Features/Titanic+Dataset+.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 186,
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC, LinearSVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.linear_model import SGDClassifier"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \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",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \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 \n",
"5 0 330877 8.4583 NaN Q \n",
"6 0 17463 51.8625 E46 S \n",
"7 1 349909 21.0750 NaN S \n",
"8 2 347742 11.1333 NaN S \n",
"9 0 237736 30.0708 NaN C "
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv('C:/Users/R AMARTYA/Desktop/train.csv')\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
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"\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": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have a general idea of the data set contents."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2008b9752b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Survived'].value_counts().plot(kind='bar', title='Death and Survival Counts',grid=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this,we infer that majority of people did not survive the accident."
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x20088947048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Sex'].value_counts().plot(kind='bar', title='Sex')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be infered that the majority of people in the ship were male."
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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91KZmtrf2bcDKWT++Anhw131W1QZgA8CaNWtqcnJy9x7BbrjsyptHNhYMTizXbj1oZOPd\nsXZyZGONxQjva59aeSmTD1w9svH4Oe+jX0xTU1OMMlv2VsPcdRPgGmBzVf2nWZtuBNa15XXADbPa\nL2h335wOPDEzxSNJGr1hruhfD/w88KUkX2htvwa8B9iU5GLgfuD8tu3jwDnAFuBp4MJFrViStCDz\nBn1VfYa5590BzpyjfwGX7GFdkqRF4l/GSlLnDHpJ6pzvXilpZM7YdMZIx1u7/1qu2HTFyMa79c23\njmyshfCKXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6pxB\nL0mdM+glqXMGvSR1zqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS\n1DmDXpI6Z9BLUucMeknqnEEvSZ0z6CWpcwa9JHVu3qBPcm2S7Um+PKvt8CQ3Jbm3fT+stSfJ1Um2\nJLk7ySlLWbwkaX7DXNH/d+CsXdouB26pqtXALW0d4GxgdftaD3xgccqUJO2ueYO+qv438NguzecC\nG9vyRuC8We0frIHbgEOTHL1YxUqSFm535+gnquohgPb9qNZ+LPDArH7bWpskaUyWLfL+Mkdbzdkx\nWc9geoeJiQmmpqYWuZQXdtHxO0c2FsDyA58b6ZijfC7HYuWlIxtq+oCjmBrheHR+7Nbuv3ak4x2e\nw0c65t76f293g/6RJEdX1UNtamZ7a98GrJzVbwXw4Fw7qKoNwAaANWvW1OTk5G6WsnCXXXnzyMaC\nwYnl2q0HjWy8O9ZOjmyssXjfW0c21NTKS5l84OqRjcfP3TO6scbgik1XjHS8tfuv5brvXTey8W6d\nvHVkYy3E7k7d3Aisa8vrgBtmtV/Q7r45HXhiZopHkjQe817RJ/lTYBI4Isk24N3Ae4BNSS4G7gfO\nb90/DpwDbAGeBi5cgpolSQswb9BX1VteYNOZc/Qt4JI9LUqStHj8y1hJ6pxBL0mdM+glqXMGvSR1\nzqCXpM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucM\neknqnEEvSZ0z6CWpcwa9JHXOoJekzhn0ktQ5g16SOmfQS1LnDHpJ6pxBL0mdM+glqXMGvSR1zqCX\npM4Z9JLUOYNekjpn0EtS5wx6SeqcQS9JnVuSoE9yVpJ7kmxJcvlSjCFJGs6iB32S/YDfBc4GTgLe\nkuSkxR5HkjScpbiiPw3YUlX3VdV3geuAc5dgHEnSEFJVi7vD5E3AWVX1r9r6zwOvraq37dJvPbC+\nrZ4I3LOohexdjgAeHXcR2i0eu31b78fvuKo6cr5Oy5Zg4MzR9ryzSVVtADYswfh7nSR3VtWacdeh\nhfPY7ds8fgNLMXWzDVg5a30F8OASjCNJGsJSBP1ngdVJXpnkAGAtcOMSjCNJGsKiT91U1TNJ3gZ8\nAtgPuLaqvrLY4+xjXhRTVJ3y2O3bPH4swYuxkqS9i38ZK0mdM+glqXMGvSR1zqBfAklOS3JqWz4p\nyTuSnDPuuqTeJfnhJGcmOWSX9rPGVdPewBdjF1mSdzN4n59lwE3Aa4Ep4A3AJ6rqyvFVpz2R5MKq\n+oNx16G5JbkUuATYDJwMvL2qbmjbPldVp4yzvnEy6BdZki8x+Ed2IPAwsKKqnkxyEHB7Vf2jsRao\n3Zbk/qr6B+OuQ3Nr//deV1XTSVYB1wN/WFW/k+TzVfWasRY4RkvxFggvds9U1bPA00m2VtWTAFW1\nM8lzY65N80hy9wttAiZGWYsWbL+qmgaoqq8nmQSuT3Icc781y4uGQb/4vpvkB6rqaeDHZhqTvAIw\n6Pd+E8A/AXbs0h7gr0Zfjhbg4SQnV9UXANqV/RuBa4EfHW9p42XQL76frKrvAFTV7GDfH1g3npK0\nAB8DDpkJi9mSTI2+HC3ABcAzsxuq6hnggiS/N56S9g7O0UtS57y9UpI6Z9BLUucMeknqnEEvSZ0z\n6CWpc/8PBAmLJAlSW5cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2008904f5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Pclass'].value_counts().plot(kind='bar', title='Passenger Class',alpha=0.90,grid=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be infered that the largest number of passengers were in class 3 followed by class 1 and class 2."
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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e18sGI+Ik4MttTScAHwIOB95G69amAO/PzK/3sg1JUv9mLBARMQ/4ZmaeDvRUFNpl5u3A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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2008b79ac18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Age'].plot(kind='hist',title='Age',alpha=0.90,grid=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be infered that majority of passengers were in the age group 15-30 years."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature1 : Passenger Class "
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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BpgCMGDEi6uvr2z3Y1kw47c6KtQXZP8oLnirnblnb88fUV6ytaqjk/vO+a1/+\nv1cd5Rw+mgEMkjRA0ibAOGBaYQFJewOXAqMj4u9ljMXMzEpQtqQQEauBE4G7gbnAjRHxjKQfSRqd\niv0X0AO4SdIsSdNaqc7MzCqgrH2liJgOTG8274yCzweXs30zM1s3/kWzmZnlnBTMzCznpGBmZjkn\nBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczM\nck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOC\nmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5cqaFCQdKmmepPmS\nTmth+aaSbkjLH5XUv5zxmJlZcWVLCpJqgMnA54AhwHhJQ5oVOx54MyIGAj8DflqueMzMrG3l7Cns\nA8yPiAURsQqYCoxpVmYMcFX6fDNwkCSVMSYzMytCEVGeiqUjgUMj4p/T9FeAfSPixIIyT6cyjWn6\nuVTm9WZ1TQQmpsnBwLyyBN0x9AFeb7OUdUTed53bhr7/doqIbdsq1LWMAbR0xt88A5VShoiYAkxp\nj6A6OkmPR8SIasdh6877rnPz/suUc/ioEehXMF0HvNRaGUldgV7AG2WMyczMiihnUpgBDJI0QNIm\nwDhgWrMy04Cvps9HAn+Mco1nmZlZm8o2fBQRqyWdCNwN1ABXRMQzkn4EPB4R04DLgaslzSfrIYwr\nVzydyEYxTLaB8r7r3Lz/KOOFZjMz63z8i2YzM8s5KZiZWc5JwczMck4KZutJ0m6SDpLUo9n8Q6sV\nk5VO0j6SRqbPQySdIumwasdVbb7Q3EFJ+lpE/E+147CWSToJOAGYCwwHTo6I29KyP0fEx6sZnxUn\n6Uyy57J1Be4F9gUagIOBuyPinOpFV11OCh2UpBci4mPVjsNaJukpYL+IWJae7nszcHVEXCTpiYjY\nu6oBWlFp/w0HNgVeAeoi4m1JmwGPRsReVQ2wisr5mAtrg6TZrS0CaisZi62zmohYBhARz0uqB26W\ntBMtP77FOpbVEbEGWCHpuYh4GyAi3pH0fpVjqyonheqqBUYBbzabL+Chyodj6+AVScMjYhZA6jEc\nDlwB7Fnd0KwEqyRtHhErgE80zZTUC3BSsKq5A+jRdGApJKmh8uHYOjgWWF04IyJWA8dKurQ6Idk6\nOCAi3gWIiMIk0I0PHr2zUfI1BTMzy/mWVDMzyzkpmJlZzknBOhVJayTNkvS0pJskbV7tmNqLpF0l\nTZc0X9JcSTdKqpVUL+mOaseSIrrHAAADpklEQVRnGwcnBets3omI4RGxB7AK+Ga1A1of6aVShdPd\ngTuBX0bEwIjYHfgl0ObrE83ak5OCdWYPAAMBJN0qaaakZ9I7vZFUI+nK1Kt4StJ30/yTJM2RNFvS\n1DRvC0lXSJoh6QlJY9L8CZL+V9Jdkp6VdF5T45KOl/RXSQ2SLpN0SZq/raRbUl0zJO2f5k+SNEXS\nPcBvmq3L0cDDEXF704yIuC8ini4slB7N8FCK8SFJg9P8oZIeS72o2ZIGpXW6U9KTaRuMbc+Nbxsm\n35JqnVI60/4ccFeadVxEvJF+kTpD0i1Af2DH1KtAUu9U9jRgQES8WzDv+2Rv/jsuzXtM0u/TsuHA\n3sC7wDxJPwfWAD8EPg4sBf4IPJnKXwT8LCL+T9LHyF40tXta9gngUxHxTrNV2gOYWcKq/4XsdsrV\nkg4GfgJ8kazHdFFEXJvedFgDHAa8FBGfT+vfq4T6bSPnpGCdzWaSmn7X8QDZ2/sATpL0hfS5HzAI\nmAfsnA7idwL3pOWzgWsl3QrcmuYdAoyW9K9pujvQ9JiRP0TEEgBJc4CdgD7A/RHxRpp/E7BrKn8w\nMETKf9i8paSe6fO0FhLCuugFXCVpEBBk99UDPAx8X1Id8L8R8Wx6lMP5kn4K3BERD3yEdm0j4eEj\n62yarikMj4jvRMSq9IiJg8meRTQMeALoHhFvAsPIHnR2AvDrVMfngclkZ+0zU69DwBcL6v5YRMxN\n5d8taH8N2clUsUdZdEmxNNW1Y0QsTcuWt/KdZyj4ZW0RZwP3pd7PEWTJi4i4DhgNvAPcLekzEfHX\nVOdTwH9KOqOE+m0j56RgG4JewJsRsULSbsA/AEjqA3SJiFtIQz2SugD9IuI+4HtAb6AH2RDPd5RO\n7yW19UC7x4ADJW2VksoXC5bdA5zYNCFpeAnrcB3wSUmfL/jeoZKaPzKjF7AofZ5QUHZnYEFEXAxM\nA/aS1BdYERHXAOeTDXWZFeWkYBuCu4Cu6QGDZwOPpPk7Ag1puOlK4HSysfZr0tDKE2Rj/2+l73UD\nZkt6Ok23KiIWkY3nPwr8HpgDLEmLTwJGpAu+cyjhDqk0pHQ4WWJ6Nn1vAvD3ZkXPIzvrfzCtS5Ox\nwNNpXXcju5C9J9m1kVlk10x+3FYcZn7Mhdl6ktQjPQivK/Bb4IqI+G214zL7KNxTMFt/k9JZ+NPA\nQj64aG3WabmnYGZmOfcUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaW+/8MlPYP9qK8xQAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20088db36a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rel_1=pd.crosstab(df['Pclass'],df['Survived'])\n",
"rel_1_normalize = rel_1.div(rel_1.sum(1).astype(float), axis=0)\n",
"rel_1_normalize.plot(kind='bar',title='Survival Rate by Passenger Classes',stacked=True,grid=True)\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those in First Class has the highest chance for survival."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature2 : Sex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" <th>Sex_Val</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",
" <td>1</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",
" <td>0</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",
" <td>0</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",
" <td>0</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",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>0</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",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \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",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Val \n",
"0 0 A/5 21171 7.2500 NaN S 1 \n",
"1 0 PC 17599 71.2833 C85 C 0 \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 \n",
"3 0 113803 53.1000 C123 S 0 \n",
"4 0 373450 8.0500 NaN S 1 \n",
"5 0 330877 8.4583 NaN Q 1 \n",
"6 0 17463 51.8625 E46 S 1 \n",
"7 1 349909 21.0750 NaN S 1 \n",
"8 2 347742 11.1333 NaN S 0 \n",
"9 0 237736 30.0708 NaN C 0 "
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sexes = sorted(df['Sex'].unique())\n",
"genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
"df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"data": {
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avhynmVmv58txmplZpqyX4zQzs54lTyi0vBznQHJcjtPMzHqePCcrb74c5xUkp74ekN42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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20088f19940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rel_2=pd.crosstab(df['Sex'],df['Survived'])\n",
"rel_2_normalize=rel_2.div(rel_2.sum(1).astype(float),axis=0)\n",
"rel_2_normalize.plot(kind='bar',title='Survival Rate by Sex',stacked=True,grid=True) \n",
"plt.xlabel('Sex')\n",
"plt.ylabel('Survival Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The majority of females survived, whereas the majority of males did not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count males and females in each Pclass:"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the unique values of Pclass:\n",
