
" <td>332.000000</td>\n",
" <td>418.000000</td>\n",
" <td>418.000000</td>\n",
" <td>417.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1100.500000</td>\n",
" <td>2.265550</td>\n",
" <td>30.272590</td>\n",
" <td>0.447368</td>\n",
" <td>0.392344</td>\n",
" <td>35.627188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>120.810458</td>\n",
" <td>0.841838</td>\n",
" <td>14.181209</td>\n",
" <td>0.896760</td>\n",
" <td>0.981429</td>\n",
" <td>55.907576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>892.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.170000</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>996.250000</td>\n",
" <td>1.000000</td>\n",
" <td>21.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.895800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1100.500000</td>\n",
" <td>3.000000</td>\n",
" <td>27.000000</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>1204.750000</td>\n",
" <td>3.000000</td>\n",
" <td>39.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1309.000000</td>\n",
" <td>3.000000</td>\n",
" <td>76.000000</td>\n",
" <td>8.000000</td>\n",
" <td>9.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Age SibSp Parch Fare\n",
"count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\n",
"mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\n",
"std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\n",
"min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n",
"25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n",
"50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n",
"75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\n",
"max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d74e934a-ba79-47ef-82e0-a984afbaf093",
"_uuid": "a52a172af0c0f4a6a8675a25162ca4bdb8a67df8"
},
"source": [
"# Combine Train and Test Data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38.0</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>71.2833</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>PC 17599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>26.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.9250</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>0</td>\n",
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" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>35.0</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>53.1000</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>113803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>373450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>8.4583</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>330877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>54.0</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>51.8625</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>17463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>349909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>27.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>11.1333</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>347742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>30.0708</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>237736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>4.0</td>\n",
" <td>G6</td>\n",
" <td>S</td>\n",
" <td>16.7000</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>PP 9549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>58.0</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" <td>26.5500</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>113783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Saundercock, Mr. William Henry</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>A/5. 2151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>39.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>31.2750</td>\n",
" <td>Andersson, Mr. Anders Johan</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>347082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.8542</td>\n",
" <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>350406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>55.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>16.0000</td>\n",
" <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>248706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>29.1250</td>\n",
" <td>Rice, Master. Eugene</td>\n",
" <td>1</td>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>382652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>13.0000</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>244373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>18.0000</td>\n",
" <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>345763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>7.2250</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>26.0000</td>\n",
" <td>Fynney, Mr. Joseph J</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>239865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>34.0</td>\n",
" <td>D56</td>\n",
" <td>S</td>\n",
" <td>13.0000</td>\n",
" <td>Beesley, Mr. Lawrence</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>248698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>8.0292</td>\n",
" <td>McGowan, Miss. Anna \"Annie\"</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>330923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>28.0</td>\n",
" <td>A6</td>\n",
" <td>S</td>\n",
" <td>35.5000</td>\n",
" <td>Sloper, Mr. William Thompson</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>113788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>349909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>38.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>31.3875</td>\n",
" <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
" <td>5</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>347077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>7.2250</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>19.0</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" <td>263.0000</td>\n",
" <td>Fortune, Mr. Charles Alexander</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>19950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.8792</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>330959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.8958</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>349216</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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td>21.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7500</td>\n",
" <td>Canavan, Mr. Patrick</td>\n",
" <td>0</td>\n",
" <td>1280</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>364858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Master. Paul Folke</td>\n",
" <td>1</td>\n",
" <td>1281</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>349909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>23.0</td>\n",
" <td>B24</td>\n",
" <td>S</td>\n",
" <td>93.5000</td>\n",
" <td>Payne, Mr. Vivian Ponsonby</td>\n",
" <td>0</td>\n",
" <td>1282</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>12749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>51.0</td>\n",
" <td>D28</td>\n",
" <td>S</td>\n",
" <td>39.4000</td>\n",
" <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
" <td>1</td>\n",
" <td>1283</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>PC 17592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>20.2500</td>\n",
" <td>Abbott, Master. Eugene Joseph</td>\n",
" <td>2</td>\n",
" <td>1284</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>C.A. 2673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>47.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>10.5000</td>\n",
" <td>Gilbert, Mr. William</td>\n",
" <td>0</td>\n",
" <td>1285</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>C.A. 30769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>22.0250</td>\n",
" <td>Kink-Heilmann, Mr. Anton</td>\n",
" <td>1</td>\n",
" <td>1286</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>315153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>18.0</td>\n",
" <td>C31</td>\n",
" <td>S</td>\n",
" <td>60.0000</td>\n",
" <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
" <td>0</td>\n",
" <td>1287</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>13695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>24.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.2500</td>\n",
" <td>Colbert, Mr. Patrick</td>\n",
" <td>0</td>\n",
" <td>1288</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>371109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>48.0</td>\n",
" <td>B41</td>\n",
" <td>C</td>\n",
" <td>79.2000</td>\n",
" <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
" <td>1</td>\n",
" <td>1289</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>13567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.7750</td>\n",
" <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
" <td>0</td>\n",
" <td>1290</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>347065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7333</td>\n",
" <td>Conlon, Mr. Thomas Henry</td>\n",
" <td>0</td>\n",
" <td>1291</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>21332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>30.0</td>\n",
" <td>C7</td>\n",
" <td>S</td>\n",
" <td>164.8667</td>\n",
" <td>Bonnell, Miss. Caroline</td>\n",
" <td>0</td>\n",
" <td>1292</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>36928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401</th>\n",
" <td>38.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0000</td>\n",
" <td>Gale, Mr. Harry</td>\n",
" <td>0</td>\n",
" <td>1293</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>28664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402</th>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>59.4000</td>\n",
" <td>Gibson, Miss. Dorothy Winifred</td>\n",
" <td>1</td>\n",
" <td>1294</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>112378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>47.1000</td>\n",
" <td>Carrau, Mr. Jose Pedro</td>\n",
" <td>0</td>\n",
" <td>1295</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>113059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>43.0</td>\n",
" <td>D40</td>\n",
" <td>C</td>\n",
" <td>27.7208</td>\n",
" <td>Frauenthal, Mr. Isaac Gerald</td>\n",
" <td>0</td>\n",
" <td>1296</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>17765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>20.0</td>\n",
" <td>D38</td>\n",
" <td>C</td>\n",
" <td>13.8625</td>\n",
" <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
" <td>0</td>\n",
" <td>1297</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>SC/PARIS 2166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>10.5000</td>\n",
" <td>Ware, Mr. William Jeffery</td>\n",
" <td>0</td>\n",
" <td>1298</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>28666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>50.0</td>\n",
" <td>C80</td>\n",
" <td>C</td>\n",
" <td>211.5000</td>\n",
" <td>Widener, Mr. George Dunton</td>\n",
" <td>1</td>\n",
" <td>1299</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>113503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>408</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7208</td>\n",
" <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
" <td>0</td>\n",
" <td>1300</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>334915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>13.7750</td>\n",
" <td>Peacock, Miss. Treasteall</td>\n",
" <td>1</td>\n",
" <td>1301</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>SOTON/O.Q. 3101315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>410</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7500</td>\n",
" <td>Naughton, Miss. Hannah</td>\n",
" <td>0</td>\n",
" <td>1302</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>365237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>411</th>\n",
" <td>37.0</td>\n",
" <td>C78</td>\n",
" <td>Q</td>\n",
" <td>90.0000</td>\n",
" <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
" <td>0</td>\n",
" <td>1303</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>19928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.7750</td>\n",
" <td>Henriksson, Miss. Jenny Lovisa</td>\n",
" <td>0</td>\n",
" <td>1304</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>347086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Spector, Mr. Woolf</td>\n",
" <td>0</td>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>A.5. 3236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>39.0</td>\n",
" <td>C105</td>\n",
" <td>C</td>\n",
" <td>108.9000</td>\n",
" <td>Oliva y Ocana, Dona. Fermina</td>\n",
" <td>0</td>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>PC 17758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>38.5</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.2500</td>\n",
" <td>Saether, Mr. Simon Sivertsen</td>\n",
" <td>0</td>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>SOTON/O.Q. 3101262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>0</td>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>359309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>22.3583</td>\n",
" <td>Peter, Master. Michael J</td>\n",
" <td>1</td>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>2668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Age Cabin Embarked Fare \\\n",
"0 22.0 NaN S 7.2500 \n",
"1 38.0 C85 C 71.2833 \n",
"2 26.0 NaN S 7.9250 \n",
"3 35.0 C123 S 53.1000 \n",
"4 35.0 NaN S 8.0500 \n",
"5 NaN NaN Q 8.4583 \n",
"6 54.0 E46 S 51.8625 \n",
"7 2.0 NaN S 21.0750 \n",
"8 27.0 NaN S 11.1333 \n",
"9 14.0 NaN C 30.0708 \n",
"10 4.0 G6 S 16.7000 \n",
"11 58.0 C103 S 26.5500 \n",
"12 20.0 NaN S 8.0500 \n",
"13 39.0 NaN S 31.2750 \n",
"14 14.0 NaN S 7.8542 \n",
"15 55.0 NaN S 16.0000 \n",
"16 2.0 NaN Q 29.1250 \n",
"17 NaN NaN S 13.0000 \n",
"18 31.0 NaN S 18.0000 \n",
"19 NaN NaN C 7.2250 \n",
"20 35.0 NaN S 26.0000 \n",
"21 34.0 D56 S 13.0000 \n",
"22 15.0 NaN Q 8.0292 \n",
"23 28.0 A6 S 35.5000 \n",
"24 8.0 NaN S 21.0750 \n",
"25 38.0 NaN S 31.3875 \n",
"26 NaN NaN C 7.2250 \n",
"27 19.0 C23 C25 C27 S 263.0000 \n",
"28 NaN NaN Q 7.8792 \n",
"29 NaN NaN S 7.8958 \n",
".. ... ... ... ... \n",
"388 21.0 NaN Q 7.7500 \n",
"389 6.0 NaN S 21.0750 \n",
"390 23.0 B24 S 93.5000 \n",
"391 51.0 D28 S 39.4000 \n",
"392 13.0 NaN S 20.2500 \n",
"393 47.0 NaN S 10.5000 \n",
"394 29.0 NaN S 22.0250 \n",
"395 18.0 C31 S 60.0000 \n",
"396 24.0 NaN Q 7.2500 \n",
"397 48.0 B41 C 79.2000 \n",
"398 22.0 NaN S 7.7750 \n",
"399 31.0 NaN Q 7.7333 \n",
"400 30.0 C7 S 164.8667 \n",
"401 38.0 NaN S 21.0000 \n",
"402 22.0 NaN C 59.4000 \n",
"403 17.0 NaN S 47.1000 \n",
"404 43.0 D40 C 27.7208 \n",
"405 20.0 D38 C 13.8625 \n",
"406 23.0 NaN S 10.5000 \n",
"407 50.0 C80 C 211.5000 \n",
"408 NaN NaN Q 7.7208 \n",
"409 3.0 NaN S 13.7750 \n",
"410 NaN NaN Q 7.7500 \n",
"411 37.0 C78 Q 90.0000 \n",
"412 28.0 NaN S 7.7750 \n",
"413 NaN NaN S 8.0500 \n",
"414 39.0 C105 C 108.9000 \n",
"415 38.5 NaN S 7.2500 \n",
"416 NaN NaN S 8.0500 \n",
"417 NaN NaN C 22.3583 \n",
"\n",
" Name Parch PassengerId \\\n",
"0 Braund, Mr. Owen Harris 0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 2 \n",
"2 Heikkinen, Miss. Laina 0 3 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 4 \n",
"4 Allen, Mr. William Henry 0 5 \n",
"5 Moran, Mr. James 0 6 \n",
"6 McCarthy, Mr. Timothy J 0 7 \n",
"7 Palsson, Master. Gosta Leonard 1 8 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 2 9 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) 0 10 \n",
"10 Sandstrom, Miss. Marguerite Rut 1 11 \n",
"11 Bonnell, Miss. Elizabeth 0 12 \n",
"12 Saundercock, Mr. William Henry 0 13 \n",
"13 Andersson, Mr. Anders Johan 5 14 \n",
"14 Vestrom, Miss. Hulda Amanda Adolfina 0 15 \n",
"15 Hewlett, Mrs. (Mary D Kingcome) 0 16 \n",
"16 Rice, Master. Eugene 1 17 \n",
"17 Williams, Mr. Charles Eugene 0 18 \n",
"18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 0 19 \n",
"19 Masselmani, Mrs. Fatima 0 20 \n",
"20 Fynney, Mr. Joseph J 0 21 \n",
"21 Beesley, Mr. Lawrence 0 22 \n",
"22 McGowan, Miss. Anna \"Annie\" 0 23 \n",
"23 Sloper, Mr. William Thompson 0 24 \n",
"24 Palsson, Miss. Torborg Danira 1 25 \n",
"25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 5 26 \n",
"26 Emir, Mr. Farred Chehab 0 27 \n",
"27 Fortune, Mr. Charles Alexander 2 28 \n",
"28 O'Dwyer, Miss. Ellen \"Nellie\" 0 29 \n",
"29 Todoroff, Mr. Lalio 0 30 \n",
".. ... ... ... \n",
"388 Canavan, Mr. Patrick 0 1280 \n",
"389 Palsson, Master. Paul Folke 1 1281 \n",
"390 Payne, Mr. Vivian Ponsonby 0 1282 \n",
"391 Lines, Mrs. Ernest H (Elizabeth Lindsey James) 1 1283 \n",
"392 Abbott, Master. Eugene Joseph 2 1284 \n",
"393 Gilbert, Mr. William 0 1285 \n",
"394 Kink-Heilmann, Mr. Anton 1 1286 \n",
"395 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) 0 1287 \n",
"396 Colbert, Mr. Patrick 0 1288 \n",
"397 Frolicher-Stehli, Mrs. Maxmillian (Margaretha ... 1 1289 \n",
"398 Larsson-Rondberg, Mr. Edvard A 0 1290 \n",
"399 Conlon, Mr. Thomas Henry 0 1291 \n",
"400 Bonnell, Miss. Caroline 0 1292 \n",
"401 Gale, Mr. Harry 0 1293 \n",
"402 Gibson, Miss. Dorothy Winifred 1 1294 \n",
"403 Carrau, Mr. Jose Pedro 0 1295 \n",
"404 Frauenthal, Mr. Isaac Gerald 0 1296 \n",
"405 Nourney, Mr. Alfred (Baron von Drachstedt\")\" 0 1297 \n",
"406 Ware, Mr. William Jeffery 0 1298 \n",
"407 Widener, Mr. George Dunton 1 1299 \n",
"408 Riordan, Miss. Johanna Hannah\"\" 0 1300 \n",
"409 Peacock, Miss. Treasteall 1 1301 \n",
"410 Naughton, Miss. Hannah 0 1302 \n",
"411 Minahan, Mrs. William Edward (Lillian E Thorpe) 0 1303 \n",
"412 Henriksson, Miss. Jenny Lovisa 0 1304 \n",
"413 Spector, Mr. Woolf 0 1305 \n",
"414 Oliva y Ocana, Dona. Fermina 0 1306 \n",
"415 Saether, Mr. Simon Sivertsen 0 1307 \n",
"416 Ware, Mr. Frederick 0 1308 \n",
"417 Peter, Master. Michael J 1 1309 \n",
"\n",
" Pclass Sex SibSp Survived Ticket \n",
"0 3 male 1 0.0 A/5 21171 \n",
"1 1 female 1 1.0 PC 17599 \n",
"2 3 female 0 1.0 STON/O2. 3101282 \n",
"3 1 female 1 1.0 113803 \n",
"4 3 male 0 0.0 373450 \n",
"5 3 male 0 0.0 330877 \n",
"6 1 male 0 0.0 17463 \n",
"7 3 male 3 0.0 349909 \n",
"8 3 female 0 1.0 347742 \n",
"9 2 female 1 1.0 237736 \n",
"10 3 female 1 1.0 PP 9549 \n",
"11 1 female 0 1.0 113783 \n",
"12 3 male 0 0.0 A/5. 2151 \n",
"13 3 male 1 0.0 347082 \n",
"14 3 female 0 0.0 350406 \n",
"15 2 female 0 1.0 248706 \n",
"16 3 male 4 0.0 382652 \n",
"17 2 male 0 1.0 244373 \n",
"18 3 female 1 0.0 345763 \n",
"19 3 female 0 1.0 2649 \n",
"20 2 male 0 0.0 239865 \n",
"21 2 male 0 1.0 248698 \n",
"22 3 female 0 1.0 330923 \n",
"23 1 male 0 1.0 113788 \n",
"24 3 female 3 0.0 349909 \n",
"25 3 female 1 1.0 347077 \n",
"26 3 male 0 0.0 2631 \n",
"27 1 male 3 0.0 19950 \n",
"28 3 female 0 1.0 330959 \n",
"29 3 male 0 0.0 349216 \n",
".. ... ... ... ... ... \n",
"388 3 male 0 NaN 364858 \n",
"389 3 male 3 NaN 349909 \n",
"390 1 male 0 NaN 12749 \n",
"391 1 female 0 NaN PC 17592 \n",
"392 3 male 0 NaN C.A. 2673 \n",
"393 2 male 0 NaN C.A. 30769 \n",
"394 3 male 3 NaN 315153 \n",
"395 1 female 1 NaN 13695 \n",
"396 3 male 0 NaN 371109 \n",
"397 1 female 1 NaN 13567 \n",
"398 3 male 0 NaN 347065 \n",
"399 3 male 0 NaN 21332 \n",
"400 1 female 0 NaN 36928 \n",
"401 2 male 1 NaN 28664 \n",
"402 1 female 0 NaN 112378 \n",
"403 1 male 0 NaN 113059 \n",
"404 1 male 1 NaN 17765 \n",
"405 2 male 0 NaN SC/PARIS 2166 \n",
"406 2 male 1 NaN 28666 \n",
"407 1 male 1 NaN 113503 \n",
"408 3 female 0 NaN 334915 \n",
"409 3 female 1 NaN SOTON/O.Q. 3101315 \n",
"410 3 female 0 NaN 365237 \n",
"411 1 female 1 NaN 19928 \n",
"412 3 female 0 NaN 347086 \n",
"413 3 male 0 NaN A.5. 3236 \n",
"414 1 female 0 NaN PC 17758 \n",
"415 3 male 0 NaN SOTON/O.Q. 3101262 \n",
"416 3 male 0 NaN 359309 \n",
"417 3 male 1 NaN 2668 \n",
"\n",
"[1309 rows x 12 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = train.append(test)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data.reset_index(inplace=True, drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" <th>Fare</th>\n",
" <th>Name</th>\n",
" <th>Parch</th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>SibSp</th>\n",
" <th>Survived</th>\n",
" <th>Ticket</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.2500</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>A/5 21171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>38.0</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" <td>71.2833</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>PC 17599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>26.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.9250</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>35.0</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" <td>53.1000</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>113803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>8.4583</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
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" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>330877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>54.0</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>51.8625</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
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" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
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" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>349909</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>27.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>11.1333</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>347742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>30.0708</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>237736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>4.0</td>\n",
" <td>G6</td>\n",
" <td>S</td>\n",
" <td>16.7000</td>\n",
" <td>Sandstrom, Miss. Marguerite Rut</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>PP 9549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>58.0</td>\n",
" <td>C103</td>\n",
" <td>S</td>\n",
" <td>26.5500</td>\n",
" <td>Bonnell, Miss. Elizabeth</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>113783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Saundercock, Mr. William Henry</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>A/5. 2151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>39.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>31.2750</td>\n",
" <td>Andersson, Mr. Anders Johan</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>347082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.8542</td>\n",
" <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>350406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>55.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>16.0000</td>\n",
" <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>248706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>29.1250</td>\n",
" <td>Rice, Master. Eugene</td>\n",
" <td>1</td>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>382652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>13.0000</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>244373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>18.0000</td>\n",
