Titanic Prediction Comparing all classification models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #linear algebra\n",
    "import pandas as pd #data processing\n",
    "\n",
    "import matplotlib.pyplot as plt #vizualisation\n",
    "import seaborn as sns#vizualisation\n",
    "\n",
    "#importing classification models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#importing dataprocessing models\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#importing model evaluation tools\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "#import time\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading datasets\n",
    "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": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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      ],
      "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": 112,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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      ],
      "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": 113,
     "metadata": {},
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    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "#here we can see that Name sex ticket cabin embarked columns are in Object datatypes\n",
    "#these are to be converted to binary numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()\n",
    "#columns Name, sex, ticket, embarked are object data types to be proceesed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping unnecessary attributes using drop function\n",
    "train_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Fare'], axis=1, inplace = True)\n",
    "test_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Fare'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 48.9+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 6 columns):\n",
      "Pclass      418 non-null int64\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 19.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding unique values in object datatype attributes by using unique() function\n",
    "#train dataset\n",
    "train_df['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'].unique()\n",
    "#embarked column is having NAN value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Pclass</th>\n",
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       "      <td>61</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
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      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "61          1       1  female  38.0      0      0      NaN\n",
       "829         1       1  female  62.0      0      0      NaN"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Embarked'].isna()]\n",
    "#there are two NAN to be replaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing NAN using replace function\n",
    "train_df['Embarked'].fillna(value = 'C', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Survived, Pclass, Sex, Age, SibSp, Parch, Embarked]\n",
       "Index: []"
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     "execution_count": 122,
     "metadata": {},
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   ],
   "source": [
    "train_df[train_df['Embarked'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting attributes with object data types to binary\n",
    "train_df['Sex'].replace({'male':0, 'female': 1}, inplace = True)\n",
    "train_df['Embarked'].replace({'S':0, 'C':1, 'Q':2}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d438d6c48>"
      ]
     },
     "execution_count": 124,
     "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": [
    "#looking for missing values by vizualization fro train data\n",
    "sns.heatmap(data = train_df.isnull()) \n",
    "#here we can see that the attribute Age is have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling missing values in Age attribute\n",
    "train_df['Age'].fillna(train_df['Age'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d43990488>"
      ]
     },
     "execution_count": 126,
     "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": [
    "# visualising train data for missing values\n",
    "sns.heatmap(train_df.isna())\n",
    "#there are no missing values in train data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 6 columns):\n",
      "Pclass      418 non-null int64\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 19.7+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male', 'female'], dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Q', 'S', 'C'], dtype=object)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting object data types to binary \n",
    "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": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d43a750c8>"
      ]
     },
     "execution_count": 131,
     "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 test data for missing values\n",
    "sns.heatmap(test_df.isna())\n",
    "#age attribute is having missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing missing values in age attribute with mean\n",
    "test_df['Age'].fillna(train_df['Age'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22d43b13588>"
      ]
     },
     "execution_count": 133,
     "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": [
    "sns.heatmap(test_df.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 6), (891,), (418, 6))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training data sets\n",
    "X_train = train_df.drop(['Survived'], axis = 1)\n",
    "Y_train = train_df['Survived']\n",
    "X_test = test_df\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('Logistic Regression: ', LogisticRegression()))\n",
    "models.append(('KNearestNeighbor: ', KNeighborsClassifier(n_neighbors = 3)))\n",
    "models.append(('decisiontree: ', DecisionTreeClassifier()))\n",
    "models.append(('SVM',  SVC( )))\n",
    "models.append(('NaiveBayes: ', GaussianNB()))\n",
    "models.append(('RandomForest: ', RandomForestClassifier(n_estimators = 5)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "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",
      "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",
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  79.46393823363253\n",
      "KNearestNeighbor:  79.46393823363253\n",
      "decisiontree:  79.46393823363253\n",
      "SVM 79.46393823363253\n",
      "NaiveBayes:  79.46393823363253\n",
      "RandomForest:  79.46393823363253\n"
     ]
    },
    {
     "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",
      "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"
     ]
    }
   ],
   "source": [
    "num_folds = 10\n",
    "results = []\n",
    "names = []\n",
    "for name,model in models:\n",
    "    kfold = KFold(n_splits=5, random_state=0)\n",
    "    start = time.time()\n",
    "    cv_results = cross_val_score(model,X_train,Y_train, cv = kfold,scoring = \"accuracy\")\n",
    "    end = time.time()\n",
    "    names.append(name)\n",
    "    results.append(cv_result)\n",
    "for i in range(len(names)):\n",
    "    print(names[i],results[i].mean()*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScaledCART: 0.784569 (0.026376) (run time: 0.262381)\n",
      "ScaledSVM: 0.828302 (0.037179) (run time: 0.395650)\n",
      "ScaledNB: 0.780062 (0.025799) (run time: 0.111455)\n",
      "ScaledKNN: 0.803645 (0.041490) (run time: 0.189688)\n",
      "scaledlogisticregression: 0.794607 (0.023097) (run time: 0.127951)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "pipelines = []\n",
    "\n",
    "pipelines.append(('ScaledCART',Pipeline([('Scaler', StandardScaler()),('CART',DecisionTreeClassifier())])))\n",
    "pipelines.append(('ScaledSVM', Pipeline([('Scaler', StandardScaler()),('SVM', SVC( ))])))\n",
    "pipelines.append(('ScaledNB', Pipeline([('Scaler', StandardScaler()),('NB', GaussianNB())])))\n",
    "pipelines.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()),('KNN', KNeighborsClassifier())])))\n",
    "pipelines.append(('scaledlogisticregression', Pipeline([('Scaler', StandardScaler()),('logregression', LogisticRegression())])))\n",
    "results = []\n",
    "names = []\n",
    "kfold = KFold(n_splits=num_folds, random_state=0)\n",
    "for name, model in pipelines:\n",
    "        start = time.time()\n",
    "        cv_results = cross_val_score(model,X_train,Y_train, cv = kfold,scoring = \"accuracy\")\n",
    "        end = time.time()\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        print( \"%s: %f (%f) (run time: %f)\" % (name, cv_results.mean(), cv_results.std(), end-start))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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