"passenger_classes = sorted(df['Pclass'].unique())\n",
"passenger_classes"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"M : 1 122\n",
"F : 1 94\n",
"M : 2 108\n",
"F : 2 76\n",
"M : 3 347\n",
"F : 3 144\n"
]
}
],
"source": [
"for i in passenger_classes:\n",
" print ('M : ',i ,len(df[(df['Sex'] == 'male') & (df['Pclass'] == i)]))\n",
" print ('F : ',i ,len(df[(df['Sex'] =='female') & (df['Pclass'] == i)]))\n",
"male=df[df['Sex']=='male']\n",
"female=df[df['Sex']=='female']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot survival rate by Sex and Pclass:"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2008b9892b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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fTXq6V7/zzgblt95qy+xz/513ZPttezNr9t/57bT7+eX3vvW++kePHs0555zD66+/zowZ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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20089328320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"male_rel=pd.crosstab(male['Pclass'],male['Survived'])\n",
"male_rel_normalize=male_rel.div(male_rel.sum(1).astype(float),axis=0)\n",
"male_rel_normalize.plot(kind='bar',title='Survival rate of male by passenger classes',stacked='True',grid='True')\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')\n",
"plt.show()\n",
"female_rel=pd.crosstab(female['Pclass'],female['Survived'])\n",
"female_rel_normalize=female_rel.div(female_rel.sum(1).astype(float),axis=0)\n",
"female_rel_normalize.plot(kind='bar',title='Survival rate of female by passenger classes',stacked='True',grid='True')\n",
"plt.xlabel('Passenger Class')\n",
"plt.ylabel('Survival Rate')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['Embarked_Value']=df['Embarked']\n",
"df['Embarked_Value'].replace(['C','Q','S'],[1,2,3],inplace=True)\n",
"embarked_locs = sorted(df['Embarked_Value'].unique())"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x20088efe588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Embarked_Value'].plot(kind='hist',bins=3,range=(1,3),grid='True')\n",
"plt.title('Port of Embarkation Histogram')\n",
"plt.xlabel('Port of Embarkation')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature3: Embarked"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" <th>Sex_Val</th>\n",
" <th>Embarked_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>62</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Icard, Miss. Amelie</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113572</td>\n",
" <td>80.0</td>\n",
" <td>B28</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>829</th>\n",
" <td>830</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
" <td>female</td>\n",
" <td>62.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>113572</td>\n",
" <td>80.0</td>\n",
" <td>B28</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name \\\n",
"61 62 1 1 Icard, Miss. Amelie \n",
"829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked Sex_Val \\\n",
"61 female 38.0 0 0 113572 80.0 B28 NaN 0 \n",
"829 female 62.0 0 0 113572 80.0 B28 NaN 0 \n",
"\n",
" Embarked_Value \n",
"61 NaN \n",
"829 NaN "
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Embarked'].isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus we can see that the Embarked Column is missing certain values, which have to be filled otherwise it may cause problems during the machine learning algorithms."
