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rohit-nsit08/titanic.ipynb

Created May 20, 2017 10:21
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titanic.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from pandas import Series, DataFrame\n",
"from IPython.display import display\n",
"sns.set_style('whitegrid')\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load the data in dataframe\n",
"\n",
"titanic_df = pd.read_csv('train.csv')\n",
"test_df = pd.read_csv('test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</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": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Preview the data\n",
"\n",
"titanic_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"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",
"--------------------------------------\n",
"<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": [
"# Check Train and Test data\n",
"\n",
"# titanic_df.columns\n",
"# titanic_df.dtypes\n",
"\n",
"titanic_df.info()\n",
"print(\"--------------------------------------\")\n",
"test_df.info()\n"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Drop unnecessary columns\n",
"# axis = 1 implies columns --> e.g.\n",
"\"\"\"\n",
"+------------+---------+--------+\n",
"| | A | B |\n",
"+------------+---------+---------\n",
"| 0 | 0.626386| 1.52325|----axis=1----->\n",
"+------------+---------+--------+\n",
" | |\n",
" | axis=0 |\n",
" ↓ ↓\n",
"\"\"\"\n",
"titanic_df = titanic_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1)\n",
"test_df = test_df.drop(['Name', 'Ticket', 'Cabin'], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
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" <td>7.9250</td>\n",
" <td>S</td>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>S</td>\n",
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" <td>35.0</td>\n",
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" <td>8.0500</td>\n",
" <td>S</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.4583</td>\n",
" <td>Q</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
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" <td>0</td>\n",
" <td>51.8625</td>\n",
" <td>S</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21.0750</td>\n",
" <td>S</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>11.1333</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>30.0708</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>16.7000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>58.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.5500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>39.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>31.2750</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8542</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>55.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>29.1250</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>S</td>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>18.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>34.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0292</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>35.5000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>8.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>21.0750</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>31.3875</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>263.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8792</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>S</td>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>861</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>21.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>11.5000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>48.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.9292</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>69.5500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>24.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>42.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>13.8583</td>\n",
" <td>C</td>\n",
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" <tr>\n",
" <th>867</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50.4958</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.5000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>11.1333</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>52.5542</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>47.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>28.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>24.0000</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>15.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.2250</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>20.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.8458</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>56.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>83.1583</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>female</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>26.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>33.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.8958</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>22.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.5167</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>28.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10.5000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.0500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>39.0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>29.1250</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.0000</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>23.4500</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.0000</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.7500</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age SibSp Parch Fare Embarked\n",