" <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>345763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>7.2250</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>26.0000</td>\n",
" <td>Fynney, Mr. Joseph J</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>239865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>34.0</td>\n",
" <td>D56</td>\n",
" <td>S</td>\n",
" <td>13.0000</td>\n",
" <td>Beesley, Mr. Lawrence</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>248698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>8.0292</td>\n",
" <td>McGowan, Miss. Anna \"Annie\"</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>330923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>28.0</td>\n",
" <td>A6</td>\n",
" <td>S</td>\n",
" <td>35.5000</td>\n",
" <td>Sloper, Mr. William Thompson</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>113788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Miss. Torborg Danira</td>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>349909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>38.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>31.3875</td>\n",
" <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
" <td>5</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>347077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>7.2250</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>19.0</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" <td>263.0000</td>\n",
" <td>Fortune, Mr. Charles Alexander</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>19950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.8792</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>330959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.8958</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>349216</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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1279</th>\n",
" <td>21.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7500</td>\n",
" <td>Canavan, Mr. Patrick</td>\n",
" <td>0</td>\n",
" <td>1280</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>364858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1280</th>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0750</td>\n",
" <td>Palsson, Master. Paul Folke</td>\n",
" <td>1</td>\n",
" <td>1281</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>349909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1281</th>\n",
" <td>23.0</td>\n",
" <td>B24</td>\n",
" <td>S</td>\n",
" <td>93.5000</td>\n",
" <td>Payne, Mr. Vivian Ponsonby</td>\n",
" <td>0</td>\n",
" <td>1282</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>12749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1282</th>\n",
" <td>51.0</td>\n",
" <td>D28</td>\n",
" <td>S</td>\n",
" <td>39.4000</td>\n",
" <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
" <td>1</td>\n",
" <td>1283</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>PC 17592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1283</th>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>20.2500</td>\n",
" <td>Abbott, Master. Eugene Joseph</td>\n",
" <td>2</td>\n",
" <td>1284</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>C.A. 2673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1284</th>\n",
" <td>47.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>10.5000</td>\n",
" <td>Gilbert, Mr. William</td>\n",
" <td>0</td>\n",
" <td>1285</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>C.A. 30769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1285</th>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>22.0250</td>\n",
" <td>Kink-Heilmann, Mr. Anton</td>\n",
" <td>1</td>\n",
" <td>1286</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>315153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>18.0</td>\n",
" <td>C31</td>\n",
" <td>S</td>\n",
" <td>60.0000</td>\n",
" <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
" <td>0</td>\n",
" <td>1287</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>13695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1287</th>\n",
" <td>24.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.2500</td>\n",
" <td>Colbert, Mr. Patrick</td>\n",
" <td>0</td>\n",
" <td>1288</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>371109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1288</th>\n",
" <td>48.0</td>\n",
" <td>B41</td>\n",
" <td>C</td>\n",
" <td>79.2000</td>\n",
" <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
" <td>1</td>\n",
" <td>1289</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>13567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1289</th>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.7750</td>\n",
" <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
" <td>0</td>\n",
" <td>1290</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>347065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1290</th>\n",
" <td>31.0</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7333</td>\n",
" <td>Conlon, Mr. Thomas Henry</td>\n",
" <td>0</td>\n",
" <td>1291</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>21332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1291</th>\n",
" <td>30.0</td>\n",
" <td>C7</td>\n",
" <td>S</td>\n",
" <td>164.8667</td>\n",
" <td>Bonnell, Miss. Caroline</td>\n",
" <td>0</td>\n",
" <td>1292</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>36928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1292</th>\n",
" <td>38.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>21.0000</td>\n",
" <td>Gale, Mr. Harry</td>\n",
" <td>0</td>\n",
" <td>1293</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>28664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1293</th>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>59.4000</td>\n",
" <td>Gibson, Miss. Dorothy Winifred</td>\n",
" <td>1</td>\n",
" <td>1294</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>112378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>47.1000</td>\n",
" <td>Carrau, Mr. Jose Pedro</td>\n",
" <td>0</td>\n",
" <td>1295</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>113059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1295</th>\n",
" <td>43.0</td>\n",
" <td>D40</td>\n",
" <td>C</td>\n",
" <td>27.7208</td>\n",
" <td>Frauenthal, Mr. Isaac Gerald</td>\n",
" <td>0</td>\n",
" <td>1296</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>17765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1296</th>\n",
" <td>20.0</td>\n",
" <td>D38</td>\n",
" <td>C</td>\n",
" <td>13.8625</td>\n",
" <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
" <td>0</td>\n",
" <td>1297</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>SC/PARIS 2166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1297</th>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>10.5000</td>\n",
" <td>Ware, Mr. William Jeffery</td>\n",
" <td>0</td>\n",
" <td>1298</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>28666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1298</th>\n",
" <td>50.0</td>\n",
" <td>C80</td>\n",
" <td>C</td>\n",
" <td>211.5000</td>\n",
" <td>Widener, Mr. George Dunton</td>\n",
" <td>1</td>\n",
" <td>1299</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>113503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7208</td>\n",
" <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
" <td>0</td>\n",
" <td>1300</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>334915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1300</th>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>13.7750</td>\n",
" <td>Peacock, Miss. Treasteall</td>\n",
" <td>1</td>\n",
" <td>1301</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>SOTON/O.Q. 3101315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>7.7500</td>\n",
" <td>Naughton, Miss. Hannah</td>\n",
" <td>0</td>\n",
" <td>1302</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>365237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>37.0</td>\n",
" <td>C78</td>\n",
" <td>Q</td>\n",
" <td>90.0000</td>\n",
" <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
" <td>0</td>\n",
" <td>1303</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>19928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1303</th>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.7750</td>\n",
" <td>Henriksson, Miss. Jenny Lovisa</td>\n",
" <td>0</td>\n",
" <td>1304</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>347086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1304</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Spector, Mr. Woolf</td>\n",
" <td>0</td>\n",
" <td>1305</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>A.5. 3236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1305</th>\n",
" <td>39.0</td>\n",
" <td>C105</td>\n",
" <td>C</td>\n",
" <td>108.9000</td>\n",
" <td>Oliva y Ocana, Dona. Fermina</td>\n",
" <td>0</td>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>PC 17758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1306</th>\n",
" <td>38.5</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>7.2500</td>\n",
" <td>Saether, Mr. Simon Sivertsen</td>\n",
" <td>0</td>\n",
" <td>1307</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>SOTON/O.Q. 3101262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>8.0500</td>\n",
" <td>Ware, Mr. Frederick</td>\n",
" <td>0</td>\n",
" <td>1308</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>359309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1308</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>22.3583</td>\n",
" <td>Peter, Master. Michael J</td>\n",
" <td>1</td>\n",
" <td>1309</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>2668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1309 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Age Cabin Embarked Fare \\\n",
"0 22.0 NaN S 7.2500 \n",
"1 38.0 C85 C 71.2833 \n",
"2 26.0 NaN S 7.9250 \n",
"3 35.0 C123 S 53.1000 \n",
"4 35.0 NaN S 8.0500 \n",
"5 NaN NaN Q 8.4583 \n",
"6 54.0 E46 S 51.8625 \n",
"7 2.0 NaN S 21.0750 \n",
"8 27.0 NaN S 11.1333 \n",
"9 14.0 NaN C 30.0708 \n",
"10 4.0 G6 S 16.7000 \n",
"11 58.0 C103 S 26.5500 \n",
"12 20.0 NaN S 8.0500 \n",
"13 39.0 NaN S 31.2750 \n",
"14 14.0 NaN S 7.8542 \n",
"15 55.0 NaN S 16.0000 \n",
"16 2.0 NaN Q 29.1250 \n",
"17 NaN NaN S 13.0000 \n",
"18 31.0 NaN S 18.0000 \n",
"19 NaN NaN C 7.2250 \n",
"20 35.0 NaN S 26.0000 \n",
"21 34.0 D56 S 13.0000 \n",
"22 15.0 NaN Q 8.0292 \n",
"23 28.0 A6 S 35.5000 \n",
"24 8.0 NaN S 21.0750 \n",
"25 38.0 NaN S 31.3875 \n",
"26 NaN NaN C 7.2250 \n",
"27 19.0 C23 C25 C27 S 263.0000 \n",
"28 NaN NaN Q 7.8792 \n",
"29 NaN NaN S 7.8958 \n",
"... ... ... ... ... \n",
"1279 21.0 NaN Q 7.7500 \n",
"1280 6.0 NaN S 21.0750 \n",
"1281 23.0 B24 S 93.5000 \n",
"1282 51.0 D28 S 39.4000 \n",
"1283 13.0 NaN S 20.2500 \n",
"1284 47.0 NaN S 10.5000 \n",
"1285 29.0 NaN S 22.0250 \n",
"1286 18.0 C31 S 60.0000 \n",
"1287 24.0 NaN Q 7.2500 \n",
"1288 48.0 B41 C 79.2000 \n",
"1289 22.0 NaN S 7.7750 \n",
"1290 31.0 NaN Q 7.7333 \n",
"1291 30.0 C7 S 164.8667 \n",
"1292 38.0 NaN S 21.0000 \n",
"1293 22.0 NaN C 59.4000 \n",
"1294 17.0 NaN S 47.1000 \n",
"1295 43.0 D40 C 27.7208 \n",
"1296 20.0 D38 C 13.8625 \n",
"1297 23.0 NaN S 10.5000 \n",
"1298 50.0 C80 C 211.5000 \n",
"1299 NaN NaN Q 7.7208 \n",
"1300 3.0 NaN S 13.7750 \n",
"1301 NaN NaN Q 7.7500 \n",
"1302 37.0 C78 Q 90.0000 \n",
"1303 28.0 NaN S 7.7750 \n",
"1304 NaN NaN S 8.0500 \n",
"1305 39.0 C105 C 108.9000 \n",