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead 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",
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" <th>Name</th>\n",
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" <th>Embarked</th>\n",
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" </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",
" <td>1</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",
" <td>0</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",
" <td>0</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",
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" <td>S</td>\n",
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" <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",
" <td>1</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>1</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>McCarthy, Mr. Timothy J</td>\n",
" <td>male</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17463</td>\n",
" <td>51.8625</td>\n",
" <td>E46</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Palsson, Master. Gosta Leonard</td>\n",
" <td>male</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>349909</td>\n",
" <td>21.0750</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
" <td>female</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>347742</td>\n",
" <td>11.1333</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>0</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
" <td>female</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>237736</td>\n",
" <td>30.0708</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>C</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",
"5 6 0 3 \n",
"6 7 0 1 \n",
"7 8 0 3 \n",
"8 9 1 3 \n",
"9 10 1 2 \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",
"5 Moran, Mr. James male NaN 0 \n",
"6 McCarthy, Mr. Timothy J male 54.0 0 \n",
"7 Palsson, Master. Gosta Leonard male 2.0 3 \n",
"8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n",
"9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n",
"\n",
" Parch Ticket Fare Cabin Embarked Sex_Val Embarked_Value \n",
"0 0 A/5 21171 7.2500 NaN S 1 S \n",
"1 0 PC 17599 71.2833 C85 C 0 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S 0 S \n",
"3 0 113803 53.1000 C123 S 0 S \n",
"4 0 373450 8.0500 NaN S 1 S \n",
"5 0 330877 8.4583 NaN Q 1 Q \n",
"6 0 17463 51.8625 E46 S 1 S \n",
"7 1 349909 21.0750 NaN S 1 S \n",
"8 2 347742 11.1333 NaN S 0 S \n",
"9 0 237736 30.0708 NaN C 0 C "
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Embarked_Value']=df['Embarked']\n",
"df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We make a new column 'Embarked_Value'and replace C,Q,S with 1,2,3 so that we can carry out our machine learning algorithms on it."
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [],
"source": [
"df['Embarked_Value'].replace(['C','Q','S'],[1,2,3],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1.0, 2.0, 3.0, nan]"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"embarked_locs = sorted(df['Embarked_Value'].unique())\n",
"embarked_locs"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [
{
"data": {
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prVKX3t5eml22So6rMY6rcY3GNvu0q6sLpuTUqRs4e1llH0lNa9e45k8fX3kbq3KvDwfe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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2008ba95358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Embarked_Value'].plot(kind='hist',bins=3,range=(1,3),grid='True')\n",
"plt.title('Port of Embarkation Histogram')\n",
"plt.xlabel('Port of Embarkation')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the majority of passengers embarked in 'S': 3, we assign the missing values in Embarked to 'S':"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1.0, 2.0, 3.0]"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Embarked_Value']=df['Embarked_Value'].fillna(3)\n",
"embarked_locs = sorted(df['Embarked_Value'].unique())\n",
"embarked_locs"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzj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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20088f96630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"embarked_rel=pd.crosstab(df['Embarked_Value'],df['Survived'])\n",
"embarked_rel_normalize=embarked_rel.div(embarked_rel.sum(1).astype(float),axis=0)\n",
"embarked_rel_normalize.plot(kind='bar',title='Survival Rate by Port of Embarkation',grid='True',stacked='True')\n",