"0 0 3 male 22.0 1 0 7.2500 S\n",
"1 1 1 female 38.0 1 0 71.2833 C\n",
"2 1 3 female 26.0 0 0 7.9250 S\n",
"3 1 1 female 35.0 1 0 53.1000 S\n",
"4 0 3 male 35.0 0 0 8.0500 S\n",
"5 0 3 male NaN 0 0 8.4583 Q\n",
"6 0 1 male 54.0 0 0 51.8625 S\n",
"7 0 3 male 2.0 3 1 21.0750 S\n",
"8 1 3 female 27.0 0 2 11.1333 S\n",
"9 1 2 female 14.0 1 0 30.0708 C\n",
"10 1 3 female 4.0 1 1 16.7000 S\n",
"11 1 1 female 58.0 0 0 26.5500 S\n",
"12 0 3 male 20.0 0 0 8.0500 S\n",
"13 0 3 male 39.0 1 5 31.2750 S\n",
"14 0 3 female 14.0 0 0 7.8542 S\n",
"15 1 2 female 55.0 0 0 16.0000 S\n",
"16 0 3 male 2.0 4 1 29.1250 Q\n",
"17 1 2 male NaN 0 0 13.0000 S\n",
"18 0 3 female 31.0 1 0 18.0000 S\n",
"19 1 3 female NaN 0 0 7.2250 C\n",
"20 0 2 male 35.0 0 0 26.0000 S\n",
"21 1 2 male 34.0 0 0 13.0000 S\n",
"22 1 3 female 15.0 0 0 8.0292 Q\n",
"23 1 1 male 28.0 0 0 35.5000 S\n",
"24 0 3 female 8.0 3 1 21.0750 S\n",
"25 1 3 female 38.0 1 5 31.3875 S\n",
"26 0 3 male NaN 0 0 7.2250 C\n",
"27 0 1 male 19.0 3 2 263.0000 S\n",
"28 1 3 female NaN 0 0 7.8792 Q\n",
"29 0 3 male NaN 0 0 7.8958 S\n",
".. ... ... ... ... ... ... ... ...\n",
"861 0 2 male 21.0 1 0 11.5000 S\n",
"862 1 1 female 48.0 0 0 25.9292 S\n",
"863 0 3 female NaN 8 2 69.5500 S\n",
"864 0 2 male 24.0 0 0 13.0000 S\n",
"865 1 2 female 42.0 0 0 13.0000 S\n",
"866 1 2 female 27.0 1 0 13.8583 C\n",
"867 0 1 male 31.0 0 0 50.4958 S\n",
"868 0 3 male NaN 0 0 9.5000 S\n",
"869 1 3 male 4.0 1 1 11.1333 S\n",
"870 0 3 male 26.0 0 0 7.8958 S\n",
"871 1 1 female 47.0 1 1 52.5542 S\n",
"872 0 1 male 33.0 0 0 5.0000 S\n",
"873 0 3 male 47.0 0 0 9.0000 S\n",
"874 1 2 female 28.0 1 0 24.0000 C\n",
"875 1 3 female 15.0 0 0 7.2250 C\n",
"876 0 3 male 20.0 0 0 9.8458 S\n",
"877 0 3 male 19.0 0 0 7.8958 S\n",
"878 0 3 male NaN 0 0 7.8958 S\n",
"879 1 1 female 56.0 0 1 83.1583 C\n",
"880 1 2 female 25.0 0 1 26.0000 S\n",
"881 0 3 male 33.0 0 0 7.8958 S\n",
"882 0 3 female 22.0 0 0 10.5167 S\n",
"883 0 2 male 28.0 0 0 10.5000 S\n",
"884 0 3 male 25.0 0 0 7.0500 S\n",
"885 0 3 female 39.0 0 5 29.1250 Q\n",
"886 0 2 male 27.0 0 0 13.0000 S\n",
"887 1 1 female 19.0 0 0 30.0000 S\n",
"888 0 3 female NaN 1 2 23.4500 S\n",
"889 1 1 male 26.0 0 0 30.0000 C\n",
"890 0 3 male 32.0 0 0 7.7500 Q\n",
"\n",
"[891 rows x 8 columns]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic_df"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check for missing values. \n",
"\"\"\"\n",
"missing values are denoted by NaN\n",
"\"\"\"\n",
"#Check in entire DataFrame is any value is Missing \n",
"titanic_df.isnull().values.any()\n"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Survived 0\n",
"Pclass 0\n",
"Sex 0\n",
"Age 177\n",
"SibSp 0\n",
"Parch 0\n",
"Fare 0\n",
"Embarked 2\n",
"dtype: int64\n",
"--------------------------\n",
"PassengerId 0\n",
"Pclass 0\n",
"Sex 0\n",
"Age 86\n",
"SibSp 0\n",
"Parch 0\n",
"Fare 1\n",
"Embarked 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# How many missing values are there in each column\n",
"print(titanic_df.isnull().sum())\n",
"print(\"--------------------------\")\n",
"print(test_df.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"S 644\n",
"C 168\n",
"Q 77\n",
"Name: Embarked, dtype: int64"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check what is the most frequent value in Embarked\n",
"\n",
"titanic_df.Embarked.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11b6e1518>"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wOJgaP4qp8aP4/Wdnsv/EZbJzS93+HXZ0ujhwsoIDJyuIHhHMmu527+rwKCLyxSlgiYjI\nfZ0+X8XmrAJOn7/Ra2xOcjQb16UwffJoH1QmjyIkyJ+1aQmsTUug7Fpdd7v3Umrqe7R7r2nm1zmF\n/DqnsLvdezyLZo4nMEDt3kVEHoUCloiI9HLuYjWbs85xoqiq19iMxNFsejKFWVOifVCZfFlx4yL4\n5oYZfG39NA6fvUpOXilHC67R6bp7zfGi6xwvuk54SACrFsSRkRavZ+pERPpJAUtERO4oLL3J5qwC\njhZU9hqbNimKTU+mMDspWh3ohoAAfydLZk9gyewJVN1qYufhUrLzSqmsbrxzTX1TG7/77AK/++wC\nyXEjyUhP4Il5avcuIvIgClgiIkJx+S22ZBVw+Oy1XmNT40eyad005pkxClZDVPTIEF7NMHxlzVRO\nFVeRnVfCgZPu7d6Lym5RVHaLn350mqWzJ5CZnsD0yVH6mRARuYcClojIMHbxSg1bsgo4dPpqr7Ep\nsSPYtC6FBdPG6UX0MOF0OpgzdQxzpo6hrrGVPfnlZOeWcKni7j5nLa0d7DpSxq4jZUwcE0ZGWgKr\nF8YxKiLYh5WLiAwcClgiIsNQydVa3s6y7D95pdfYpPGRbHoyhfQZMQpWw1hEaCAblifyzLLJFJff\nIju3lH3Hymlsbr9zzeXrDfz8k7P8cts5Fk4fR2Z6AvPNWPz8nD6sXETEt7wWsIwxTuAHwBygBXjT\nWlvcY/yPgTeB692nvg0UPegeERH5csqu1fFOtuWzE5dxudzH4mMi2LguhcUzx+PUfkjSzeFwkBw3\niuS4Ufz+hhnsP3mFnLxSzly421myo9PFodNXOXT6KlGRwaxNiycjTe3eRWR48uYM1vNAsLV2sTFm\nEfB94Lke46nA1621+bdPGGNefMg9IiLyBVypquedbMveo+Vu3eIAYseGszEzhaVzJihYyQMFB/mz\nZmE8axbGU15Zx468UnYeKeNWXcuda6prm3l3RyHv7ihkdlI0GekJLJmldu8iMnx4M2AtA7YDWGsP\nGWMW3DOeCvypMSYG+MRa+9f9uKdP+fn5D79IRGQYqq5vZ9/pWk5cbOw1YxUV7s8TsyKYlRCKs/Mq\nx471fg5rqGps6XA7PnHiOKFBCgCPatZ4mP50NEVXmjla3EBRRbPbz9nJ4ipOFlfxjwEOZk8OZd6U\nMMaPCvRdwSIiHpSamtrneW8GrEigpsdxhzHG31p7e/H2O8A/ArXAVmPMM/24p0/3++ZERIaryupG\n3t1ZyI68y3TcM2U1LiqU1zIMq1Jjh+2zMrUNrfB+xZ3jOXPmEhmmF/5fVBqwCbhR08TOw2XsyCul\n4kbDnfHmNhd5hQ3kFTaQFDuCjPQEVsyLJTxE7d5FZOjxZsCqBSJ6HDtvByVjjAP4O2ttTffxJ8C8\nB90jIiIPV3WriXd3FpKTW0J7h3uwGjMqhFfXGtYsjMN/mAYr8a7RI0J4Ze1UXl6dzOkLVeTklrL/\n5BXa2u+2ey8ur6G4/CQ//egMS2ePJyM9gZmJo9VQRUSGDG8GrP3ABuDd7uepTvUYiwROG2OmAQ3A\nauBnQMgD7hERkfuorm3mvV1FbD94ye3FLMDoEcG8snYqGWnxBPhrGZx4n9PpYHbSGGYnjeHbL8xi\n79FysnNLuXDl7iKV1rYOdueXszu/nAnRYaxN63q2KypS7d5lYPnhByf5ZP9Fnl46me+8ONvX5cgg\n4M2AtRXIMMYcABzAG8aYjUC4tfYtY8yfAbvp6ha401r7aXfnQbd7vFifiMigd6uuhfd3F/Hp/ou0\n3hOsRkUE8ZU1U1m3KEENBsRnwkMDeXpZIk8vS6S4/BY5uSXsPVpOQ49271eqGvjFp+f41fYCFk4b\nR0ZaPAumjRu2S1hl4GhqaefTAxcB2HbgIr/39HRCgrTLkTyY135CrLWdwHfuOV3QY/yXwC/7cY+I\niNyjpr6FrXuK+Xj/RVpa3Rs2jAwP4qXVyaxfMokgBSsZQJJiR5IUO5I3Nszg4KkKsnNLOH3+brv3\nzk4XuWeuknvmKlGRQaxe0NXufcKYcB9WLcNZW3vnncYtna6u45Ag39YkA58iuIjIIFLX2NoVrD6/\nQNM9nfAiQgN5eXUSTy2ZTLDeYZUBLDjQn1WpcaxKjePK9Xpy8krZebiUm27t3lt4b1cR7+0qYuaU\n0WSmJ7Bk9gS9aSAiA57+AouIDAL1TW18tO88v913nsZm994/4SEBvLgqiaeXTiY0WF3ZZHCZMCac\n33t6Ol99MoX8gkqyc0s4fO4anT26X54+f4PT52/wow9OsmJ+LJnpCSTFjvRh1SIi96eAJSIygDU2\nt/G7zy6wde95Gpra3MbCgv157okknl2eSJjaXcsg5+fnJG1GDGkzYqiubWbn4VJy8kqpqLrb7r2h\nuZ1tBy6x7cAlEieMIDM9nifmxxIeqhb7IjJwKGCJiAxATS3tfPz5BbbuKaau0T1YhQT58+yKRJ5f\nMUUvLGVIiooM5itrbrd7v0FObgn7T1xxa+Ry4UoNP9x6ip/97gxLZk8gIz2emYnROJ1q9y4ivvXA\ngGWMWfGgcWvtPs+WIyIyvDW3tvPp/ku8v7uoazPcHoID/diwPJHnn0jSprgyLDgcDmZNiWbWlGi+\n9cJs9h0rJye3hOLyHu3e2zvZc7ScPUfLGT/6drv3OEaPCPFh5SIynD1sBusvuv85Gkiia2+rDmAJ\nXXtULfVeaSIiw0dLWwdZBy/xm11F3OrxoD9AYIAfTy+dzEurkhgRrvZVMjyFhwTw1JLJPLVkMhcu\n15CTW8Luo+VuS2crbjTwy23n2Lz9HKnTxpGRlsDC6eO0sbaIPFYPDFjW2lUAxphPgRettcXdxwnA\nj7xfnojI0NbW3kH2oRLe3VlEdW2z21iAv5P1Sybx8qpkRmnzVY8K8HficIDLBU5H17EMHokTR/Dt\nF2fzje527zm5JZwsrroz3umCw2evcfjsNUZGBLFmQRwZ6QlMVLt3EXkM+vsMVsLtcNWtFEjwQj0i\nIsNCW3snOw6X8m6OparGPVj5+zl5clECL69J1jInLwkJ8uepJZP5ZP9F1i+ZrI1DB6mgAD9Wzo9l\n5fxYKqoa2HG4lB15pW5vVnRtxl3M+7uLmZE4msz0eJbMnkBwoP6di4h3OFwu10MvMsb8AnAB7wJO\nYCNQZ639lnfLe7j8/HxXamqqr8sQEemX9o5Odh0p49c5lsqbTW5j/n4OMtIS+MqaqYwZpWAl8kV0\ndHRy1Ha3ez97jY7O3q9zQoP9WTEvlsz0eJJiR+JwqDGG9K22oZVN39t253jzX67XM7DSU5+/PPr7\n9s2bwL8BvkNX0NoB/MAzdYmIDH0dHZ3sPVbOO9mFVNxocBtzOh2sXRjPK2unMi4q1EcVigwNfn5O\nFk6PYeH0GG7WNbP7SBnZuSVcvn73v7vG5na2H7zE9oOXmDQ+ksz0BFamxhKhrpwi4gH9msECMMZM\nAmYAWUCctfaiF+vqN81gichA1tHp4rPjl3knu8DtBR50PfuzMjWO1zIM46PDfFShyNDncrk4e7Ga\n7NwSPj9xhda2jl7XBPg7WTxzPJnpCcxKUrt36aIZLHmILz6DZYx5FfgvQAhdHQQPGmP+xFr7K8/V\nJyIydHR2uth/8gpvZxdQdq3ebczhgCfmxfJaptFD9yKPgcPhYEbiaGYkjuZbz89i3/HL5OSWUFR2\n6841be2d7Dt+mX3HLzMuKpSMtHjWLIwneqSW64rIo+nvEsH/RFew2metrTTGzKNrmaAClohIDy6X\ni4OnKtiSVUDJ1bpe48vnTuS1jKnEx0T6oDoRCQsJYP3iSaxfPImLV2rIyStl95Ey6nu0e79W3civ\nthewJauA+SnjyEiLZ+H0GHWbFJF+6W/A6rDW1hljALDWVhhjOh9yj4jIsOFyuTh89hqbswq4cLmm\n1/jiWePZuC6FSeMVrEQGiskTRvCt52fxjaenc+h0BTm5pRwvun5nvNMFR85d48i5a4wID2T1gngy\n0uKJGxfhw6pFZKDrb8A6Y4z5IyDAGDMX+EPguPfKEhEZHFwuF/kFlWzOKqC4x3Kj29JnxPB6pmFK\n7EgfVCci/REY4MeKebGsmBfL1Rt3273f6LGFQk19K1v3FLN1TzHTJkWRmR7PsjkTCVaLfxG5R39/\nK/xrup7BagJ+BuwC/r23ihIRGehcLhfHC6+zOasAW3Kz1/iCaePYuM6QHDfKB9WJyBcVMzqMrz45\njdczUzjW3e4978xVt3bv5y5Vc+5SNW99eJoV8yaSmZ5AcpzavYtIl/4GrD8A/s5a+6feLEZEZDA4\nWXydzdsLOHuxutfYvKlj2PhkCikJUT6oTEQ8xc/pYMG0cSyYNo5bdS3sOlJGTl4J5ZV3m9Y0tbST\ndaiErEMlJMREdLd7j1OXOZFhrr8BayJwyBhj6Wps8YG1ttF7ZYmIDDxnLtxgS1YBJ4ureo3NTopm\n47oUZiSO9kFlIuJNIyOCeHFVEi+snELBpZtk55bw2YnLtLTebfdecrWOH//2NP/88VkWzYwhMz2B\nOclj1O5dZBjq9z5YAMaY5cCrQCaQa639mrcK6y/tgyUi3lZwqZrNWQUcL7zea2xG4mg2rUthVlK0\nDyoTEV9pbG7js+OXycktxZb2XiYMMHZUCGvTEli7MJ4xo9TufTDSPljyEF98HywAY4wDCAACgU6g\nxTN1iYgMTIWlN9mSVUB+QWWvMZMwiq8+mcKc5DF67kJkGAoNDmDdokmsWzSJkopasvNK2H2knLrG\n1jvXVN5sYktWAW9nFzDPjCUzLYG0GWr3LjLU9Xej4b8HngeOAZuBf2utbX7wXSIig9P58ltsybLk\nnb3aayw5biSbnkxhvhmrYCUiACSMj+QPnrvd7v0qObklHC+6zu1FQi4XHC2o5GhBJZFhgaxeEEdG\nWrz2wxMZovo7g1UIzLfW9l4fIyIyRFyqqGVLVgEHT1X0GkucOIJNT6awcNo4BSsR6VOAvx/L505k\n+dyJVFY3drV7P1zK9ZtNd66pbWjlw73n+XDveUzCKDLTE1g+dyIhavcuMmQ88L9mY8y3rLVvAVHA\nv7q90fBt1tq/9GJtIiKPRenVWt7Otnx+4kqvsUnjI9m4zrBo5ngFKxHpt7FRoWxcl8KrGYYThdfJ\nzish93QF7R13n323JTexJTf58YenWD63q927SRil3zUig9zD3i5x3OfjhzLGOIEfAHPoel7rTWtt\ncR/XvQVUW2u/2318FKjtHr5orX3jUb6uiEh/lVfW8U52IfuOl3Nvv5+4cRFsXGdYMmuCuoCJyBfm\n53QwP2Us81PGUlPfwu78crJzSyi7VnfnmubWDnLySsnJKyVuXASZ6fGsSo1jRHiQDysXkS/qgQHL\nWvuj7g9rgLettdce4XM/DwRbaxcbYxYB3wee63mBMebbwCxgb/dxMOCw1q58hK8jIvJIKqoaeCfH\nsie/jM57gtXEMWG8npnCsrkT8VOwEhEPGhEexPNPTOG5FYnY0ptkHyrhs+OXae7R7r3sWh0//egM\n//LJWdJnjO9q9z51jH4fiQwi3twHaxmwHcBae8gYs6DnoDFmCZAO/AhI6T49Bwg1xmR31/Zn1tpD\n/axRROSBrlU38uscy84jZXTek6zGjw7jtUzDE/Mm4uenDl8i4j0Oh4OUhChSEqL4g+dn8fnxy2Tn\nllBQcrfde3uHi/0nr7D/5BWiR4awdmE8a9PiGRcV6sPKRaQ/vLYPljHmJ8D71tpt3celQKK1tt0Y\nMx74OfAC8AqQYq39rjFmFrAI+AmQDGwDjLW2/X5fJz8/v//fgIgMSzUN7ew7U8ex8w29ZqxGhvmx\nYmYkcyaH6h1iEfGpypo2jp1v4MTFRhpbOvu8JjEmiPlTwkiJDcHfT7+zvK2xpYP//v7dxkf/8aXx\nhAb5+bAiGUhSU1Mf+z5YtUBEj2Nnj6D0FSAa+BSIoWvWqgB4Gyi21rqAQmPMDWA8UPagL6SNhkWk\nLzdqmvjNziKyDl2hvcP9xUr0yBBeXTuVNQvjtSeNiAwY61dDW3sneWeukp1XwjFb6faM6IWrLVy4\n2kJEaCCrFsSSmZZAwni1e/eW2oZW6BGw5syZq42G5aEeZR+s54DjdC0R7M8+WPuBDcC73c9gnbo9\nYK39v8A8sfZRAAAeW0lEQVT/7f7c36BrBuvnxph/RdczWX9ojJkARAK9+yWLiDzAzdpm3ttVxLaD\nl2hrdw9WUZHBvLJ2Kpnp8QT4611IERl4AvydLJ0zgaVzJlB5s5Gdh8vYkVdCZY9273WNrXy07wIf\n7buAiR9FRno8y+dOJDQ4wIeViwj0fwbrGpD6iPtgbQUyjDEH6OpA+IYxZiMQ3t36vS8/BX5ujPkc\ncAHffNDyQBGRnmrqW3hvVxGfHrhEa1uH29jIiCC+siaZJxdNIjBAwUpEBoexo0J5PdPw6tqpnCi6\nTk5eKQdPVbjNytvSm9jSm/z4t6dZPmciGenxTJsUpXbvIj7Sr2ewjDHnrLXTHkM9jyw/P9+lJYIi\nw1ttQytb9xTz8ecX3LpxAYwID+SlVcmsXzKJ4EBt5Ckig19tQyt78svIzi2h5Gpdn9fEjg0nIy2B\n1QviGBmhdu9fVG1DK5u+t+3O8ea/XK8lgtLTl3oG66wx5ntALnBnftpau88DhYmIfCH1ja18uPc8\nH312nqYW92AVERrAi6uSeXrpZEKCFKxEZOiIDAvk2RVT2LA8kaKyW2TnlrDv2GWaWu4u+imvrOef\nPz7DLz49S9qMGDLTE5hnxqqZj8hj0N9XHVHAqu7/3eYCVnu8IhGRh2hoauOjfef5cN95GpvdVxGH\nhQTwwsopbFiWqGcRRGRIczgcTI0fxdT4Ubz57Ew+P3GF7NwSzl2qvnNNR6eLg6cqOHiqgugRwazp\nbvceMzrMh5WLDG39CljW2lUPv0pExLsam9v4+POLbN1TTH1Tm9tYaLA/z6+YwrMrphAWomAlIsNL\ncJA/a9O6wlPZtTp25JWy60gZt+rvNn2uqmnm1zsK+fWOQuYkR5ORlsDiWeP1XKqIh/W3i+Buumas\n3FhrNYMlIl7X3NLOJ/sv8v7uYuoaW93GQoL82LB8Cs8/MYWIUK2LFxGJGxfBGxtm8LWnpnH47FWy\nc0s5WnDNbR/AE0VVnCiqIjwkgJWpsWSmJzB5wgjfFS0yhPR3ieB/7fFxAF0t22/2famIiGc0t7az\n/eAl3t9V7PYuLEBQoB/PLJ3MCyuTGBGuB7hFRO7l7+dk8awJLJ41gapbTew8XEpOXinXqhvvXFPf\n1LUy4OPPL5IUN5LM9ARWzJ2olQAiX0K/ugj2xRiTa61N93A9j0xdBEWGnta2DrYfusR7O4u4Wece\nrAL9nTy1dDIvrUpWZywRkUfU2eniVHEV2XklHDxV0WuvQIDAAD+WzZlAZnoC0ycP73bv6iIoD/HF\nuwgaY+Lv+UQzgNEeKEpE5I629g5y8kp5d0chN2rc9zIP8HeyfvEkXlqdTFRksI8qFBEZ3JxOB3Om\njmHO1DHUNbayJ7+c7NwSLlXU3rmmta2DXUfK2HWkjIljwu60ex+l370i/dLfJYJ7ufsMlguoAv6N\nVyoSkWGnvaOTnYdL+fWOQq7fbHIb8/dzkJmewCtrpzJ6RIiPKhQRGXoiQgPZsDyRZ5ZNprj8Fjm5\npew9Vu7WnfXy9QZ+/slZfrHtHGnTx5GRnkCqGYufn9OHlYsMbA8NWMaYZ4C11trzxpgXgN8HjgI5\n3i5ORIa2jo5OdueX8U5OodszAQB+Tgdr0+J5Ze1Uxo4K9VGFIiJDn8PhIDluFMlxo/jmszM4cPIK\n2bmlnLlw4841nZ0uDp2+yqHTV4mKDGbNwjgy0hIYH6127yL3emDAMsb8CfAq8HvGmNnAr4D/B5gO\n/E/g33m9QhEZcjo6Xew9Ws47OZaKqga3MafTwZoFcbyydqr2aRERecyCA/1ZvSCe1QviKa/save+\n80gZt3o8D1td28xvdhbxm51FzE6KJiMtnsWzJxCkdu8iwMNnsL4GLLbWNhpj/gb4yFr7E2OMAzjr\n/fJEZCjp6HSx/8RltmRZLl+vdxtzOmBlahyvZkxlQnS4jyoUEZHbYsdG8I1nZvDV9dM4cu4a2bkl\n5J9zb/d+sriKk8VVhG09xcr5Xe3eEyeq3bsMbw8LWC5r7e11O6uAHwBYa13GGK8WJiJDR2eni4On\nKtiSXUDp1Tq3MYcDls+dyGsZhrhxET6qUERE7sffz8mimeNZNHM8N2qa2Hm4jB15pVTcuLsCoaGp\njU/2X+ST/ReZEjuiq937vFjC1e5dhqGHBax2Y8xIIByYB2QDGGMSgPYH3Sgi4nJ1rdnfklXg1qHq\ntqVzJvB6piEhJtIH1YmIyKMaPSKEV9ZO5eXVyZy5cIPs3BIOnLxCa4927+fLa/in8pP89LenWdLd\n7n1m4uhh3e5dhpeHBay/AY53X/cTa22FMeYV4L8Bf+Ht4kRkcHK5XBw+d40tWQWcL6/pNb541nhe\nzzRMnqBlJCIig5HT6WBWUjSzkqL59guz2Hu0nOy8Ui5cvvs7v7W9kz355ezJL2d8dBgZafGsWRiv\nrTZkyHvoRsPGmAlAtLX2ZPfxU0CjtXaP98t7OG00LDJwuFwujtnrbM46R2HprV7jadNjeH2dISl2\npA+qExERb+tq917C3qPlNDT3XuzkdDpYkDKOzPR4FkwbN+DbvWujYXmIPqdlHxqwBjoFLBHfc7lc\nnCyqYnNWAecuVfcan58ylk3rUpgaP8oH1YmIyOPW0tbBgZNXyMkt5dT5qj6vGRURxJqF8WSkxTNh\nzMBsbqSAJQ/RZ8Dq70bDIiJ9OnW+is3bC9z2S7ltbvIYNq5LYdrkKB9UJiIivhIU4Meq1DhWpcZx\npaq+q9374VKqa++2e79Z18J7u4p4b1cRM6eMJiMtgSWzxxMcqJenMrjpJ1hEvpCzF2+weXsBJ4t7\nvzM5c8poNq1LYeaUaB9UJiIiA8mE6HC+/tR0Nq1LIb+gkuzcEg6fu0Znj37vp8/f4PT5G7y11Z8V\n3e3etZxcBisFLBF5JLakmi1ZlqO2stfYtElRfHV9CrOTxvigMhERGcj8/JykzYghbUYM1bXN7DpS\nRnZuiduG8w3N7Ww7cIltBy6ROGEEGenxrJwfS3ioluXJ4KGAJSL9Ulx2i81ZBRw5d63XmIkfxcYn\nU5g3dYza8IqIyENFRQbz8upkXlqVdKfd+/6TFbS2ddy55sKVGn609RQ/+90ZlsyaQOaieGYmRuN0\n6u+MDGwKWCLyQBcu17Alq4DcM1d7jSXFjmDTk9NITRmrYCUiIo/M4XAwc0o0M6dE860X2th3rJyc\n3BKKe2zx0dbeyd5j5ew9Vk7M6FAy0hJYszCO0SNCfFi5yP0pYIlIn0oqatmSXcCBkxW9xiZPiGTT\nuhTSZsQoWImIiEeEhwTw1JLJPLVkMhcu15CTW8Luo+U0NLXduebqjUZ+ue0cm7efI3XaODLSElg4\nfRz+A7zduwwvClgi4qbsWh1vZ1s+P3GZe3dxSIiJYOO6FBbNHK8lGiIi4jWJE0fw7Rdn840NMzh4\nqoKc3BK3pkqdLjh89hqHz15jZEQQaxbEkZGewMQB2u5dhhcFLBEB4Mr1et7Otuw9Vt4rWMWNC+f1\nzBSWzp6gYCUiIo9NUIAfK+fHsnJ+LBVVDew4XMqOvFKqa5vvXHOrroX3dxfz/u5iZiSOJiMtnqWz\nJxAcpJe54hte+8kzxjiBHwBzgBbgTWttcR/XvQVUW2u/2997RMRzrt5o4J0cy+78creWuQATosN4\nPdOwfF4sfgpWIiLiQ+Ojw/ja+mlszDQctZXk5JWSd+YqHT3+dp25cIMzF27wo62neGJ+LBlp8STH\njdRydnmsvBntnweCrbWLjTGLgO8Dz/W8wBjzbWAWsLe/94iIZ1RWN/LrHYXsPFzq9scJIGZ0KK9l\nGFbOj8VP69pFRGQA8fNzsnB6DAunx3Czrpnd3e3eL1+/2+69qaWd7Qcvsf3gJSaNjyQjPZ5VqXFE\nqN27PAbeDFjLgO0A1tpDxpgFPQeNMUuAdOBHQEp/7hGRL6/qVhPv7iwkJ7eE9g73YDV2VAivZhhW\nL4jTA8MiIjLgjYoI5sVVybywMomzF6vJySvh8xNXaGm92+79UkUtP/7wND//+CyLZ44nIz2e2Ulj\ntORdvMabASsSqOlx3GGM8bfWthtjxgN/DrwAvNKfex70hfLz8z1Vs8iQVdvYwedn68gvrqej030s\nMtSPFTMimJsYhr9fFSeOV/X9SURERAaw5cmwcNI4Tpc0crS4gSvVdzsQtrV3su/4ZfYdv8zIMD/m\nJYYxd0ooI0Lv/3K4saXD7fjEieOEBvl5rX4ZXFJTU/s8782AVQtE9Dh29ghKXwGigU+BGCDUGFPw\nkHvu637fnIjAzbpm3t9VzLYDF2ltd09WUZFBfGXNVDLTEwgM0B8MEREZGpYu6vrnxSs15OSVsie/\njLrGu2HrVkMHu0/Vsvd0LfPMWDLTE1g4PYYA/7urN4rLbrF7r3srgKiYKcxIHP1YvgcZvByue9uF\neYgx5iVgg7X2G93PU/25tXZ9H9d9A0jpbnLRr3t6ys/PdylgifRWU9/CB7uL+Xj/RVrb3N+BGxke\nxMtrknly8SSCFKxERGSIa23r4NDpCnJySzledL3Pa0aEB7J6QTwZafEcOHWFX20r6PO6bz0/iw3L\nE71Zrgwefa4z9eYM1lYgwxhzoPuLv2GM2QiEW2vf6u89XqxPZEiqbWjlw73F/O6zCzS3ugeryLBA\nXlqVzFNLJql9rYiIDBuBAX6smBfLinmxXL3R1e59Z14pVTV3273X1LeydU8xW/c8uIH1Wx+eYuLY\ncOabsd4uWwYpr81gPS6awRLpUt/Uxm/3nue3+87T1OK+sjYiNIAXVibxzLJEQhSsRERE6Oh0ccxW\nkpNXQu7pq7066j7I3OQx/NV3lnixOhkkHvsMlog8Bo3NbXz02QU+3FNMQ7N7sAoL9uf5lUk8uzyR\n0OAAH1UoIiIy8Pg5HSyYNo4F08Zxq66F3fllbDt4iYqqhofee7zoOs0t7VoNIn3ST4XIINXU0s7H\nn19g655itwd3AUKC/HluxRSee2IK4SEKViIiIg8yMiKIF1YmkTZ9HN/52139uqe5tUMBS/qknwqR\nQaa5tZ1P91/k/d3F1Da0uo0FB/qxYXkiL6xM0maKIiIij2j0yBACA/x6NYe6V0RoABFh+jsrfVPA\nEhkkWto62H7wEu/tKuJWXYvbWGCAH88sncyLq5IYER7kmwJFREQGueBAf1bOjyU7t+SB161ZGI+f\nNiqW+1DAEhng2to7yDpUwm92FlJde0+w8neyfslkXlqdxKiIYB9VKCIiMnRsXGc4cu4a1bXNfY6P\niwrl5dXJj7kqGUwUsEQGqLb2TnbklfDujkK3NrIA/n5OnlycwMurkxk9IsRHFYqIiAw9o0eE8Ld/\ntIy/f/c4J4ur3MbmJEfzx6/P12oReSC1aZch5YcfnOST/Rd5eulkvvPibF+X84W0d3Sy83AZ7+6w\nVN5schvz93OQkZ7AK2umEj1SwUpERMSbzl2q5j/+/Wd3jjf/5Xoi9eyV3KU27TK0NbW08+mBiwBs\nO3CR33t6+qDa86mjo5M9R8t5J8dy9Uaj25if08HatHheWTOVsVGhPqpQRERkeJk4JtzXJcggNHhe\nfYo8RFt7J7cnZDtdXcchg2AGv6PTxWfHynk723Llnr03nA5YtSCO1zIMMaPDfFShiIiIiPSXApaI\nj3R2uth/4gpbsgsor6x3G3M44In5sbyWYfTumYiIiMggooAl8ph1dro4eLqCt7MKKLla5zbmcMDy\nORN5LdMQNy7CRxWKiIiIyBelgCXymLhcLvLOXGVLluXClZpe40tmj2djZgoJ4yN9UJ2IiIiIeIIC\nloiXuVwu8gsq2bz9HMXlvYNV+owYNq5LIXHiCB9UJyIiIiKepIAl4iUul4tjhdfZsr0AW3qz1/iC\naePYtC6FpLiRPqhORERERLxBAUvEC04UXWfz9gLOXaruNTbfjGXjOoNJiPJBZSIiIiLiTQpYIh50\n+nwVW7Isp85X9RqbkxzNxnUpTJ882geViYiIiMjjoIAl4gEFl6rZvL2A40XXe43NSBzNpidTmDUl\n2geViYiIiMjjpIAl8iUUlt5kc1YBRwsqe42lJIziq09OY3ZyNA6HwwfViYiIiMjjpoAl8gWcL7/F\n5qwCDp+91mtsavxINq2bxjwzRsFKREREZJhRwBJ5BBev1PB2tuXgqYpeY1NiR7BpXQoLpo1TsBIR\nEREZphSwRPqh5Gotb2db9p+40mts0vhINq5LYdHMGAUrERERkWFOAUvkAcor63g72/LZ8cu4XO5j\nceMi2LQuhcWzxuN0KliJiIiIiAKWSJ+uVNXzTrZl79FyOu8JVhPHhLNxnWHpnIn4KViJiIiISA8K\nWCI9XL3RwLs7Ctl5pIzOe5LV+OgwXs80rJgXq2AlIiIiIn3yWsAyxjiBHwBzgBbgTWttcY/xl4Dv\nAi5gs7X2/3SfPwrUdl920Vr7hrdqFLmt8mYj7+4oZEdeKR33BKuxUaG8njGVValx+Pk5fVShiIiI\niAwG3pzBeh4IttYuNsYsAr4PPAdgjPED/gZYANQDZ40xm7s/dlhrV3qxLpE7btQ08e6OQrJzS2jv\ncA9W0SNDeC1jKmsWxuOvYCUiIiIi/eDNgLUM2A5grT1kjFlwe8Ba22GMmWatbTfGjAX8gFa6ZrtC\njTHZ3bX9mbX20MO+UH5+vle+ARlcGls63I5PnDhOaJBfn9fWNXXw+Zk6jhTX09HpPhYR4seKmRHM\nSwzD3+8GJ47f8FbJIiIiMoA9ymsLGX5SU1P7PO/NgBUJ1PQ47jDG+Ftr2wG6w9WLwD8CnwANQCPw\nP4GfAMnANmOMuX3P/dzvm5PhpbahFd6/uz/VnDlziQwLdLvmVl0L7+8u4tP9F2ltd09WoyKC+Mqa\nqaxblEBggH55ioiIDHf9eW0hci9vBqxaIKLHsfPeoGSt/cAY8yHwc+DrwBag2FrrAgqNMTeA8UCZ\nF+uUIaClrYO9R91/TA6dqmD1wjj8/ZzU1LewdU8xH++/SEur+7tRI8IDeXl1MuuXTCZIwUpERERE\nvgRvBqz9wAbg3e5nsE7dHjDGRAK/AzKttS3GmAagE/gmMAv4Q2PMBLpmwSp6fWaRHq7fbOJ7bx2g\nvLLe7fzf/+Y4nx64wMwpY8jOvUTTPdP8EaGBvLQqiaeXTiY4SA01RUREROTL8+aryq1AhjHmAOAA\n3jDGbATCrbVvdTe12GeMaQNOAr+i61msnxtjPqeru+A3H7Y8UIa3jk4Xf/WzQ73C1W3nL9dy/nKt\n27nwkABeWJnEM8smExoc8DjKFBEREZFhwmsBy1rbCXznntMFPcbfAt66Z7wD2OitmmToyS+4xsUr\ntQ+/EAgN9uf5J5J4dnkiYSEKViIiIiLieVoXJYPaoVP9W0E6bXIU3/tmOuGhejBVRERERLxHm/vI\noNbU0r8VpFMmjlC4EhERERGvU8CSQW18dJhHrxMRERER+TIUsGRQW7sw/qHXBPg7eWJe7GOoRkRE\nRESGOwUsGdQmjAnn+SemPPCarz6ZwojwoMdUkYiIiIgMZ2pyIYPeG8/MIDTInw/2FNHc2nnnfFiw\nP5uenMYzyyb7sDoRERERGU40gyWDntPp4PV1KfzDf1jtdv4f/sNqNixPxOFw+KgyERERERluFLBk\nyAgJct/bKjDAz0eViIiIiMhwpYAlIiIiIiLiIQpYIiIiIiIiHqKAJSIiIiIi4iEKWCIiIiIiIh6i\ngCUiIiIiIuIhClgiIiIiIiIeooAlIiIiIiLiIQpYIiIiIiIiHqKAJSIiIiIi4iEKWCIiIiIiIh6i\ngCUiIiIiIuIhClgiIiIiIiIeooAlIiIiIiLiIQpYIiIiIiIiHqKAJSIiIiIi4iH+3vrExhgn8ANg\nDtACvGmtLe4x/hLwXcAFbLbW/p+H3SMiIiIiIjKQeXMG63kg2Fq7mK4g9f3bA8YYP+BvgLXAYuAP\njTHRD7pHRERERERkoPNmwFoGbAew1h4CFtwesNZ2ANOstTXAaMAPaH3QPSIPE+DvxOHo+tjp6DoW\nEREREXmcvLZEEIgEanocdxhj/K217QDW2nZjzIvAPwKfAA0Pu+d+8vPzPVu5DFoLksI4XNRAalIY\nZ0+f8HU5IiIiMog1tnS4HZ84cZzQID8fVSMDTWpqap/nvRmwaoGIHsfOe4OStfYDY8yHwM+Br/fn\nnr7c75uT4Uc/CiIiIuIptQ2t8H7FneM5c+YSGRbow4pkMPDmGqr9wFMAxphFwKnbA8aYSGPMXmNM\nkLW2k67Zq84H3SMiIiIiIjLQeXMGayuQYYw5ADiAN4wxG4Fwa+1bxpjNwD5jTBtwEvgVXR0F3e7x\nYn0iIiIiIiIe5bWA1T0z9Z17Thf0GH8LeKuPW++9R0REREREZFBQmzUREREREREPUcASERERERHx\nEAUsERERERERD1HAEhERERER8RAFLBERERGRPgT4O3E4uj52OrqORR5GPyUiIiIiIn0ICfLnqSWT\nAVi/ZDIhQd7c4UiGCofL5fJ1DV9Kfn6+KzU11ddliIiIiIjI8OLo66RmsERERERERDxEAUtERERE\nRMRDFLBEREREREQ8RAFLRERERETEQxSwREREREREPEQBS0RERERExEMUsERERERERDxEAUtERERE\nRMRDhsR21Pn5+b4uQUREREREhhdXampqr82GHS6XyxfFiIiIiIiIDDlaIigiIiIiIuIhClgiIiIi\nIiIeooAlIiIiIiLiIQpYIiIiIiIiHqKAJSIiIiIi4iEKWCIiIiIiIh4yJPbBErnNGPNdYC0QAHQC\nf2Kt1UZpIuLGGDMD+O9AKBAOfAr8V2ut9i4RkTuMManAX9P1u8IJ7Ab+wlrb6tPCZEDTDJYMGcaY\n6cCzQIa19gngj4Gf+bYqERlojDEjgXeAf2etXQUsAmYB3/ZpYSIyoBhjYoFfAX9krV0GLAVagP/t\n08JkwFPAkqGkBogHvmmMmWitPQ6k+bgmERl4ngN2WWuLAKy1HcDX0RsyIuLua8BPrLWFAN0z3H8F\nPGWMCfFpZTKgKWDJkGGtvUzXDNZS4KAxpgB4xrdVicgANAG40POEtbZeS35E5B6T6P27wgVcA2J8\nUZAMDgpYMmQYY5KAWmvtN6218cBXgR8aY6J8XJqIDCwlQFzPE8aYycaYFT6qR0QGplIgsecJY4yT\nrtUylT6pSAYFBSwZSmYD/2CMCew+LgRuAR2+K0lEBqCPgSeNMVMAjDEBwP8CZvq0KhEZaH4BvGmM\nSTbGjDTGZAM/AT621jb4uDYZwBwulxomydBhjPnPwCtAPV1vIPyttfZD31YlIgNNd2ew/0HX74kI\n4Hd0dQbTH0URuaP7d8V/o6vbaChwla4lgv+vtbbal7XJwKWAJSIiIiLST8aY2cAFa229r2uRgUkB\nS0RERERExEP0DJaIiIiIiIiHKGCJiIiIiIh4iAKWiIiIiIiIhyhgiYiIiIiIeIi/rwsQERHpy//f\n3v28WFXGcRx/T4FBTWIgRjMGFcRHKGWSFpNgJNofYBvJfs3GFkqiYCHSwmhR9GtnLUQQigQVcSEI\nEXIVkRapgwvhiys3pQkhohRpTYtzhOvkgA1XvDO+X3DhPud+n3OeczeXz32ec06Sp2ieZ3d20kc7\nq2rHHfTvANurqjPN4+8GOlW1exp9x4BXqmpsOseWJM1cBixJUj/7papG7vUgJEm6UwYsSdKMk+QC\nzcOBlwO/Al8DG4GFwFhVHW1L303yFTAAbK6qTpJhYBcwD3gC2FNVW9tZp3eA+e2+bx7rYeCHtm5H\nkreBTTTL7E8CG6rqzyRvAR8CV4DzNA88lyTdZ7wGS5LUz4aSjE96LQYeBw5V1aK2bnVVLQe204Sf\nm65W1VKa4PRtkoeA12nC0iiwBFifZH5bvxB4oaq2te05wAFgfxuungPWAcvambXfgC1JhoDPgJeB\nl4BH78aXIUnqf85gSZL62W2XCCYBONw2zwPHu94/1lW6C6CqziS5BCyqqi+SrEiyBXieJkQ90taf\nqqobXf0/Bv4BXmvbK4BngZ/aMcwBTgHLgBNVdbEd33fAyumetCRp5jJgSZJmpKr6q6t5Y4qy7u0D\nwPUkXwLPAN8DB4FV7WcAf0zqvwcYBD4C3gceBPZW1UaAJIM0v6UruXVVyFTjkSTNci4RlCTNZm8A\nJHkRmAucA14FPq+qfcCTwDBNcLqdceAD4M0kI0AHWJ1kQZIB4BuaJYnHgdEkw0keANbcvVOSJPUz\nZ7AkSf1sKMn4pG3H/kf/wSSngb+BtVV1PcknNNdjXQYuAj8DT0+1g6r6PclWYCcwSjObdYTmT8rT\nwKftTS7eA34ErvHfW8tLku4TAxMTE/d6DJIkSZI0K7hEUJIkSZJ6xIAlSZIkST1iwJIkSZKkHjFg\nSZIkSVKPGLAkSZIkqUcMWJIkSZLUIwYsSZIkSeqRfwEuErDn9AI1CgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b6e11d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Embarked\n",
"# Handling the missing values in Embarked, we can use the most frequent value as fill value\n",
"\n",
"titanic_df.Embarked = titanic_df.Embarked.fillna(\"S\")\n",
"\n",
"# How likely someone is survival based on where they are embarked\n",
"sns.factorplot(x = 'Embarked', y = 'Survived', data = titanic_df, size=4, aspect=3)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Embarked Survived\n",
"0 C 0.553571\n",
"1 Q 0.389610\n",
"2 S 0.339009\n",
"Embarked doesn't seem to be a major survival predictor\n"
]
},
{
"data": {
"image/png": 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RJ0mSJEkVYpEnSZIkSRVikSdJkiRJFdLMKRQkSZI0SBExDLgI2AJYBBySmXNXsN+lwN8z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\nt6xb10sxjYIkNVz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"text/plain": [
"<matplotlib.figure.Figure at 0x116b545f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (axis1, axis2, axis3) = plt.subplots(1, 3, figsize=(15, 5))\n",
"sns.countplot(x = 'Embarked', data = titanic_df, ax = axis1)\n",
"sns.countplot(x = 'Survived', hue='Embarked', data = titanic_df, ax = axis2)\n",
"\n",
"embark_perc = titanic_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean()\n",
"print(embark_perc)\n",
"print('Embarked doesn\\'t seem to be a major survival predictor')\n",
"sns.barplot(x='Embarked', y = 'Survived', data = embark_perc, order = ['S', 'C', 'Q'], ax = axis3)\n",
"# Dropping Embarked columns\n",
"titanic_df = titanic_df.drop(['Embarked'], axis = 1)\n",
"test_df = test_df.drop(['Embarked'], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Fare\n"
]
},
{
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},
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"text/plain": [
"<matplotlib.figure.Figure at 0x11be697f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11c10ffd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Fare\n",
"\n",
"#fill missing values with median in test data\n",
"test_df.Fare.fillna(test_df.Fare.median(), inplace=True)\n",
"# Float to int\n",
"titanic_df.Fare = titanic_df.Fare.astype(int)\n",
"test_df.Fare = test_df.Fare.astype(int)\n",
"\n",
"fare_not_survived = titanic_df['Fare'][titanic_df['Survived'] == 0]\n",
"fare_survived = titanic_df['Fare'][titanic_df['Survived'] == 1]\n",
"\n",
"#Average fare of person who didn't survived vs the one who survived \n",
"average_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
"print(\"Average Fare\")\n",
"display(average_fare)\n",
"\n",
"std_fare = DataFrame([fare_not_survived.std(), fare_survived.std()])\n",
"print(\"Standard deviation in Fare. Highly varying\")\n",
"display(std_fare)\n",
"\n",
"titanic_df['Fare'].plot(kind='hist', figsize=(15, 3), bins = 100, xlim=(0, 100))\n",
"average_fare.index.names = std_fare.index.names = ['Survived']\n",
"average_fare.plot(yerr = std_fare, kind='bar', legend = False)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rjangid/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:21: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/Users/rjangid/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:22: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11c25cc88>"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11bf81fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Age\n",