"1306 38.5 NaN S 7.2500 \n",
"1307 NaN NaN S 8.0500 \n",
"1308 NaN NaN C 22.3583 \n",
"\n",
" Name Parch PassengerId \\\n",
"0 Braund, Mr. Owen Harris 0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 2 \n",
"2 Heikkinen, Miss. Laina 0 3 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 4 \n",
"4 Allen, Mr. William Henry 0 5 \n",
"5 Moran, Mr. James 0 6 \n",
"6 McCarthy, Mr. Timothy J 0 7 \n",
"7 Palsson, Master. Gosta Leonard 1 8 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 2 9 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) 0 10 \n",
"10 Sandstrom, Miss. Marguerite Rut 1 11 \n",
"11 Bonnell, Miss. Elizabeth 0 12 \n",
"12 Saundercock, Mr. William Henry 0 13 \n",
"13 Andersson, Mr. Anders Johan 5 14 \n",
"14 Vestrom, Miss. Hulda Amanda Adolfina 0 15 \n",
"15 Hewlett, Mrs. (Mary D Kingcome) 0 16 \n",
"16 Rice, Master. Eugene 1 17 \n",
"17 Williams, Mr. Charles Eugene 0 18 \n",
"18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 0 19 \n",
"19 Masselmani, Mrs. Fatima 0 20 \n",
"20 Fynney, Mr. Joseph J 0 21 \n",
"21 Beesley, Mr. Lawrence 0 22 \n",
"22 McGowan, Miss. Anna \"Annie\" 0 23 \n",
"23 Sloper, Mr. William Thompson 0 24 \n",
"24 Palsson, Miss. Torborg Danira 1 25 \n",
"25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 5 26 \n",
"26 Emir, Mr. Farred Chehab 0 27 \n",
"27 Fortune, Mr. Charles Alexander 2 28 \n",
"28 O'Dwyer, Miss. Ellen \"Nellie\" 0 29 \n",
"29 Todoroff, Mr. Lalio 0 30 \n",
"... ... ... ... \n",
"1279 Canavan, Mr. Patrick 0 1280 \n",
"1280 Palsson, Master. Paul Folke 1 1281 \n",
"1281 Payne, Mr. Vivian Ponsonby 0 1282 \n",
"1282 Lines, Mrs. Ernest H (Elizabeth Lindsey James) 1 1283 \n",
"1283 Abbott, Master. Eugene Joseph 2 1284 \n",
"1284 Gilbert, Mr. William 0 1285 \n",
"1285 Kink-Heilmann, Mr. Anton 1 1286 \n",
"1286 Smith, Mrs. Lucien Philip (Mary Eloise Hughes) 0 1287 \n",
"1287 Colbert, Mr. Patrick 0 1288 \n",
"1288 Frolicher-Stehli, Mrs. Maxmillian (Margaretha ... 1 1289 \n",
"1289 Larsson-Rondberg, Mr. Edvard A 0 1290 \n",
"1290 Conlon, Mr. Thomas Henry 0 1291 \n",
"1291 Bonnell, Miss. Caroline 0 1292 \n",
"1292 Gale, Mr. Harry 0 1293 \n",
"1293 Gibson, Miss. Dorothy Winifred 1 1294 \n",
"1294 Carrau, Mr. Jose Pedro 0 1295 \n",
"1295 Frauenthal, Mr. Isaac Gerald 0 1296 \n",
"1296 Nourney, Mr. Alfred (Baron von Drachstedt\")\" 0 1297 \n",
"1297 Ware, Mr. William Jeffery 0 1298 \n",
"1298 Widener, Mr. George Dunton 1 1299 \n",
"1299 Riordan, Miss. Johanna Hannah\"\" 0 1300 \n",
"1300 Peacock, Miss. Treasteall 1 1301 \n",
"1301 Naughton, Miss. Hannah 0 1302 \n",
"1302 Minahan, Mrs. William Edward (Lillian E Thorpe) 0 1303 \n",
"1303 Henriksson, Miss. Jenny Lovisa 0 1304 \n",
"1304 Spector, Mr. Woolf 0 1305 \n",
"1305 Oliva y Ocana, Dona. Fermina 0 1306 \n",
"1306 Saether, Mr. Simon Sivertsen 0 1307 \n",
"1307 Ware, Mr. Frederick 0 1308 \n",
"1308 Peter, Master. Michael J 1 1309 \n",
"\n",
" Pclass Sex SibSp Survived Ticket \n",
"0 3 male 1 0.0 A/5 21171 \n",
"1 1 female 1 1.0 PC 17599 \n",
"2 3 female 0 1.0 STON/O2. 3101282 \n",
"3 1 female 1 1.0 113803 \n",
"4 3 male 0 0.0 373450 \n",
"5 3 male 0 0.0 330877 \n",
"6 1 male 0 0.0 17463 \n",
"7 3 male 3 0.0 349909 \n",
"8 3 female 0 1.0 347742 \n",
"9 2 female 1 1.0 237736 \n",
"10 3 female 1 1.0 PP 9549 \n",
"11 1 female 0 1.0 113783 \n",
"12 3 male 0 0.0 A/5. 2151 \n",
"13 3 male 1 0.0 347082 \n",
"14 3 female 0 0.0 350406 \n",
"15 2 female 0 1.0 248706 \n",
"16 3 male 4 0.0 382652 \n",
"17 2 male 0 1.0 244373 \n",
"18 3 female 1 0.0 345763 \n",
"19 3 female 0 1.0 2649 \n",
"20 2 male 0 0.0 239865 \n",
"21 2 male 0 1.0 248698 \n",
"22 3 female 0 1.0 330923 \n",
"23 1 male 0 1.0 113788 \n",
"24 3 female 3 0.0 349909 \n",
"25 3 female 1 1.0 347077 \n",
"26 3 male 0 0.0 2631 \n",
"27 1 male 3 0.0 19950 \n",
"28 3 female 0 1.0 330959 \n",
"29 3 male 0 0.0 349216 \n",
"... ... ... ... ... ... \n",
"1279 3 male 0 NaN 364858 \n",
"1280 3 male 3 NaN 349909 \n",
"1281 1 male 0 NaN 12749 \n",
"1282 1 female 0 NaN PC 17592 \n",
"1283 3 male 0 NaN C.A. 2673 \n",
"1284 2 male 0 NaN C.A. 30769 \n",
"1285 3 male 3 NaN 315153 \n",
"1286 1 female 1 NaN 13695 \n",
"1287 3 male 0 NaN 371109 \n",
"1288 1 female 1 NaN 13567 \n",
"1289 3 male 0 NaN 347065 \n",
"1290 3 male 0 NaN 21332 \n",
"1291 1 female 0 NaN 36928 \n",
"1292 2 male 1 NaN 28664 \n",
"1293 1 female 0 NaN 112378 \n",
"1294 1 male 0 NaN 113059 \n",
"1295 1 male 1 NaN 17765 \n",
"1296 2 male 0 NaN SC/PARIS 2166 \n",
"1297 2 male 1 NaN 28666 \n",
"1298 1 male 1 NaN 113503 \n",
"1299 3 female 0 NaN 334915 \n",
"1300 3 female 1 NaN SOTON/O.Q. 3101315 \n",
"1301 3 female 0 NaN 365237 \n",
"1302 1 female 1 NaN 19928 \n",
"1303 3 female 0 NaN 347086 \n",
"1304 3 male 0 NaN A.5. 3236 \n",
"1305 1 female 0 NaN PC 17758 \n",
"1306 3 male 0 NaN SOTON/O.Q. 3101262 \n",
"1307 3 male 0 NaN 359309 \n",
"1308 3 male 1 NaN 2668 \n",
"\n",
"[1309 rows x 12 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110b07860>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AX5+UX1JqgreJ1LEi4jco1r/5J+AR4JGIWAb8ATBKsYYTFLeSar1es/13wM3A8XJ7rHr9\nU4H3Zea3yjouoriltJwz36CdQGoRRwbqZEeA+yLiUoDyVs4vAM8Br1C8WwdY2uAcj1Pc6rkO+GKT\n/V8FPlT+zJ8Engd+FvhX4JaImBIRlwDvfrO/mDRRhoE6VmZuA+4BtkREAv9F8a7948CfAQ9FxLM0\nWD8/M1+nWB58d2a+1mT/PcDbImIPRTDckZkvUnxH9WGKZZsHgT2T8otKTXDVUkmSIwNJkmEgScIw\nkCRhGEiSMAwkSRgGkiQMA0kS8P8SedqAKTk8BwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d51a588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x113ee1e10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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H7s3MlRExHbgBWAVs6tRmCzDsnfa9cWNzT5YqSQA0NW2pdQmF6i7sqjmA/Fhmrux4DXwc\n2Ax0rq4eeL2KNUmSqG4YPBURJ5ZfnwaspNRbOCUiBkfEMOBYYE0Va5IkUfDVRLu5BLgzIt4C/gBM\nyszNETEHWEopmKZn5rYq1iRJAvq1tx94Dyxratpy4BUt9TFX3rqw1iXsszuuHlfrEgrV2Fjfb2/b\nvOlMkmQYSJKqO2agCl29aEatS9hnt35+Vq1LkPQu2DOQJBkGkiTDQJKEYSBJwjCQJGEYSJIwDCRJ\nGAaSJAwDSRLegSxJO/Xlu//tGUiSDANJkmEgScIwkCRhGEiSMAwkSRR8aWlEnATckpljI+IjwHyg\nHVgDXJaZbRExEZgMtAKzMnNRkTVJkvZUWM8gIq4B7gUGl1fdDszIzFOAfsD4iDgcuAIYA/wFcHNE\nHFRUTZKkrhV5mugV4JxOy6OAZ8uvnwA+B5wIPJeZLZm5CVgHjCywJklSFwo7TZSZj0bEiE6r+mVm\ne/n1FmAYMBTY1KlNx/puNTQMoa5uQE+Vqh7Q2Fhf6xKkPqmn/u9VczqKtk6v64HXgc3l17uv79bG\njc09W5netaamLbUuQeqT9uX/XnfBUc0weDEixmbmM8CZwE+B5cCNETEYOAg4ltLgsgTAlbcurHUJ\n++SOq8fVugRpv1QzDP4OmBcRg4CXgAWZuSMi5gBLKY1fTM/MbVWsSZJEwWGQmeuBk8uvXwY+00Wb\necC8IuuQJHXPm84kSYaBJMmH20g9qi8/HEUHNnsGkiTDQJJkGEiS6ANjBgfaTUsAg46tdQWS+hp7\nBpIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJGowa2lE/BzY\nXF58FbgRmA+0A2uAyzKzrdp1SVJfVtUwiIjBQL/MHNtp3UJgRmY+ExHfAcYDj1WzLknq66rdMzgB\nGBIRi8vHvh4YBTxb3v4EcDqGgSRVVbXDoBm4DbgX+CilL/9+mdle3r4FGPZOO2loGEJd3YDCitS+\na2ysr3UJ2k/+7g5sPfX7q3YYvAysK3/5vxwRGyj1DDrUA6+/0042bmwuqDztr6amLbUuQfvJ392B\nbV9+f90FR7WvJpoAzAaIiCOAocDiiBhb3n4msLTKNUlSn1ftnsF9wPyIWEbp6qEJwB+BeRExCHgJ\nWFDlmiSpz6tqGGTmduC8LjZ9ppp1SJJ25U1nkiTDQJJkGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRh\nIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJQF2tCwCIiP7AXOAE\noAW4ODPX1bYqSeo7ekvP4GxgcGZ+Evg6MLvG9UhSn9JbwuDTwJMAmfk8MLq25UhS39Kvvb291jUQ\nEfcCj2bmE+XlXwNHZ2ZrbSuTpL6ht/QMNgP1nZb7GwSSVD29JQyeA/4SICJOBn5Z23IkqW/pFVcT\nAY8Bfx4R/wb0Ay6qcT2S1Kf0ijEDSVJt9ZbTRJKkGjIMJEmGgSSp9wwgqywiTgJuycyxta5FlYuI\ngcD9wAjgIGBWZi6saVGqWEQMAOYBAbQDUzJzTW2rqi57Br1IRFwD3AsMrnUt2mdfBTZk5inAGcBd\nNa5H++YLAJk5BpgB3FjbcqrPMOhdXgHOqXUR2i+PADPLr/sB3jR5AMnMHwGTyotHAa/XsJya8DRR\nL5KZj0bEiFrXoX2XmW8AREQ9sIDSX5c6gGRma0Q8CHwROLfW9VSbPQOph0TEkcBPgYcz8/u1rkf7\nLjMvAI4B5kXEIbWup5rsGUg9ICI+ACwGpmbmv9a6Hu2biDgf+GBm3gw0A23lnz7DMJB6xvVAAzAz\nIjrGDs7MzDdrWJMq90PggYhYAgwEpvW1353TUUiSHDOQJBkGkiQMA0kShoEkCcNAkoSXlkp7KN8F\n/jLwH5QmLRsE/A64KDN/00X7C4GxmXlh9aqUepZhIHXtd5n5PzoWIuJm4E5KUxVI7zmGgVSZJcC4\niPgcMJvSKdb/B5zXuVFE/C/g74CDyz8XZ+aSiLgKuIDSXa3LM3NyRIwE7qH0/3AbpZ7H2mp9IKkz\nxwykd1B+VsGXgeXA94ALMvN4YDWlL/iOdv2BKcDnM/ME4B+BqyOiDrgOGA2MAtoiYjjwNWB2Zo6m\n1Os4uXqfStqVdyBLu9ltzABKD6tZDvxv4DuZ+T93a38h5TGDiBhKaW78AMYCOzLzsxHxOKWpkR8H\nHsnMNRFxbnmfi8o/CzNzR8EfT+qSp4mkru0yZgAQESfstjwMqO+0fCiwAniY0mml1cDU8uazKf3l\nfybwZER8JTMXRMTPgM8D04C/BCYW83Gk7nmaSKpcAo0R8bHy8jWUTgt1OIbSmMBNwE8offEPiIhG\n4CXgl5n595RmNx0ZET8ATszM71J6MM4uPQ6pmgwDqUKZuY3S4y0fiojVwMcojQt0+AWwCvgV8HPg\nDeCozGwCvgusiIiVlGY3nU8pNK6PiJ8DtwFXVemjSHtwzECSZM9AkmQYSJIwDCRJGAaSJAwDSRKG\ngSQJw0CSBPx/e0QGiA7Dh3cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105a21cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Pclass'], hue=data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1140aaac8>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x114079b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Sex'], hue=data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x114171da0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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gaRP6+9FW0zWDzLyY4lmDAPYGbsrMyza2fUS8CbgfuDgz7yibn4qIieXyscCjwFxgQkSM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\nGAaSJOD/A19SxXPlkzr9AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110b339e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Embarked'], hue=data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x114343a58>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MzXuh6B6BZvWJPdJzXe2Tvs4kMeEOpV+mnjIpMx/pW0GTRMQ1wBlUw9VdVlMNWxcDDwPn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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1143431d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col='Survived')\n",
"g.map(sns.distplot, 'Age', kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x113f40390>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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l5mPZ00tifY6UJJXNIyhJUpG65ghK0vim+gX3sd7/Z8e8ZkrLlMbTtQ1qZDF5Gk9qzju0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\n2aAkSUWyQUmSivT/IT3e1jZ8koMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113fea748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col='Survived')\n",
"g.map(sns.distplot, 'Fare', kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x114981e10>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x114981710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col='Survived')\n",
"g.map(sns.distplot, 'Parch', kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11468db70>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SGqp//zdS/f73Aee383Xtlt7otr4Ae6PVWtEbpZ5BPTFkEvAXVEMmleqNwLbMXAG8EvhY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\n6FPk9/kK7wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114c03518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col='Survived')\n",
"g.map(sns.distplot, 'SibSp', kde=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Family_Size'] = data['Parch'] + data['SibSp']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11498a2e8>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x114dd5ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(data, col='Survived')\n",
"g.map(sns.distplot, 'Family_Size', kde=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Title1'] = data['Name'].str.split(\", \", expand=True)[1]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Braund</td>\n",
" <td>Mr. Owen Harris</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Cumings</td>\n",
" <td>Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Heikkinen</td>\n",
" <td>Miss. Laina</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 Braund Mr. Owen Harris\n",
"1 Cumings Mrs. John Bradley (Florence Briggs Thayer)\n",
"2 Heikkinen Miss. Laina"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Name'].str.split(\", \", expand=True).head(3)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Mr. Owen Harris\n",
"1 Mrs. John Bradley (Florence Briggs Thayer)\n",