"plt.xlabel('Port of Embarkation')\n",
"plt.ylabel('Survival Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature4 : Age"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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",
" <th>Sex_Val</th>\n",
" <th>Embarked_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Moran, Mr. James</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330877</td>\n",
" <td>8.4583</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Williams, Mr. Charles Eugene</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>244373</td>\n",
" <td>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Masselmani, Mrs. Fatima</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2649</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Emir, Mr. Farred Chehab</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2631</td>\n",
" <td>7.2250</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>330959</td>\n",
" <td>7.8792</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Todoroff, Mr. Lalio</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349216</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17569</td>\n",
" <td>146.5208</td>\n",
" <td>B78</td>\n",
" <td>C</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Glynn, Miss. Mary Agatha</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>335677</td>\n",
" <td>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Mamee, Mr. Hanna</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2677</td>\n",
" <td>7.2292</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Kraeff, Mr. Theodor</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>349253</td>\n",
" <td>7.8958</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"5 6 0 3 \n",
"17 18 1 2 \n",
"19 20 1 3 \n",
"26 27 0 3 \n",
"28 29 1 3 \n",
"29 30 0 3 \n",
"31 32 1 1 \n",
"32 33 1 3 \n",
"36 37 1 3 \n",
"42 43 0 3 \n",
"\n",
" Name Sex Age SibSp Parch \\\n",
"5 Moran, Mr. James male NaN 0 0 \n",
"17 Williams, Mr. Charles Eugene male NaN 0 0 \n",
"19 Masselmani, Mrs. Fatima female NaN 0 0 \n",
"26 Emir, Mr. Farred Chehab male NaN 0 0 \n",
"28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 0 \n",
"29 Todoroff, Mr. Lalio male NaN 0 0 \n",
"31 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 0 \n",
"32 Glynn, Miss. Mary Agatha female NaN 0 0 \n",
"36 Mamee, Mr. Hanna male NaN 0 0 \n",
"42 Kraeff, Mr. Theodor male NaN 0 0 \n",
"\n",
" Ticket Fare Cabin Embarked Sex_Val Embarked_Value \n",
"5 330877 8.4583 NaN Q 1 2.0 \n",
"17 244373 13.0000 NaN S 1 3.0 \n",
"19 2649 7.2250 NaN C 0 1.0 \n",
"26 2631 7.2250 NaN C 1 1.0 \n",
"28 330959 7.8792 NaN Q 0 2.0 \n",
"29 349216 7.8958 NaN S 1 3.0 \n",
"31 PC 17569 146.5208 B78 C 0 1.0 \n",
"32 335677 7.7500 NaN Q 0 2.0 \n",
"36 2677 7.2292 NaN C 1 1.0 \n",
"42 349253 7.8958 NaN C 1 1.0 "
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Age'].isnull()].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here also many ages are missing. So we assign the missing ages the median values of the age \n",
"according to their sex and passenger class."
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 22.0\n",
"1 38.0\n",
"2 26.0\n",
"3 35.0\n",
"4 35.0\n",
"5 25.0\n",
"6 54.0\n",
"7 2.0\n",
"8 27.0\n",
"9 14.0\n",
"Name: Age_complete, dtype: float64"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Age_complete'] = df['Age']\n",
"df['Age_complete']=df['Age_complete'].groupby([df['Sex_Val'],df['Pclass']]).apply(lambda x: x.fillna(x.median()))\n",
"df['Age_complete'].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\R AMARTYA\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" return getattr(obj, method)(*args, **kwds)\n"
]
},
{
"data": {
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QFdN3vA94JzBW0jtLLGkacEGbtuuAByLiWOCBdL+zbQI+FxEnAGcA16TXqRZq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\nzHI5IMzMLNf/B+FtyWy4vuUGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2008908a400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"age_survived=df[df['Survived']==1]['Age_complete']\n",
"age_not_survived = df[df['Survived'] == 0]['Age_complete']\n",
"max_age=max(df['Age_complete'])\n",
"\n",
"plt.hist([age_survived,age_not_survived],bins=8,range=(1, max_age),stacked=True)\n",
"plt.title('Survivors by Age Plot')\n",
"plt.xlabel('Ages')\n",
"plt.ylabel('Count')\n",
"plt.legend(('Died', 'Survived'), loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more detailed analysis,we draw the age density plots."