"\n",
"fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(15,4))\n",
"axis1.set_title('Original Age values - Titanic')\n",
"axis2.set_title('New age values - Titanic')\n",
"\n",
"average_age_titanic = titanic_df['Age'].mean()\n",
"std_age_titanic = titanic_df['Age'].std()\n",
"count_nan_age_titanic = titanic_df['Age'].isnull().sum()\n",
"\n",
"average_age_test = test_df['Age'].mean()\n",
"std_age_test = test_df['Age'].std()\n",
"count_nan_age_test = test_df['Age'].isnull().sum()\n",
"\n",
"# Generate random numbers between (mean - std) and (mean + std)\n",
"rand_1 = np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic, size = count_nan_age_titanic)\n",
"rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)\n",
"\n",
"titanic_df['Age'].dropna().astype(int).hist(bins=70, ax = axis1)\n",
"\n",
"titanic_df['Age'][np.isnan(titanic_df['Age'])] = rand_1\n",
"test_df['Age'][np.isnan(test_df['Age'])] = rand_2\n",
"\n",
"titanic_df['Age'] = titanic_df['Age'].astype(int)\n",
"test_df['Age'] = test_df['Age'].astype(int)\n",
"\n",
"titanic_df['Age'].hist(bins=70, ax = axis2)\n"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rjangid/anaconda/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119181be0>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Q4k4iSaIQM+BCRQcvvqUJ8HXn4TXJHzqJszlsHG05zMHmfdgcNuJ9EskLy8fT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116b6d828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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G5/xvyupmMO5MuvdrXszGx017A+9h9n3MtrgdMvMHHfsKs8XNtu3e6DiNEbZP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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa5e978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# peaks for survived/not survived by their age\n",
"facet = sns.FacetGrid(titanic_df, hue = 'Survived', aspect = 4)\n",
"facet.map(sns.kdeplot, 'Age', shade=True)\n",
"facet.set(xlim=(0, titanic_df['Age'].max()))\n",
"facet.add_legend()\n",
"\n",
"# Average survived passengers by age\n",
"fig, axis1 = plt.subplots(1,1, figsize=(18,4))\n",
"average_age = titanic_df[[\"Age\", 'Survived']].groupby(['Age'], as_index=False).mean()\n",
"sns.barplot(x='Age', y='Survived', data=average_age)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rjangid/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:141: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" self._setitem_with_indexer(indexer, value)\n"
]
}
],
"source": [
"# Family\n",
"# Combine two columns to make family\n",
"\n",
"titanic_df['Family'] = titanic_df['Parch'] + titanic_df['SibSp']\n",
"titanic_df['Family'].loc[titanic_df['Family'] > 0] =1\n",
"titanic_df['Family'].loc[titanic_df['Family'] ==0] =0\n",
"\n",
"test_df['Family'] = test_df['Parch'] + test_df['SibSp']\n",
"test_df['Family'].loc[test_df['Family'] > 0] =1\n",
"test_df['Family'].loc[test_df['Family'] ==0] =0\n",
"\n",
"#drop Parch and SibSp\n",
"\n",
"titanic_df = titanic_df.drop(['SibSp', 'Parch'], axis=1)\n",
"test_df = test_df.drop(['SibSp', 'Parch'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Family</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0.303538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.505650</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Family Survived\n",
"0 0 0.303538\n",
"1 1 0.505650"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0x11b5e9e80>, <matplotlib.text.Text at 0x11b5ee198>]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Rn6OavmIIeHHPqpKkLsrM24Hnjtj9JeDTmbm2QEmSBHTeI3YI8AiwM9VUFr+kGmchSRNe\nRHwoIua27qsH66+tj+8YER8pU52kJhtLj9iLMvMR4Pb6KcrvA0t6Vpkkdc9y4GsR8QBwPdW4sA1U\nXy4PAJ4KnFKuPElN1WkQ25Y/nkn/UTZfBFySJqTMvA2YHxH7A38LHApsAn4MfDYzry1Zn6Tm6jSI\nXQFcGxHL6+0jgK/1piRJ6o3MvA64rnQdkjSs05n13xMRrwL2Ax4DFtcTIUrSpBERBwNnAjtSPXgE\nQGY+q1hRkhqt0x4xMvMyqke/JWmy+jTwTuBOHF4haQLoOIhJ0hTwq8y8qnQRkjTMICapSW6IiHOA\na4B1wzsz8/pyJUlqMoOYpCZ5Uf37C1v2DVFNYSFJfWcQk9QYmbl/6RokqZVBTFJjRMQ+VGtLzqJ6\nanIbYOfM3KVkXZKaq9MljiRpKjifal7EGcC5wD8DlxetSFKjGcQkNckfMvMCYCXwW+B4qvkRJakI\ng5ikJlkXETsCCeyVmUPAkwrXJKnBDGKSmuQc4FLgSuDYiPgn4NayJUlqMoOYpMbIzC8DB2Xmw8AA\ncDRwTNmqJDWZQUxSY0TEk4ElEXEtMBN4KzC3bFWSmswgJqlJlgI/AP4MeBj4OXBx0YokNZpBTFKT\nPDMzlwCbMvPRzDwd+K+li5LUXAYxSU2yISLmUi1rRETsCmwqW5KkJnNmfUlN8n6qOcSeHhFXAHsD\nb2r3gYiYDpwHzAPWAwszc03L8dcDpwAbgDuAt2Sm4U5SR+wRk9Qkg1Qz6f8r8Azgq1RPT7ZzODAz\nM/cGTgXOHj4QEX8CnAnsn5kvoRr4f2gP6pY0RRnEJDXJN4GdgauArwG/oFpzsp19gGsAMvNmYI+W\nY+uBF2fmI/X2DGBdNwuWNLV5a1JSo2Tmm8f4kTnA2pbtjRExIzM31Lcg/x0gIt5KtZj4itFOODg4\nOMYSNBF5HZup29fdICapSa6IiIXAtVRjugDIzJ+2+cxDwOyW7emZ+fhn6zFkHwWeDRxZL5vU1sDA\naHdDR7j0nrG9X30x5us4Hrdf2PufoTEZz3VvF94MYpKaZC7VOK9ftewbAp7V5jOrgcOA5RGxF9WA\n/FafpbpFebiD9CWNVU+CWERsC3we2AXYnmow613AMqpG707g5MzcFBHHAydSfTs9MzOv6kVNkgQc\nCeyUmX8Yw2cuBw6MiJuoxpMdFxFHUd2GvBV4M3ADcG1EAHwqMy/vbtmSpqpe9YgdDfw6M4+JiB2B\nf6x/LcrMlRHxGWBBRHwPeBvV4NeZwI0RsSIz1/eoLknN9i/Ak4GOg1jdy3XSiN2t9wp96EnSuPUq\niH0ZuKx+PY2qt2sAWFXvuxo4CNgIrK6D1/qIWAPsTrUEiSR12xBwV0TcCTw6vDMzDyhXkqQm60kQ\ny8zfAUTEbKpAtgj4eMsg1oepxmqMfBppeP+ofFpl8vMaNlfBa39WqR8sSVvSs8H6EfF0qrEV52Xm\nFyPioy2HZwMPsvnTSMP7R+VTR5NfX544Ap86moC6/dRRpzJz1ejvkqT+6cnYhoj4C+DbwHsy8/P1\n7tsiYn79+hCqwa23APtGxMx6/bfdqAbyS5IkTXm96hF7L9WA2PdFxPvqfW8HFkfEdsDdwGWZuTEi\nFlOFsunA6ZnprNSSJKkRejVG7O1UwWuk/bbw3qXA0l7UIUmSNJH52LUkSVIhBjFJkqRCDGKSJEmF\nGMQkSZIKMYhJkiQVYhCTJEkqxCAmSZJUiEFMkiSpEIOYJElSIQYxSZKkQgxikiRJhRjEJEmSCjGI\nSZIkFWIQkyRJKsQgJkmSVIhBTJIkqRCDmCRJUiEGMUmSpEIMYpIkSYUYxCRJkgoxiEmSJBViEJMk\nSSrEICZJklSIQUySJKkQg5gkSVIhBjFJkqRCDGKSJEmFGMQkSZIKMYhJkiQVYhCTJEkqxCAmSZJU\niEFMkiSpEIOYJElSIQYxSZKkQgxikiRJhRjEJEmSCjGISZIkFTKjdAGSNJFFxHTgPGAesB5YmJlr\nRrxnB2AF8ObMvKf/VUqarOwRk6T2DgdmZubewKnA2a0HI2IP4HrgrwrUJmmSM4hJUnv7ANcAZObN\nwB4jjm8PvBKwJ0zSmPX01mRE7Al8JDPnR8RfA8uAIeBO4OTM3BQRxwMnAhuAMzPzql7WJEljNAdY\n27K9MSJmZOYGgMxcDRARJWqTNMn1LIhFxLuBY4Df17vOARZl5sqI+AywICK+B7yN6hvmTODGiFiR\nmet7VZckjdFDwOyW7enDIWy8BgcHt64iTQhex2bq9nXvZY/Yj4EjgIvq7QFgVf36auAgYCOwug5e\n6yNiDbA78IMe1iVJY7EaOAxYHhF7AXds7QkHBgbG9oFLves5EY35Oo7H7Rf2/mdoTMZz3duFt56N\nEcvMrwCPteyalplD9euHgbls3uU/vF+SJorLgXURcRPwCeAdEXFURJxQuC5JU0A/p6/Y1PJ6NvAg\nm3f5D+8flV3Ck5/XsLkm07XPzE3ASSN2b9ZFlZnz+1KQpCmln0HstoiYn5krgUOA64BbgLMiYibV\nk0e7UQ3kH5Vd+5NfX7r1wa79CajbXfuSNFn1M4i9C1gaEdsBdwOXZebGiFgM3EB1m/T0zFzXx5ok\nSZKK6WkQy8z7gL3q1/cC+23hPUuBpb2sQ5IkaSJyQldJkqRCDGKSJEmFGMQkSZIKMYhJkiQVYhCT\nJEkqxCAmSZJUiEFMkiSpEIOYJElSIQYxSZKkQgxikiRJhRjEJEmSCjGISZIkFWIQkyRJKsQgJkmS\nVIhBTJIkqRCDmCRJUiEGMUmSpEIMYpIkSYUYxCRJkgoxiEmSJBViEJMkSSrEICZJklSIQUySJKkQ\ng5gkSVIhBjFJkqRCDGKSJEmFGMQkSZIKMYhJkiQVYhCTJEkqxCAmSZJUiEFMkiSpEIOYJElSIQYx\nSZKkQgxikiRJhRjEJEmSCjGISZIkFWIQkyRJKsQgJkmSVIhBTJIkqRCDmCRJUiEzShcAEBHTgfOA\necB6YGFmrilblSSN3j5FxGHA/wE2AJ/PzKVFCpU0KU2UHrHDgZmZuTdwKnB24XokadgTtk8RsS3w\nCeAgYD/ghIj4iyJVSpqUJkoQ2we4BiAzbwb2KFuOJD2uXfu0G7AmM3+bmY8CNwIv7X+JkiariRLE\n5gBrW7Y3RsSEuG0qqfHatU8jjz0MzO1XYZImv4kSdh4CZrdsT8/MDe0+MDg4OKYf8K7XPmccZamX\nxnoNx+utux/bl5+jzvXr2ndJu/Zp5LHZwIOjndD2a2rox99j26+Jp9vXfaIEsdXAYcDyiNgLuKPd\nmwcGBqb1pSpJat8+3Q3sGhE7Ar+jui358XYns/2S1Gra0NBQ6Rpan0raHZgGHJeZ95StSpK23D4B\n/w2YlZlLWp6anE711OS5xYqVNOlMiCAmSZLURBNlsL4kSVLjGMQkSZIKMYhJkiQVMlGempz0IuK7\nwGmZeUtEbAf8EjgzMz9WH18JnEI1M/exwFOAeZl5ZX3spCd6QCEi5gPLgbtadn8xM5eMs9aXA88A\nvg18KTP3Gs951B0R8W7gHcAzM3PdaH8fpF6wDdN42YZtHYNY96wA9gVuqX//FvAK4GMRMRPYGfhR\nZr4OICIOAJ4DXNnh+a8d/uzWysxr6hp26cb5tNWOBr4EvA5YVrYUNZhtmMbLNmwrGMS6ZwXwPqp1\n6F4BnA98JCLmUj3qviozhyLiPuB5VN8qd4iIm+rPv79eo+5JwOsz819G+4ERMaf+OX8KPBU4NzP/\nvv428iPg+VRzG90AHFy/7yBgAVUD+pn6PM8GLs7MF9XblwJnZ+YtW/MfRKOrewp+THUtLqalEYuI\nP633zaH6t7ooM6+NiNuBVVTTKQwBCzJzbUR8iOp/oNsA52Tml/v4R9HkZxumMbMN23qOEeue24Dn\nRMQ0qkkdVwHfAV4GzKdeq662EfgwVdf81+t938jMA4CrgVdt4fwHRMTKll/bAH9N1S1/EFXj9M6W\n99+Smf8d2B54JDMPpLotsN/IE2fmvcAfIuK59cSUz7QB65uFwPmZmcD6iNiz5dgiYEVmvhR4NfC5\n+u/XHOAfMnM/4H7gkIg4hOq67QPsD5xeN4JSp2zDNB62YVvJHrEuycxNEfEj4OXALzJzfURcDRwK\nzAM+NcophtdM+AXV2IuRNuvWj4h/B06JiCOollrZtuXwD+vfH+Q/x2X8Fpj5BD9/KfBG4KdU32DU\nYxHxZKqeh50i4q1UaxT+r5a37AZcApCZ90fEQ8BO9bHb6t9/RnVNnwEM1D0JUP1d2AX4xx7+ETSF\n2IZprGzDusMese5aAbyX6hshwI1UXfrTM/M3I967iT/+7z+emXXfBXwvM48Gvkw16/d4z3cZ1TfS\nV2Ij1i9HA5/LzIMy8+XAnlTX4M/r43dTddMTEU8Dngz8uj428vreA1yXmfOBA6gGRv+4p9VrKrIN\n01jYhnWBQay7VgD7AN8EyMxHqb7NrdrCe+8AFkTE1gxevRI4OSJWUT3NtCEith/PiTJzHXA98B9b\naHDVGwuBi4Y3MvMR4CvArvWu/0t1O+d64ArghJbFpke6EvhdRNxA1TMxlJkP96xyTVW2YRoL27Au\ncIkjPS4izgW+kpnXlq5FksbKNkyTkT1iAiAivg082QZM0mRkG6bJyh4xSZKkQuwRkyRJKsQgJkmS\nVIhBTJIkqRAndFXf1evD3csfLwAMcFhm/myc59yDapHZhfWEgGdk5sqtqVOSRrL9UrcZxFTKA5n5\nN906WWbeSjWnjST1mu2XusYgpgkjIp4PfBqYRbUMxtmZuTgizqBa/mJevX8R1czLe1ItDPw6qvXn\nzqhnZR4+30XADZm5pN6+Djg1M7/frz+TpGaw/dJ4GcRUylMjonUNsUuApwFnZuZ3I+JZVI3U4vr4\nC6garpcA19bb91ItobH7E/yMzwMfAJZExM7ATjZikrrA9ktdYxBTKZt17UfENsDLI+I0qsZpVsvh\nFZm5ISJ+Avw8M++qP3M/1fplW7KSqsHcBTgGuLC7fwRJDWX7pa7xqUlNJMupFuy9i2rh4VaPtrx+\norXK/khmDgFfAF4PvIaWNdEkqctsvzQu9ohpIjkQeE5mPhARb4THv2VujWXAjcA/ZeYDW3kuSXoi\ntl8aF3vENJGcAdwYET8EDgbuA565NSesHyf/GVWDJkm9cga2XxoH15rUlBUR04C/BFYBz8/M9YVL\nkqSO2H41hz1imsqOpHpy6TQbMUmTjO1XQ9gjJkmSVIg9YpIkSYUYxCRJkgoxiEmSJBViEJMkSSrE\nICZJklSIQUySJKmQ/w/OTrrMMbLrBQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1ad550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (axis1, axis2) = plt.subplots(1, 2, sharex=True, figsize = (10,5))\n",
"\n",
"sns.countplot(x = 'Family', data = titanic_df, order = [1, 0], ax = axis1)\n",
"\n",
"family_perc = titanic_df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()\n",
"display(family_perc)\n",
"sns.barplot(x = 'Family', y = 'Survived', data = family_perc, order = [1, 0], ax = axis2)\n",
"\n",
"axis1.set_xticklabels(['With Family', 'Alone'], rotation = 0)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Sex\n",
"\n",
"def get_person(passenger):\n",
" age, sex = passenger\n",
" return 'child' if age < 16 else sex\n",
"\n",
"titanic_df['Person'] = titanic_df[['Age', 'Sex']].apply(get_person, axis=1)\n",
"test_df['Person'] = test_df[['Age', 'Sex']].apply(get_person, axis=1)\n",
"\n",
"# No need to use sex column anymore. can be dropped\n",
"titanic_df.drop(['Sex'], axis=1, inplace=True)\n",
"test_df.drop(['Sex'], axis=1, inplace=True)\n",
"\n",
"person_dummies_titanic = pd.get_dummies(titanic_df['Person'])\n",
"person_dummies_titanic.columns = ['Child', 'Female', 'Male']\n",
"person_dummies_titanic.drop(['Male'], axis = 1, inplace=True)\n",
"\n",
"person_dummies_test = pd.get_dummies(test_df['Person'])\n",
"person_dummies_test.columns = ['Child', 'Female', 'Male']\n",
"person_dummies_test.drop(['Male'], axis = 1, inplace=True)\n",
"\n",
"titanic_df = titanic_df.join(person_dummies_titanic)\n",
"test_df = test_df.join(person_dummies_test)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11adbff60>"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dVdUDTrziHaVL6CoLZ19UugRtmU9RTfZ9f0QcBbwJ2Ad4MfC/qd56lKSOmzDyLhAR/VSD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\nSZIK+Q8hTcq2Elx79gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bcf3d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (axis1, axis2) = plt.subplots(1, 2, figsize = (10, 5))\n",
"sns.countplot(x = 'Person', data = titanic_df, ax = axis1)\n",
"\n",
"person_perc = titanic_df[['Person', 'Survived']].groupby(['Person'], as_index = False).mean()\n",
"sns.barplot(x = 'Person', y = 'Survived', data = person_perc, ax = axis2, order = ['male', 'female', 'child'])"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic_df.drop(['Person'], axis = 1, inplace=True)\n",
"test_df.drop(['Person'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x118f5e908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# PClass\n",
"\n",
"sns.factorplot('Pclass', 'Survived', order = [1,2,3], data = titanic_df, size = 5)\n",
"\n",
"pclass_dummies_titanic = pd.get_dummies(titanic_df['Pclass'])\n",
