Titanic Prediction using SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.svm import SVC, LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(r'F:\\Datasets\\titanic\\train.csv')\n",
    "test_file = r'F:\\Datasets\\titanic\\test.csv'\n",
    "test_df = pd.read_csv(test_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>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",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Pclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Pclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>61</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\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",
       "    </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  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Embarked'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Embarked'].fillna(value = 'C', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</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>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",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked  \n",
       "0         A/5 21171   7.2500   NaN         0  \n",
       "1          PC 17599  71.2833   C85         1  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0  \n",
       "3            113803  53.1000  C123         0  \n",
       "4            373450   8.0500   NaN         0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Sex'].replace({'male':0,'female':1}, inplace=True)\n",
    "train_df['Embarked'].replace({'S':0,'C':1,'Q':2}, inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aec59f7f88>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#vizualising for null values\n",
    "sns.heatmap(train_df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Age'].fillna((train_df['Age'].mean()), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Cabin', 'PassengerId', 'Name', 'Ticket'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aec5ae7848>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for missing values on train data\n",
    "sns.heatmap(train_df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aec5b88d88>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for null values in testdata\n",
    "sns.heatmap(test_df.isnull())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.fillna(test_df['Age'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 7 columns):\n",
      "Pclass      418 non-null int64\n",
      "Sex         418 non-null object\n",
      "Age         418 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Fare        418 non-null float64\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(2), int64(3), object(2)\n",
      "memory usage: 23.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Q', 'S', 'C'], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Sex'].replace({'male':0, 'female':1}, inplace= True)\n",
    "test_df['Embarked'].replace({'Q':0,'S':1,'C':2}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 7 columns):\n",
      "Pclass      418 non-null int64\n",
      "Sex         418 non-null int64\n",
      "Age         418 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Fare        418 non-null float64\n",
      "Embarked    418 non-null int64\n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 23.0 KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aec5c99308>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for missing values in testdata\n",
    "sns.heatmap(test_df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.352413</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.636159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Sex         Age       SibSp       Parch  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.383838    2.308642    0.352413   29.699118    0.523008    0.381594   \n",
       "std      0.486592    0.836071    0.477990   13.002015    1.102743    0.806057   \n",
       "min      0.000000    1.000000    0.000000    0.420000    0.000000    0.000000   \n",
       "25%      0.000000    2.000000    0.000000   22.000000    0.000000    0.000000   \n",
       "50%      0.000000    3.000000    0.000000   29.699118    0.000000    0.000000   \n",
       "75%      1.000000    3.000000    1.000000   35.000000    1.000000    0.000000   \n",
       "max      1.000000    3.000000    1.000000   80.000000    8.000000    6.000000   \n",
       "\n",
       "             Fare    Embarked  \n",
       "count  891.000000  891.000000  \n",
       "mean    32.204208    0.363636  \n",
       "std     49.693429    0.636159  \n",
       "min      0.000000    0.000000  \n",
       "25%      7.910400    0.000000  \n",
       "50%     14.454200    0.000000  \n",
       "75%     31.000000    1.000000  \n",
       "max    512.329200    2.000000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>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",
       "      <td>count</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.614378</td>\n",
       "      <td>1.133971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>12.634534</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.841115</td>\n",
       "      <td>0.580452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.471875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Sex         Age       SibSp       Parch        Fare  \\\n",
       "count  418.000000  418.000000  418.000000  418.000000  418.000000  418.000000   \n",
       "mean     2.265550    0.363636   30.272590    0.447368    0.392344   35.614378   \n",
       "std      0.841838    0.481622   12.634534    0.896760    0.981429   55.841115   \n",
       "min      1.000000    0.000000    0.170000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000    0.000000   23.000000    0.000000    0.000000    7.895800   \n",
       "50%      3.000000    0.000000   30.272590    0.000000    0.000000   14.454200   \n",
       "75%      3.000000    1.000000   35.750000    1.000000    0.000000   31.471875   \n",
       "max      3.000000    1.000000   76.000000    8.000000    9.000000  512.329200   \n",
       "\n",
       "         Embarked  \n",
       "count  418.000000  \n",
       "mean     1.133971  \n",
       "std      0.580452  \n",
       "min      0.000000  \n",
       "25%      1.000000  \n",
       "50%      1.000000  \n",
       "75%      1.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_df.drop(['Survived'], axis = 1)\n",
    "y_train = train_df['Survived']\n",
    "x_test = test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 7), (891,), (418, 7))"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "89.23"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using svm classifier\n",
    "svm = SVC()\n",
    "svm.fit(x_train, y_train)\n",
    "predict = svm.predict(x_test)\n",
    "accuracy = round(svm.score(x_train, y_train)*100,2)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}