"2 Miss. Laina\n",
"Name: Title1, dtype: object"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Title1'].head(3)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Title1'] = data['Title1'].str.split(\".\", expand=True)[0]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Mr\n",
"1 Mrs\n",
"2 Miss\n",
"Name: Title1, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Title1'].head(3)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
" 'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
" 'Jonkheer', 'Dona'], dtype=object)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Title1'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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" } #T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col16 {\n",
" background-color: #008066;\n",
" } #T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col17 {\n",
" background-color: #ffff66;\n",
" }</style> \n",
"<table id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023\" > \n",
"<thead> <tr> \n",
" <th class=\"index_name level0\" >Title1</th> \n",
" <th class=\"col_heading level0 col0\" >Capt</th> \n",
" <th class=\"col_heading level0 col1\" >Col</th> \n",
" <th class=\"col_heading level0 col2\" >Don</th> \n",
" <th class=\"col_heading level0 col3\" >Dona</th> \n",
" <th class=\"col_heading level0 col4\" >Dr</th> \n",
" <th class=\"col_heading level0 col5\" >Jonkheer</th> \n",
" <th class=\"col_heading level0 col6\" >Lady</th> \n",
" <th class=\"col_heading level0 col7\" >Major</th> \n",
" <th class=\"col_heading level0 col8\" >Master</th> \n",
" <th class=\"col_heading level0 col9\" >Miss</th> \n",
" <th class=\"col_heading level0 col10\" >Mlle</th> \n",
" <th class=\"col_heading level0 col11\" >Mme</th> \n",
" <th class=\"col_heading level0 col12\" >Mr</th> \n",
" <th class=\"col_heading level0 col13\" >Mrs</th> \n",
" <th class=\"col_heading level0 col14\" >Ms</th> \n",
" <th class=\"col_heading level0 col15\" >Rev</th> \n",
" <th class=\"col_heading level0 col16\" >Sir</th> \n",
" <th class=\"col_heading level0 col17\" >the Countess</th> \n",
" </tr> <tr> \n",
" <th class=\"index_name level0\" >Sex</th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023\" class=\"row_heading level0 row0\" >female</th> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col0\" class=\"data row0 col0\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col1\" class=\"data row0 col1\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col2\" class=\"data row0 col2\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col4\" class=\"data row0 col4\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col5\" class=\"data row0 col5\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col6\" class=\"data row0 col6\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col7\" class=\"data row0 col7\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col8\" class=\"data row0 col8\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col9\" class=\"data row0 col9\" >260</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col10\" class=\"data row0 col10\" >2</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col11\" class=\"data row0 col11\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col12\" class=\"data row0 col12\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col13\" class=\"data row0 col13\" >197</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col14\" class=\"data row0 col14\" >2</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col15\" class=\"data row0 col15\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col16\" class=\"data row0 col16\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row0_col17\" class=\"data row0 col17\" >1</td> \n",
" </tr> <tr> \n",
" <th id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023\" class=\"row_heading level0 row1\" >male</th> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col0\" class=\"data row1 col0\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col1\" class=\"data row1 col1\" >4</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col3\" class=\"data row1 col3\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col4\" class=\"data row1 col4\" >7</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col5\" class=\"data row1 col5\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col6\" class=\"data row1 col6\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col7\" class=\"data row1 col7\" >2</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col8\" class=\"data row1 col8\" >61</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col9\" class=\"data row1 col9\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col10\" class=\"data row1 col10\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col11\" class=\"data row1 col11\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col12\" class=\"data row1 col12\" >757</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col13\" class=\"data row1 col13\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col14\" class=\"data row1 col14\" >0</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col15\" class=\"data row1 col15\" >8</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col16\" class=\"data row1 col16\" >1</td> \n",
" <td id=\"T_963d4e5a_c24b_11e7_af8c_186590cc0023row1_col17\" class=\"data row1 col17\" >0</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x11528e128>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(data['Title1'],data['Sex']).T.style.background_gradient(cmap='summer_r')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
" #T_964706de_c24b_11e7_8348_186590cc0023row0_col0 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col1 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col2 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col3 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col4 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col5 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col6 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col7 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col8 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col9 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col10 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col11 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col12 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col13 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col14 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col15 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row0_col16 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col0 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col1 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col2 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col3 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col4 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col5 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col6 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col7 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col8 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col9 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col10 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col11 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col12 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col13 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col14 {\n",
" background-color: #ffff66;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col15 {\n",
" background-color: #008066;\n",
" } #T_964706de_c24b_11e7_8348_186590cc0023row1_col16 {\n",
" background-color: #008066;\n",
" }</style> \n",
"<table id=\"T_964706de_c24b_11e7_8348_186590cc0023\" > \n",
"<thead> <tr> \n",
" <th class=\"index_name level0\" >Title1</th> \n",
" <th class=\"col_heading level0 col0\" >Capt</th> \n",
" <th class=\"col_heading level0 col1\" >Col</th> \n",
" <th class=\"col_heading level0 col2\" >Don</th> \n",
" <th class=\"col_heading level0 col3\" >Dr</th> \n",
" <th class=\"col_heading level0 col4\" >Jonkheer</th> \n",
" <th class=\"col_heading level0 col5\" >Lady</th> \n",
" <th class=\"col_heading level0 col6\" >Major</th> \n",
" <th class=\"col_heading level0 col7\" >Master</th> \n",
" <th class=\"col_heading level0 col8\" >Miss</th> \n",
" <th class=\"col_heading level0 col9\" >Mlle</th> \n",
" <th class=\"col_heading level0 col10\" >Mme</th> \n",
" <th class=\"col_heading level0 col11\" >Mr</th> \n",
" <th class=\"col_heading level0 col12\" >Mrs</th> \n",
" <th class=\"col_heading level0 col13\" >Ms</th> \n",
" <th class=\"col_heading level0 col14\" >Rev</th> \n",
" <th class=\"col_heading level0 col15\" >Sir</th> \n",
" <th class=\"col_heading level0 col16\" >the Countess</th> \n",
" </tr> <tr> \n",
" <th class=\"index_name level0\" >Survived</th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_964706de_c24b_11e7_8348_186590cc0023\" class=\"row_heading level0 row0\" >0.0</th> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col0\" class=\"data row0 col0\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col1\" class=\"data row0 col1\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col3\" class=\"data row0 col3\" >4</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col4\" class=\"data row0 col4\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col5\" class=\"data row0 col5\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col6\" class=\"data row0 col6\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col7\" class=\"data row0 col7\" >17</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col8\" class=\"data row0 col8\" >55</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col9\" class=\"data row0 col9\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col10\" class=\"data row0 col10\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col11\" class=\"data row0 col11\" >436</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col12\" class=\"data row0 col12\" >26</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col13\" class=\"data row0 col13\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col14\" class=\"data row0 col14\" >6</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col15\" class=\"data row0 col15\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row0_col16\" class=\"data row0 col16\" >0</td> \n",
" </tr> <tr> \n",
" <th id=\"T_964706de_c24b_11e7_8348_186590cc0023\" class=\"row_heading level0 row1\" >1.0</th> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col0\" class=\"data row1 col0\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col1\" class=\"data row1 col1\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col2\" class=\"data row1 col2\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col3\" class=\"data row1 col3\" >3</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col4\" class=\"data row1 col4\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col5\" class=\"data row1 col5\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col6\" class=\"data row1 col6\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col7\" class=\"data row1 col7\" >23</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col8\" class=\"data row1 col8\" >127</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col9\" class=\"data row1 col9\" >2</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col10\" class=\"data row1 col10\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col11\" class=\"data row1 col11\" >81</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col12\" class=\"data row1 col12\" >99</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col13\" class=\"data row1 col13\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col14\" class=\"data row1 col14\" >0</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col15\" class=\"data row1 col15\" >1</td> \n",