"pclass_dummies_titanic.columns = ['Class_1', 'Class_2', 'Class_3']\n",
"pclass_dummies_titanic.drop(['Class_3'], axis=1, inplace=True)\n",
"\n",
"pclass_dummies_test = pd.get_dummies(test_df['Pclass'])\n",
"pclass_dummies_test.columns = ['Class_1', 'Class_2', 'Class_3']\n",
"pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True)\n",
"\n",
"titanic_df.drop(['Pclass'], axis =1, inplace=True)\n",
"test_df.drop(['Pclass'], axis = 1, inplace=True)\n",
"\n",
"titanic_df = titanic_df.join(pclass_dummies_titanic)\n",
"test_df = test_df.join(pclass_dummies_test)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>Fare</th>\n",
" <th>Family</th>\n",
" <th>Child</th>\n",
" <th>Female</th>\n",
" <th>Class_1</th>\n",
" <th>Class_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>71</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>26</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>54</td>\n",
" <td>51</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>27</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>58</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>31</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>55</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1</td>\n",
" <td>32</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>34</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" <td>28</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" <td>31</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>263</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>861</th>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>862</th>\n",
" <td>1</td>\n",
" <td>48</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>863</th>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>69</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>864</th>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>865</th>\n",
" <td>1</td>\n",
" <td>42</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>866</th>\n",
" <td>1</td>\n",
" <td>27</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>868</th>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>869</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870</th>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>871</th>\n",
" <td>1</td>\n",
" <td>47</td>\n",
" <td>52</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>872</th>\n",
" <td>0</td>\n",
" <td>33</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>874</th>\n",
" <td>1</td>\n",
" <td>28</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875</th>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>876</th>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877</th>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>878</th>\n",
" <td>0</td>\n",
" <td>41</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>1</td>\n",
" <td>56</td>\n",
" <td>83</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>880</th>\n",
" <td>1</td>\n",
" <td>25</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>0</td>\n",
" <td>33</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>884</th>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>885</th>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>1</td>\n",
" <td>26</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>0</td>\n",
" <td>32</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Survived Age Fare Family Child Female Class_1 Class_2\n",
"0 0 22 7 1 0 0 0 0\n",
"1 1 38 71 1 0 1 1 0\n",
"2 1 26 7 0 0 1 0 0\n",
"3 1 35 53 1 0 1 1 0\n",
"4 0 35 8 0 0 0 0 0\n",
"5 0 38 8 0 0 0 0 0\n",
"6 0 54 51 0 0 0 1 0\n",
"7 0 2 21 1 1 0 0 0\n",
"8 1 27 11 1 0 1 0 0\n",
"9 1 14 30 1 1 0 0 1\n",
"10 1 4 16 1 1 0 0 0\n",
"11 1 58 26 0 0 1 1 0\n",
"12 0 20 8 0 0 0 0 0\n",
"13 0 39 31 1 0 0 0 0\n",
"14 0 14 7 0 1 0 0 0\n",
"15 1 55 16 0 0 1 0 1\n",
"16 0 2 29 1 1 0 0 0\n",
"17 1 32 13 0 0 0 0 1\n",
"18 0 31 18 1 0 1 0 0\n",
"19 1 39 7 0 0 1 0 0\n",
"20 0 35 26 0 0 0 0 1\n",
"21 1 34 13 0 0 0 0 1\n",
"22 1 15 8 0 1 0 0 0\n",
"23 1 28 35 0 0 0 1 0\n",
"24 0 8 21 1 1 0 0 0\n",
"25 1 38 31 1 0 1 0 0\n",
"26 0 42 7 0 0 0 0 0\n",
"27 0 19 263 1 0 0 1 0\n",
"28 1 35 7 0 0 1 0 0\n",
"29 0 15 7 0 1 0 0 0\n",
".. ... ... ... ... ... ... ... ...\n",
"861 0 21 11 1 0 0 0 1\n",
"862 1 48 25 0 0 1 1 0\n",
"863 0 22 69 1 0 1 0 0\n",
"864 0 24 13 0 0 0 0 1\n",
"865 1 42 13 0 0 1 0 1\n",
"866 1 27 13 1 0 1 0 1\n",
"867 0 31 50 0 0 0 1 0\n",
"868 0 42 9 0 0 0 0 0\n",
"869 1 4 11 1 1 0 0 0\n",
"870 0 26 7 0 0 0 0 0\n",
"871 1 47 52 1 0 1 1 0\n",
"872 0 33 5 0 0 0 1 0\n",
"873 0 47 9 0 0 0 0 0\n",
"874 1 28 24 1 0 1 0 1\n",
"875 1 15 7 0 1 0 0 0\n",
"876 0 20 9 0 0 0 0 0\n",
"877 0 19 7 0 0 0 0 0\n",
"878 0 41 7 0 0 0 0 0\n",
"879 1 56 83 1 0 1 1 0\n",
"880 1 25 26 1 0 1 0 1\n",
"881 0 33 7 0 0 0 0 0\n",
"882 0 22 10 0 0 1 0 0\n",
"883 0 28 10 0 0 0 0 1\n",
"884 0 25 7 0 0 0 0 0\n",
"885 0 39 29 1 0 1 0 0\n",
"886 0 27 13 0 0 0 0 1\n",
"887 1 19 30 0 0 1 1 0\n",
"888 0 17 23 1 0 1 0 0\n",
"889 1 26 30 0 0 0 1 0\n",
"890 0 32 7 0 0 0 0 0\n",
"\n",
"[891 rows x 8 columns]"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define training and testing sets\n",
"\n",
"X_train = titanic_df.drop('Survived', axis = 1)\n",
"Y_train = titanic_df['Survived']\n",
"X_test = test_df.drop('PassengerId', axis=1).copy()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.96184062850729513"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Random Forests\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"random_forest = RandomForestClassifier(n_estimators= 100)\n",
"random_forest.fit(X_train, Y_train)\n",
"Y_pred = random_forest.predict(X_test)\n",
"random_forest.score(X_train, Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"submission = pd.DataFrame({'PassengerId':test_df['PassengerId'], 'Survived':Y_pred})\n",
"submission.to_csv('titanic.csv', index = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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