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ROnTowE033URYWFhN85KzlJQU/vKXvzSa72WXXcaNN94IwAMPPMDs2bO57bbbeOyxx1i8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20088f11048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in passenger_classes :\n",
" df.Age_complete[df.Pclass==i].plot(kind='density')\n",
"plt.title('Age Density Plots by passenger Class')\n",
"plt.xlabel('Age')\n",
"plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature5 : Family Size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define a new feature FamilySize that is the sum of Parch (number of parents or children on board) and SibSp (number of siblings or spouses):"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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TgEuB7euqlWcNsK51n3OAOQAzZsxwT0/PYGHUOu38eZy6uK2fgYy4pYf2NNJub28vQ32/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\n4Jb0IfAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20082121908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Family_Size'] = df['SibSp'] + df['Parch']\n",
"df['Family_Size'].hist(bins=10)\n",
"plt.xlabel('Family Size')\n",
"plt.ylabel('No. of families')\n",
"plt.title('Family Size Histogram')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\R AMARTYA\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" return getattr(obj, method)(*args, **kwds)\n"
]
},
{
"data": {
"image/png": 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ve9/L5MmTWbhwIeeddx7f/e53W7zk9YwzzmD8+PHNLiiPHj2aUaNGMXfu3G1lt956K+ef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\nwszMcv0vlmUXM+KqVFgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x200891e2f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"family_sizes=sorted(df['Family_Size'].unique())\n",
"family_sizes_max=max(family_sizes)\n",
"family_size_survived=df[df['Survived']==1]['Family_Size']\n",
"family_size_not_survived=df[df['Survived']==0]['Family_Size']\n",
"plt.hist([family_size_survived,family_size_not_survived],bins=family_sizes_max+1,range=(0, family_sizes_max),stacked=True)\n",
"plt.legend(('Died', 'Survived'), loc='best')\n",
"plt.xlabel('Family size')\n",
"plt.ylabel('No. of families')\n",
"plt.title('Survivors by Family Size')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data for machine learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We drop the columns having strings in it or those which we have used like we remove SibSp and Parch as we use Family_Size which has the attributes of both of them."
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Fare</th>\n",
" <th>Sex_Val</th>\n",
" <th>Age_complete</th>\n",
" <th>Family_Size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>7.2500</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
" <td>0</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>7.9250</td>\n",
" <td>0</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
" <td>0</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>8.0500</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>8.4583</td>\n",
" <td>1</td>\n",
" <td>25.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>51.8625</td>\n",
" <td>1</td>\n",
" <td>54.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>21.0750</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>11.1333</td>\n",
" <td>0</td>\n",
" <td>27.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>30.0708</td>\n",
" <td>0</td>\n",
" <td>14.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Fare Sex_Val Age_complete Family_Size\n",
"0 0 3 7.2500 1 22.0 1\n",
"1 1 1 71.2833 0 38.0 1\n",
"2 1 3 7.9250 0 26.0 0\n",
"3 1 1 53.1000 0 35.0 1\n",
"4 0 3 8.0500 1 35.0 0\n",
"5 0 3 8.4583 1 25.0 0\n",
"6 0 1 51.8625 1 54.0 0\n",
"7 0 3 21.0750 1 2.0 4\n",
"8 1 3 11.1333 0 27.0 2\n",
"9 1 2 30.0708 0 14.0 1"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes[df.dtypes.map(lambda x: x == 'object')]\n",
"df=df.drop(['Name','Sex','Ticket','Cabin','Embarked','SibSp','Parch','PassengerId','Age','Embarked_Value'],axis=1)\n",
"df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is a summary of the operations we performed on our training data set. We encapsulate this in a function since we'll need to do the same operations to our test set later."
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def data_prep(df_given):\n",
" # Get the unique values of Sex\n",
" sexes = sorted(df_given['Sex'].unique())\n",
" # Generate a mapping of Sex from a string to a number representation \n",
" genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
" # Transform Sex from a string to a number representation\n",
" df_given['Sex_Val'] = df_given['Sex'].map(genders_mapping).astype(int)\n",
" # Get the unique values of Embarked\n",
" df_given['Embarked_Value']=df_given['Embarked']\n",
" df_given['Embarked_Value'].replace(['C','Q','S'],[1,2,3],inplace=True)\n",
" df_given['Embarked_Value']=df_given['Embarked_Value'].fillna(3)\n",
" # embarked_locs = sorted(df_given['Embarked_Value'].unique())\n",
" # Fill in missing values of Fare with the average Fare\n",
" if len(df_given[df_given['Fare'].isnull()] > 0):\n",
" avg_fare = df_given['Fare'].mean()\n",
" df_given['Fare'].fillna(avg_fare, inplace=True)\n",
" df_given['Age_complete'] = df_given['Age']\n",
" df_given['Age_complete']=df_given['Age_complete'].groupby([df_given['Sex_Val'],df_given['Pclass']]).apply(lambda x: x.fillna(x.median()))\n",
" df_given['Family_Size'] = df_given['SibSp'] + df_given['Parch']\n",
" # Drop the columns we won't use:\n",
" # Drop the Age column since we will be using the Age_omplete column instead.\n",
" # Drop the SibSp and Parch columns since we will be using Family_Size.\n",
" # Drop the PassengerId column since it won't be used as a feature.\n",
" df_given=df_given.drop(['Name','Sex','Ticket','Cabin','Embarked','SibSp','Parch','PassengerId','Age','Embarked_Value'],axis=1)#axis=1 means columns\n",
" #if drop_passenger_id:\n",
" # df_given = df_given.drop(['PassengerId'], axis=1)\n",
" \n",
" return df_given\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We store the data as numpy array to perform machine learning algorithms."