" <td id=\"T_964706de_c24b_11e7_8348_186590cc0023row1_col16\" class=\"data row1 col16\" >1</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1145f9320>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(data['Title1'],data['Survived']).T.style.background_gradient(cmap='summer_r')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Title1\n",
"Capt 70.000000\n",
"Col 54.000000\n",
"Don 40.000000\n",
"Dona 39.000000\n",
"Dr 43.571429\n",
"Jonkheer 38.000000\n",
"Lady 48.000000\n",
"Major 48.500000\n",
"Master 5.482642\n",
"Miss 21.774238\n",
"Mlle 24.000000\n",
"Mme 24.000000\n",
"Mr 32.252151\n",
"Mrs 36.994118\n",
"Ms 28.000000\n",
"Rev 41.250000\n",
"Sir 49.000000\n",
"the Countess 33.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Title1'])['Age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Title1 Pclass\n",
"Capt 1 70.000000\n",
"Col 1 54.000000\n",
"Don 1 40.000000\n",
"Dona 1 39.000000\n",
"Dr 1 45.600000\n",
" 2 38.500000\n",
"Jonkheer 1 38.000000\n",
"Lady 1 48.000000\n",
"Major 1 48.500000\n",
"Master 1 6.984000\n",
" 2 2.757273\n",
" 3 6.090000\n",
"Miss 1 30.338983\n",
" 2 20.717083\n",
" 3 17.360874\n",
"Mlle 1 24.000000\n",
"Mme 1 24.000000\n",
"Mr 1 41.450758\n",
" 2 32.346715\n",
" 3 28.318910\n",
"Mrs 1 43.208955\n",
" 2 33.518519\n",
" 3 32.326531\n",
"Ms 2 28.000000\n",
" 3 NaN\n",
"Rev 2 41.250000\n",
"Sir 1 49.000000\n",
"the Countess 1 33.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Title1','Pclass'])['Age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Title2'] = data['Title1'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','the Countess','Jonkheer','Col','Rev','Capt','Sir','Don','Dona'],\n",
" ['Miss','Mrs','Miss','Mr','Mr','Mrs','Mrs','Mr','Mr','Mr','Mr','Mr','Mr','Mrs'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Mr', 'Mrs', 'Miss', 'Master'], dtype=object)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Title2'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Title2\n",
"Master 5.482642\n",
"Miss 21.824366\n",
"Mr 32.811056\n",
"Mrs 36.971264\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('Title2')['Age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Title2\n",
"Master 5.482642\n",
"Miss 21.824366\n",
"Mr 32.811056\n",
"Mrs 36.971264\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Title2'])['Age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Title2 Pclass\n",
"Master 1 6.984000\n",
" 2 2.757273\n",
" 3 6.090000\n",
"Miss 1 30.131148\n",
" 2 20.865714\n",
" 3 17.360874\n",
"Mr 1 42.241497\n",
" 2 32.914966\n",
" 3 28.318910\n",
"Mrs 1 42.802817\n",
" 2 33.518519\n",
" 3 32.326531\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Title2','Pclass'])['Age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
" #T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col0 {\n",
" background-color: #ffff66;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col1 {\n",
" background-color: #008066;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col2 {\n",
" background-color: #ffff66;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col3 {\n",
" background-color: #008066;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col0 {\n",
" background-color: #008066;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col1 {\n",
" background-color: #ffff66;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col2 {\n",
" background-color: #008066;\n",
" } #T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col3 {\n",
" background-color: #ffff66;\n",
" }</style> \n",
"<table id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023\" > \n",
"<thead> <tr> \n",
" <th class=\"index_name level0\" >Title2</th> \n",
" <th class=\"col_heading level0 col0\" >Master</th> \n",
" <th class=\"col_heading level0 col1\" >Miss</th> \n",
" <th class=\"col_heading level0 col2\" >Mr</th> \n",
" <th class=\"col_heading level0 col3\" >Mrs</th> \n",
" </tr> <tr> \n",
" <th class=\"index_name level0\" >Sex</th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023\" class=\"row_heading level0 row0\" >female</th> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col0\" class=\"data row0 col0\" >0</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col1\" class=\"data row0 col1\" >264</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row0_col3\" class=\"data row0 col3\" >201</td> \n",
" </tr> <tr> \n",
" <th id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023\" class=\"row_heading level0 row1\" >male</th> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col0\" class=\"data row1 col0\" >61</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col1\" class=\"data row1 col1\" >0</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col2\" class=\"data row1 col2\" >782</td> \n",
" <td id=\"T_9654e90c_c24b_11e7_a5af_186590cc0023row1_col3\" class=\"data row1 col3\" >0</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1145f90b8>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(data['Title2'],data['Sex']).T.style.background_gradient(cmap='summer_r') #Checking the Initials with the Sex"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
" #T_965992e8_c24b_11e7_bab8_186590cc0023row0_col0 {\n",
" background-color: #ffff66;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row0_col1 {\n",
" background-color: #ffff66;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row0_col2 {\n",
" background-color: #008066;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row0_col3 {\n",
" background-color: #ffff66;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row1_col0 {\n",
" background-color: #008066;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row1_col1 {\n",
" background-color: #008066;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row1_col2 {\n",
" background-color: #ffff66;\n",
" } #T_965992e8_c24b_11e7_bab8_186590cc0023row1_col3 {\n",
" background-color: #008066;\n",
" }</style> \n",
"<table id=\"T_965992e8_c24b_11e7_bab8_186590cc0023\" > \n",
"<thead> <tr> \n",
" <th class=\"index_name level0\" >Title2</th> \n",
" <th class=\"col_heading level0 col0\" >Master</th> \n",
" <th class=\"col_heading level0 col1\" >Miss</th> \n",
" <th class=\"col_heading level0 col2\" >Mr</th> \n",
" <th class=\"col_heading level0 col3\" >Mrs</th> \n",
" </tr> <tr> \n",
" <th class=\"index_name level0\" >Survived</th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" <th class=\"blank\" ></th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_965992e8_c24b_11e7_bab8_186590cc0023\" class=\"row_heading level0 row0\" >0.0</th> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row0_col0\" class=\"data row0 col0\" >17</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row0_col1\" class=\"data row0 col1\" >55</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row0_col2\" class=\"data row0 col2\" >451</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row0_col3\" class=\"data row0 col3\" >26</td> \n",
" </tr> <tr> \n",
" <th id=\"T_965992e8_c24b_11e7_bab8_186590cc0023\" class=\"row_heading level0 row1\" >1.0</th> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row1_col0\" class=\"data row1 col0\" >23</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row1_col1\" class=\"data row1 col1\" >130</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row1_col2\" class=\"data row1 col2\" >87</td> \n",
" <td id=\"T_965992e8_c24b_11e7_bab8_186590cc0023row1_col3\" class=\"data row1 col3\" >102</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x1154687b8>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(data['Title2'],data['Survived']).T.style.background_gradient(cmap='summer_r') #Checking the Initials with the Sex"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(('Master', 1), 6.984),\n",
" (('Master', 2), 2.7572727272727273),\n",
" (('Master', 3), 6.0900000000000007)]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(data.groupby(['Title2','Pclass'])['Age'].mean().iteritems())[:3]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1309 entries, 0 to 1308\n",
"Data columns (total 15 columns):\n",
"Age 1046 non-null float64\n",
"Cabin 295 non-null object\n",
"Embarked 1307 non-null object\n",
"Fare 1308 non-null float64\n",
"Name 1309 non-null object\n",
"Parch 1309 non-null int64\n",
"PassengerId 1309 non-null int64\n",
"Pclass 1309 non-null int64\n",
"Sex 1309 non-null object\n",
"SibSp 1309 non-null int64\n",
"Survived 891 non-null float64\n",
"Ticket 1309 non-null object\n",
"Family_Size 1309 non-null int64\n",
"Title1 1309 non-null object\n",
"Title2 1309 non-null object\n",
"dtypes: float64(3), int64(5), object(7)\n",
"memory usage: 153.5+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Ticket_info'] = data['Ticket'].apply(lambda x : x.replace(\".\",\"\").replace(\"/\",\"\").strip().split(' ')[0] if not x.isdigit() else 'X')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['A5', 'PC', 'STONO2', 'X', 'PP', 'CA', 'SCParis', 'SCA4', 'A4',\n",
" 'SP', 'SOC', 'WC', 'SOTONOQ', 'WEP', 'STONO', 'C', 'SCPARIS', 'SOP',\n",
" 'Fa', 'LINE', 'FCC', 'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH', 'AS',\n",
" 'SOPP', 'FC', 'SOTONO2', 'CASOTON', 'SCA3', 'STONOQ', 'AQ4', 'A',\n",
" 'LP', 'AQ3'], dtype=object)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Ticket_info'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1154ac2e8>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/N3A6cErqPZwC/J3IGzul6zeMaKisTg+W14MHEUFrIFFeFqd9zQR+DZyRrs/u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\nGaQHd3uuDQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114ed5a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Ticket_info'], hue=data['Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing Value-embarked、Fare、Age"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Embarked'] = data['Embarked'].fillna('S')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1309 entries, 0 to 1308\n",
"Data columns (total 16 columns):\n",
"Age 1046 non-null float64\n",
"Cabin 295 non-null object\n",
"Embarked 1309 non-null object\n",
"Fare 1308 non-null float64\n",
"Name 1309 non-null object\n",
"Parch 1309 non-null int64\n",
"PassengerId 1309 non-null int64\n",
"Pclass 1309 non-null int64\n",
"Sex 1309 non-null object\n",
"SibSp 1309 non-null int64\n",
"Survived 891 non-null float64\n",
"Ticket 1309 non-null object\n",
"Family_Size 1309 non-null int64\n",
"Title1 1309 non-null object\n",
"Title2 1309 non-null object\n",
"Ticket_info 1309 non-null object\n",
"dtypes: float64(3), int64(5), object(8)\n",
"memory usage: 163.7+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Fare'] = data['Fare'].fillna(data['Fare'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1309 entries, 0 to 1308\n",
"Data columns (total 16 columns):\n",
"Age 1046 non-null float64\n",
"Cabin 295 non-null object\n",
"Embarked 1309 non-null object\n",
"Fare 1309 non-null float64\n",
"Name 1309 non-null object\n",
"Parch 1309 non-null int64\n",
"PassengerId 1309 non-null int64\n",
"Pclass 1309 non-null int64\n",
"Sex 1309 non-null object\n",
"SibSp 1309 non-null int64\n",
"Survived 891 non-null float64\n",
"Ticket 1309 non-null object\n",
"Family_Size 1309 non-null int64\n",
"Title1 1309 non-null object\n",
"Title2 1309 non-null object\n",
"Ticket_info 1309 non-null object\n",
"dtypes: float64(3), int64(5), object(8)\n",
"memory usage: 163.7+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 NaN\n",
"1 C85\n",
"2 NaN\n",
"3 C123\n",
"4 NaN\n",
"5 NaN\n",
"6 E46\n",
"7 NaN\n",
"8 NaN\n",
"9 NaN\n",
"Name: Cabin, dtype: object"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Cabin'].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data[\"Cabin\"] = data['Cabin'].apply(lambda x : str(x)[0] if not pd.isnull(x) else 'NoCabin')"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['NoCabin', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"Cabin\"].unique()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115596588>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEHCAYAAABMRSrcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115543f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data['Cabin'], hue=data['Survived'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['Sex'] = data['Sex'].astype('category').cat.codes\n",
"data['Embarked'] = data['Embarked'].astype('category').cat.codes\n",
"data['Pclass'] = data['Pclass'].astype('category').cat.codes\n",
"data['Title1'] = data['Title1'].astype('category').cat.codes\n",
"data['Title2'] = data['Title2'].astype('category').cat.codes\n",
"data['Cabin'] = data['Cabin'].astype('category').cat.codes\n",
"data['Ticket_info'] = data['Ticket_info'].astype('category').cat.codes"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"dataAgeNull = data[data[\"Age\"].isnull()]\n",
"dataAgeNotNull = data[data[\"Age\"].notnull()]\n",
"remove_outlier = dataAgeNotNull[(np.abs(dataAgeNotNull[\"Fare\"]-dataAgeNotNull[\"Fare\"].mean())>(4*dataAgeNotNull[\"Fare\"].std()))|\n",
" (np.abs(dataAgeNotNull[\"Family_Size\"]-dataAgeNotNull[\"Family_Size\"].mean())>(4*dataAgeNotNull[\"Family_Size\"].std())) \n",
" ]\n",
"rfModel_age = RandomForestRegressor(n_estimators=2000,random_state=42)\n",
"ageColumns = ['Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title1', 'Title2','Cabin','Ticket_info']\n",
"rfModel_age.fit(remove_outlier[ageColumns], remove_outlier[\"Age\"])\n",
"\n",
"ageNullValues = rfModel_age.predict(X= dataAgeNull[ageColumns])\n",
"dataAgeNull.loc[:,\"Age\"] = ageNullValues\n",
"data = dataAgeNull.append(dataAgeNotNull)\n",
"data.reset_index(inplace=True, drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataTrain = data[pd.notnull(data['Survived'])].sort_values(by=[\"PassengerId\"])\n",
"dataTest = data[~pd.notnull(data['Survived'])].sort_values(by=[\"PassengerId\"])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Age', 'Cabin', 'Embarked', 'Fare', 'Name', 'Parch', 'PassengerId',\n",
" 'Pclass', 'Sex', 'SibSp', 'Survived', 'Ticket', 'Family_Size', 'Title1',\n",
" 'Title2', 'Ticket_info'],\n",
" dtype='object')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataTrain.columns"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataTrain = dataTrain[['Survived', 'Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']]\n",
"dataTest = dataTest[['Age', 'Embarked', 'Fare', 'Pclass', 'Sex', 'Family_Size', 'Title2','Ticket_info','Cabin']]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\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>Age</th>\n",
" <th>Embarked</th>\n",
" <th>Fare</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Family_Size</th>\n",
" <th>Title2</th>\n",
" <th>Ticket_info</th>\n",
" <th>Cabin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>0.0</td>\n",
" <td>22.000000</td>\n",
" <td>2</td>\n",
" <td>7.2500</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>1.0</td>\n",
" <td>38.000000</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>1.0</td>\n",
" <td>26.000000</td>\n",
" <td>2</td>\n",
" <td>7.9250</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>1.0</td>\n",
" <td>35.000000</td>\n",
" <td>2</td>\n",
" <td>53.1000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>0.0</td>\n",
" <td>35.000000</td>\n",
" <td>2</td>\n",
" <td>8.0500</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>41.326267</td>\n",
" <td>1</td>\n",
" <td>8.4583</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>0.0</td>\n",
" <td>54.000000</td>\n",
" <td>2</td>\n",
" <td>51.8625</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>0.0</td>\n",
" <td>2.000000</td>\n",
" <td>2</td>\n",
" <td>21.0750</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>1.0</td>\n",
" <td>27.000000</td>\n",
" <td>2</td>\n",
" <td>11.1333</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>1.0</td>\n",
" <td>14.000000</td>\n",
" <td>0</td>\n",
" <td>30.0708</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>1.0</td>\n",
" <td>4.000000</td>\n",
" <td>2</td>\n",
" <td>16.7000</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>1.0</td>\n",
" <td>58.000000</td>\n",
" <td>2</td>\n",
" <td>26.5500</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>0.0</td>\n",
" <td>20.000000</td>\n",
" <td>2</td>\n",
" <td>8.0500</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>0.0</td>\n",
" <td>39.000000</td>\n",
" <td>2</td>\n",
" <td>31.2750</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>0.0</td>\n",
" <td>14.000000</td>\n",
" <td>2</td>\n",
" <td>7.8542</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>1.0</td>\n",
" <td>55.000000</td>\n",
" <td>2</td>\n",
" <td>16.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>0.0</td>\n",
" <td>2.000000</td>\n",
" <td>1</td>\n",
" <td>29.1250</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>41.616486</td>\n",
" <td>2</td>\n",
" <td>13.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>0.0</td>\n",
" <td>31.000000</td>\n",
" <td>2</td>\n",
" <td>18.0000</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>46.792625</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>0.0</td>\n",
" <td>35.000000</td>\n",
" <td>2</td>\n",
" <td>26.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>1.0</td>\n",
" <td>34.000000</td>\n",
" <td>2</td>\n",
" <td>13.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>1.0</td>\n",
" <td>15.000000</td>\n",
" <td>1</td>\n",
" <td>8.0292</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>1.0</td>\n",
" <td>28.000000</td>\n",
" <td>2</td>\n",
" <td>35.5000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>0.0</td>\n",
" <td>8.000000</td>\n",
" <td>2</td>\n",
" <td>21.0750</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>1.0</td>\n",
" <td>38.000000</td>\n",
" <td>2</td>\n",
" <td>31.3875</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>41.326267</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>0.0</td>\n",
" <td>19.000000</td>\n",
" <td>2</td>\n",
" <td>263.0000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>34.860886</td>\n",
" <td>1</td>\n",
" <td>7.8792</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.0</td>\n",
" <td>39.428653</td>\n",
" <td>2</td>\n",
" <td>7.8958</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</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",
" </tr>\n",
" <tr>\n",
" <th>951</th>\n",
" <td>0.0</td>\n",
" <td>21.000000</td>\n",
" <td>2</td>\n",
" <td>11.5000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>1.0</td>\n",
" <td>48.000000</td>\n",
" <td>2</td>\n",
" <td>25.9292</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>0.0</td>\n",
" <td>15.470411</td>\n",
" <td>2</td>\n",
" <td>69.5500</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>0.0</td>\n",
" <td>24.000000</td>\n",
" <td>2</td>\n",
" <td>13.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>954</th>\n",
" <td>1.0</td>\n",
" <td>42.000000</td>\n",
" <td>2</td>\n",
" <td>13.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>955</th>\n",
" <td>1.0</td>\n",
" <td>27.000000</td>\n",
" <td>0</td>\n",
" <td>13.8583</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>22</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>956</th>\n",
" <td>0.0</td>\n",
" <td>31.000000</td>\n",
" <td>2</td>\n",
" <td>50.4958</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>0.0</td>\n",
" <td>39.428653</td>\n",
" <td>2</td>\n",
" <td>9.5000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>957</th>\n",
" <td>1.0</td>\n",
" <td>4.000000</td>\n",
" <td>2</td>\n",
" <td>11.1333</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958</th>\n",
" <td>0.0</td>\n",
" <td>26.000000</td>\n",
" <td>2</td>\n",
" <td>7.8958</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>1.0</td>\n",
" <td>47.000000</td>\n",
" <td>2</td>\n",
" <td>52.5542</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>960</th>\n",
" <td>0.0</td>\n",
" <td>33.000000</td>\n",
" <td>2</td>\n",
" <td>5.0000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>961</th>\n",
" <td>0.0</td>\n",
" <td>47.000000</td>\n",
" <td>2</td>\n",
" <td>9.0000</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>962</th>\n",
" <td>1.0</td>\n",
" <td>28.000000</td>\n",
" <td>0</td>\n",
" <td>24.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963</th>\n",
" <td>1.0</td>\n",
" <td>15.000000</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>0.0</td>\n",
" <td>20.000000</td>\n",
" <td>2</td>\n",
" <td>9.8458</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>965</th>\n",
" <td>0.0</td>\n",
" <td>19.000000</td>\n",
" <td>2</td>\n",
" <td>7.8958</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>0.0</td>\n",
" <td>39.428653</td>\n",
" <td>2</td>\n",
" <td>7.8958</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>966</th>\n",
" <td>1.0</td>\n",
" <td>56.000000</td>\n",
" <td>0</td>\n",
" <td>83.1583</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>967</th>\n",
" <td>1.0</td>\n",
" <td>25.000000</td>\n",
" <td>2</td>\n",
" <td>26.0000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>968</th>\n",
" <td>0.0</td>\n",
" <td>33.000000</td>\n",
" <td>2</td>\n",
" <td>7.8958</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>969</th>\n",
" <td>0.0</td>\n",
" <td>22.000000</td>\n",
" <td>2</td>\n",
" <td>10.5167</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>0.0</td>\n",
" <td>28.000000</td>\n",
" <td>2</td>\n",
" <td>10.5000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>0.0</td>\n",
" <td>25.000000</td>\n",
" <td>2</td>\n",
" <td>7.0500</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>28</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>0.0</td>\n",
" <td>39.000000</td>\n",
" <td>1</td>\n",
" <td>29.1250</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>0.0</td>\n",
" <td>27.000000</td>\n",
" <td>2</td>\n",
" <td>13.0000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>1.0</td>\n",
" <td>19.000000</td>\n",
" <td>2</td>\n",
" <td>30.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>0.0</td>\n",
" <td>19.099409</td>\n",
" <td>2</td>\n",
" <td>23.4500</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>34</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>1.0</td>\n",
" <td>26.000000</td>\n",
" <td>0</td>\n",
" <td>30.0000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>0.0</td>\n",
" <td>32.000000</td>\n",
" <td>1</td>\n",
" <td>7.7500</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Survived Age Embarked Fare Pclass Sex Family_Size \\\n",
"263 0.0 22.000000 2 7.2500 2 1 1 \n",
"264 1.0 38.000000 0 71.2833 0 0 1 \n",
"265 1.0 26.000000 2 7.9250 2 0 0 \n",
"266 1.0 35.000000 2 53.1000 0 0 1 \n",
"267 0.0 35.000000 2 8.0500 2 1 0 \n",
"0 0.0 41.326267 1 8.4583 2 1 0 \n",
"268 0.0 54.000000 2 51.8625 0 1 0 \n",
"269 0.0 2.000000 2 21.0750 2 1 4 \n",
"270 1.0 27.000000 2 11.1333 2 0 2 \n",
"271 1.0 14.000000 0 30.0708 1 0 1 \n",
"272 1.0 4.000000 2 16.7000 2 0 2 \n",
"273 1.0 58.000000 2 26.5500 0 0 0 \n",
"274 0.0 20.000000 2 8.0500 2 1 0 \n",
"275 0.0 39.000000 2 31.2750 2 1 6 \n",