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data=df.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive_Bayes Classifier"
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf = GaussianNB()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"train_features includes all the columns except the Survived.\n",
"train_target includes the Survived column."
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_features = train_data[:, 1:]\n",
"train_target = train_data[:, 0]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We fit the classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading and converting the test data to numpy array"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {},
"outputs": [],
"source": [
"df_test1 = pd.read_csv('C:/Users/R AMARTYA/Desktop/test.csv') \n",
"df_test = data_prep(df_test1)\n",
"test_x1 = df_test.values\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"test_y1 is the predicted Survival values of the test data and thus our answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we check the accuracy of our databy training with 80% of our data and testing on rest 20%"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 5) (891,)\n",
"(712, 5) (712,)\n",
"(179, 5) (179,)\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"from sklearn.cross_validation import train_test_split\n",
"# Split 80-20 train vs test data\n",
"train_x, test_x, train_y, test_y = train_test_split(train_features, train_target, test_size=0.20, random_state=0)\n",
"print (train_features.shape, train_target.shape)\n",
"print (train_x.shape, train_y.shape)\n",
"print (test_x.shape, test_y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"train_test_split here means that train_x and test_x are made from train_features and train_y and test_y are made from train_target"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Naive Bayes Accuracy = 0.82\n"
]
}
],
"source": [
"clf = clf.fit(train_x, train_y)\n",
"predict_y = clf.predict(test_x)\n",
"from sklearn.metrics import accuracy_score\n",
"print (\"Naive Bayes Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 0., 0.])"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_y1 = clf.predict(test_x1)\n",
"test_y1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"test_y1 is the predicted values of our test data i.e. test_x1"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [],
"source": [
"df_test1['Survived'] = test_y1\n",
"df_test1[['PassengerId','Survived']].to_csv('C:/Users/R AMARTYA/Desktop/submission1.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RandomForest Accuracy = 0.84\n"
]
}
],
"source": [
"RandomForest = RandomForestClassifier(n_estimators=1000)\n",
"RandomForest.fit(train_x,train_y)\n",
"predict_y = RandomForest.predict(test_x)\n",
"print (\"RandomForest Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))\n"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Kmeans Accuracy = 0.73\n"
]
}
],
"source": [
"\n",
"knn = KNeighborsClassifier(n_neighbors = 3)\n",
"knn.fit(train_x, train_y)\n",
"predict_y = knn.predict(test_x)\n",
"print (\"Kmeans Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Regression Accuracy = 0.79\n"
]
}
],
"source": [
"logreg = LogisticRegression()\n",
"logreg.fit(train_x, train_y)\n",
"predict_y = logreg.predict(test_x)\n",
"print (\"Regression Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))\n"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SVC Accuracy = 0.71\n"
]
}
],
"source": [
"svc = SVC()\n",
"svc.fit(train_x, train_y)\n",
"predict_y = svc.predict(test_x)\n",
"print (\"SVC Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decision Tree Accuracy = 0.80\n"
]
}
],
"source": [
"decision_tree = DecisionTreeClassifier()\n",
"decision_tree.fit(train_x, train_y)\n",
"predict_y = decision_tree.predict(test_x)\n",
"print (\"Decision Tree Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient Descent Accuracy = 0.75\n"
]
}
],
"source": [
"sgd = SGDClassifier(max_iter=100)\n",
"sgd.fit(train_x, train_y)\n",
"predict_y = sgd.predict(test_x)\n",
"print (\"Gradient Descent Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
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