"276 0.0 14.000000 2 7.8542 2 0 0 \n",
"277 1.0 55.000000 2 16.0000 1 0 0 \n",
"278 0.0 2.000000 1 29.1250 2 1 5 \n",
"1 1.0 41.616486 2 13.0000 1 1 0 \n",
"279 0.0 31.000000 2 18.0000 2 0 1 \n",
"2 1.0 46.792625 0 7.2250 2 0 0 \n",
"280 0.0 35.000000 2 26.0000 1 1 0 \n",
"281 1.0 34.000000 2 13.0000 1 1 0 \n",
"282 1.0 15.000000 1 8.0292 2 0 0 \n",
"283 1.0 28.000000 2 35.5000 0 1 0 \n",
"284 0.0 8.000000 2 21.0750 2 0 4 \n",
"285 1.0 38.000000 2 31.3875 2 0 6 \n",
"3 0.0 41.326267 0 7.2250 2 1 0 \n",
"286 0.0 19.000000 2 263.0000 0 1 5 \n",
"4 1.0 34.860886 1 7.8792 2 0 0 \n",
"5 0.0 39.428653 2 7.8958 2 1 0 \n",
".. ... ... ... ... ... ... ... \n",
"951 0.0 21.000000 2 11.5000 1 1 1 \n",
"952 1.0 48.000000 2 25.9292 0 0 0 \n",
"173 0.0 15.470411 2 69.5500 2 0 10 \n",
"953 0.0 24.000000 2 13.0000 1 1 0 \n",
"954 1.0 42.000000 2 13.0000 1 0 0 \n",
"955 1.0 27.000000 0 13.8583 1 0 1 \n",
"956 0.0 31.000000 2 50.4958 0 1 0 \n",
"174 0.0 39.428653 2 9.5000 2 1 0 \n",
"957 1.0 4.000000 2 11.1333 2 1 2 \n",
"958 0.0 26.000000 2 7.8958 2 1 0 \n",
"959 1.0 47.000000 2 52.5542 0 0 2 \n",
"960 0.0 33.000000 2 5.0000 0 1 0 \n",
"961 0.0 47.000000 2 9.0000 2 1 0 \n",
"962 1.0 28.000000 0 24.0000 1 0 1 \n",
"963 1.0 15.000000 0 7.2250 2 0 0 \n",
"964 0.0 20.000000 2 9.8458 2 1 0 \n",
"965 0.0 19.000000 2 7.8958 2 1 0 \n",
"175 0.0 39.428653 2 7.8958 2 1 0 \n",
"966 1.0 56.000000 0 83.1583 0 0 1 \n",
"967 1.0 25.000000 2 26.0000 1 0 1 \n",
"968 0.0 33.000000 2 7.8958 2 1 0 \n",
"969 0.0 22.000000 2 10.5167 2 0 0 \n",
"970 0.0 28.000000 2 10.5000 1 1 0 \n",
"971 0.0 25.000000 2 7.0500 2 1 0 \n",
"972 0.0 39.000000 1 29.1250 2 0 5 \n",
"973 0.0 27.000000 2 13.0000 1 1 0 \n",
"974 1.0 19.000000 2 30.0000 0 0 0 \n",
"176 0.0 19.099409 2 23.4500 2 0 3 \n",
"975 1.0 26.000000 0 30.0000 0 1 0 \n",
"976 0.0 32.000000 1 7.7500 2 1 0 \n",
"\n",
" Title2 Ticket_info Cabin \n",
"263 2 2 7 \n",
"264 3 14 2 \n",
"265 1 31 7 \n",
"266 3 36 2 \n",
"267 2 36 7 \n",
"0 2 36 7 \n",
"268 2 36 4 \n",
"269 0 36 7 \n",
"270 3 36 7 \n",
"271 3 36 7 \n",
"272 1 15 6 \n",
"273 1 36 2 \n",
"274 2 2 7 \n",
"275 2 36 7 \n",
"276 1 36 7 \n",
"277 3 36 7 \n",
"278 0 36 7 \n",
"1 2 36 7 \n",
"279 3 36 7 \n",
"2 3 36 7 \n",
"280 2 36 7 \n",
"281 2 36 3 \n",
"282 1 36 7 \n",
"283 2 36 0 \n",
"284 1 36 7 \n",
"285 3 36 7 \n",
"3 2 36 7 \n",
"286 2 36 2 \n",
"4 1 36 7 \n",
"5 2 36 7 \n",
".. ... ... ... \n",
"951 2 36 7 \n",
"952 3 36 3 \n",
"173 1 7 7 \n",
"953 2 36 7 \n",
"954 3 36 7 \n",
"955 1 22 7 \n",
"956 2 14 0 \n",
"174 2 36 7 \n",
"957 0 36 7 \n",
"958 2 36 7 \n",
"959 3 36 3 \n",
"960 2 36 1 \n",
"961 2 36 7 \n",
"962 3 16 7 \n",
"963 1 36 7 \n",
"964 2 36 7 \n",
"965 2 36 7 \n",
"175 2 36 7 \n",
"966 3 36 2 \n",
"967 3 36 7 \n",
"968 2 36 7 \n",
"969 1 36 7 \n",
"970 2 8 7 \n",
"971 2 28 7 \n",
"972 3 36 7 \n",
"973 2 36 7 \n",
"974 1 36 1 \n",
"176 1 34 7 \n",
"975 2 36 2 \n",
"976 2 36 7 \n",
"\n",
"[891 rows x 10 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataTrain"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8d534c0e-6d69-48c1-9229-0dfdc47775a6",
"_uuid": "d6e6f1310db0e9f497ef1ed98dd18d5ed5fe888b"
},
"source": [
"## Model training"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"_cell_guid": "db9f2d4a-befc-4b68-9f31-cc5d9816fb6f",
"_execution_state": "busy",
"_uuid": "b500e8cc97da46ed010fff936445056e2be4dcc3"
},
"outputs": [],
"source": [
"# rf = RandomForestClassifier(oob_score=True, random_state=1, n_jobs=-1)\n",
"# param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10], \"min_samples_split\" : [2, 4, 10, 12, 16, 20], \"n_estimators\": [50, 100, 400, 700, 1000]}\n",
"# gs = GridSearchCV(estimator=rf, param_grid=param_grid, scoring='accuracy', cv=3, n_jobs=-1)\n",
"\n",
"# gs = gs.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0])\n",
"\n",
"# print(gs.best_score_)\n",
"# print(gs.best_params_) \n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"_cell_guid": "46d3294b-f3c0-46e8-9eed-e6a9c99b6039",
"_execution_state": "busy",
"_uuid": "2361c2e4e0ed4b3eefeb614ed7881b8690a6a2ee"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8294\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
" \n",
"rf = RandomForestClassifier(criterion='gini', \n",
" n_estimators=1000,\n",
" min_samples_split=12,\n",
" min_samples_leaf=1,\n",
" oob_score=True,\n",
" random_state=1,\n",
" n_jobs=-1) \n",
"\n",
"rf.fit(dataTrain.iloc[:, 1:], dataTrain.iloc[:, 0])\n",
"print(\"%.4f\" % rf.oob_score_)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"_cell_guid": "4444e10c-159a-4f38-a4cd-d31d8ab9ac5d",
"_execution_state": "busy",
"_uuid": "1cd3a701900b591bdad1e1e19f385abe33220b59"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>variable</th>\n",
" <th>importance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sex</td>\n",
" <td>0.264997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Fare</td>\n",
" <td>0.163890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Title2</td>\n",
" <td>0.152698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Age</td>\n",
" <td>0.131891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Pclass</td>\n",
" <td>0.091048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Family_Size</td>\n",
" <td>0.070839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Cabin</td>\n",
" <td>0.067029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Ticket_info</td>\n",
" <td>0.031735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Embarked</td>\n",
" <td>0.025873</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" variable importance\n",
"4 Sex 0.264997\n",
"2 Fare 0.163890\n",
"6 Title2 0.152698\n",
"0 Age 0.131891\n",
"3 Pclass 0.091048\n",
"5 Family_Size 0.070839\n",
"8 Cabin 0.067029\n",
"7 Ticket_info 0.031735\n",
"1 Embarked 0.025873"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat((pd.DataFrame(dataTrain.iloc[:, 1:].columns, columns = ['variable']), \n",
" pd.DataFrame(rf.feature_importances_, columns = ['importance'])), \n",
" axis = 1).sort_values(by='importance', ascending = False)[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4867dfc3-023c-48fa-8970-bc0a1fbe3547",
"_uuid": "bbad5aff8f09eb3fabe732b03d1d5d4f828d52d7"
},
"source": [
"## Submit"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"_cell_guid": "de6b05c8-17b7-45c2-b813-24af91c2eaca",
"_execution_state": "busy",
"_uuid": "c5dccff507ecae39fc76de6717ac0a06f3a12bf0",
"collapsed": true
},
"outputs": [],
"source": [
"rf_res = rf.predict(dataTest)\n",
"submit['Survived'] = rf_res\n",
"submit['Survived'] = submit['Survived'].astype(int)\n",
"submit.to_csv('submit.csv', index= False)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"_cell_guid": "b353357c-0fd3-4f27-b8f7-b1b0f575a548",
"_execution_state": "busy",
"_uuid": "629c209b036372241851c1a19f7d3261b2817169",
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>892</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>893</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>894</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>895</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>896</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>897</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>898</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>899</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>900</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>901</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>902</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>903</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>904</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>905</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>906</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>907</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>908</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>909</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>910</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>911</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>912</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>913</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>914</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>915</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>916</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>917</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>918</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>919</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>920</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>921</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td>1280</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>1281</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>1282</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>1283</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>1284</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>1285</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>1286</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>1287</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>1288</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>1289</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>1290</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>1291</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>1292</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401</th>\n",
" <td>1293</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402</th>\n",
" <td>1294</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403</th>\n",
" <td>1295</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>404</th>\n",
" <td>1296</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>1297</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>1298</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>1299</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>408</th>\n",
" <td>1300</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>1301</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>410</th>\n",
" <td>1302</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>411</th>\n",
" <td>1303</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>1304</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>1305</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>1306</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>1307</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>1308</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>1309</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>418 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived\n",
"0 892 0\n",
"1 893 1\n",
"2 894 0\n",
"3 895 0\n",
"4 896 1\n",
"5 897 0\n",
"6 898 0\n",
"7 899 0\n",
"8 900 1\n",
"9 901 0\n",
"10 902 0\n",
"11 903 0\n",
"12 904 1\n",
"13 905 0\n",
"14 906 1\n",
"15 907 1\n",
"16 908 0\n",
"17 909 0\n",
"18 910 0\n",
"19 911 1\n",
"20 912 0\n",
"21 913 1\n",
"22 914 1\n",
"23 915 0\n",
"24 916 1\n",
"25 917 0\n",
"26 918 1\n",
"27 919 0\n",
"28 920 1\n",
"29 921 0\n",
".. ... ...\n",
"388 1280 0\n",
"389 1281 0\n",
"390 1282 0\n",
"391 1283 1\n",
"392 1284 1\n",
"393 1285 0\n",
"394 1286 0\n",
"395 1287 1\n",
"396 1288 0\n",
"397 1289 1\n",
"398 1290 0\n",
"399 1291 0\n",
"400 1292 1\n",
"401 1293 0\n",
"402 1294 1\n",
"403 1295 0\n",
"404 1296 0\n",
"405 1297 0\n",
"406 1298 0\n",
"407 1299 0\n",
"408 1300 1\n",
"409 1301 1\n",
"410 1302 1\n",
"411 1303 1\n",
"412 1304 0\n",
"413 1305 0\n",
"414 1306 1\n",
"415 1307 0\n",
"416 1308 0\n",
"417 1309 1\n",
"\n",
"[418 rows x 2 columns]"
]
},
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