Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic- kaggle- master"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math \n",
    "import pandas as pd\n",
    "\n",
    "#Visualizing Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from IPython.display import Image,display\n",
    "\n",
    "#Warnings Dealing\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhinav Gupta\\Downloads\n"
     ]
    }
   ],
   "source": [
    "cd C:\\Users\\Abhinav Gupta\\Downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading the train and the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization (Exploratory Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *Creating a visual copy of the training set (train) to use it in* **exploratory analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "visual_df = train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *Changing survived column : 0 - Dead ; 1 - Survived*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "survive_map = {0 : 'Dead', 1 : 'Survived' }\n",
    "visual_df['Survived'] = visual_df.Survived.map(survive_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Sex Trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2dd475d08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,6])\n",
    "sns.countplot('Survived', hue = 'Sex', data = visual_df, palette= 'Blues_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   *  It is clear form the graph that female where likely to survive and most of the male passengers died\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2) Age Trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = visual_df[visual_df['Survived'] == 'Survived']['Age']\n",
    "dead = visual_df[visual_df['Survived'] == 'Dead']['Age']\n",
    "\n",
    "fig, axis = plt.subplots(nrows = 1, ncols = 2,figsize = [15,7])\n",
    "axis[0].hist(survived, bins = 30, color = 'blue')\n",
    "axis[1].hist(dead, bins = 30, color = 'red',)\n",
    "fig.suptitle('Age VS Survival')\n",
    "axis[0].set_title('Survived')\n",
    "axis[1].set_title('Dead')\n",
    "axis[0].set_xlabel('Age')\n",
    "axis[1].set_xlabel('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * As you can see in the blue graph(Survival) there is a huge number of people in the smaller age range(0-10). Hence\n",
    "     children were given a priority to be saved "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Age and Sex Trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2e0c63488>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,6])\n",
    "sns.violinplot(x = 'Survived', y='Age', hue= 'Sex',data = visual_df, palette = 'cool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://i.imgflip.com/244i5x.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image(url = 'https://i.imgflip.com/244i5x.jpg')\n",
    "display(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  It is clear from the third trend that women and children were likely to survive the titanic crash  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * *Now we know the meaning of the iconic dialogue* - **\"Women And Children First\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Pclass Trend:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x293aa60c408>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,6])\n",
    "sns.countplot('Survived', hue = 'Pclass', data = visual_df, palette= 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * People from class 3 were likely to die... And from those who survived, 1st class was the majority.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x293aa6a53c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_df.groupby('Pclass')['Fare'].mean().plot(kind= 'bar', color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * As you can see... Ticket Fare for 1st class is maximum.. Hence those who paid more survived more..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://img.ifunny.co/images/37466d0825c72a47f66db383d612bac1e22207ac4c5f403f9506adcec001e3b2_1.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = Image(url = 'https://img.ifunny.co/images/37466d0825c72a47f66db383d612bac1e22207ac4c5f403f9506adcec001e3b2_1.jpg')\n",
    "display(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  LOOL right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing (Feature Engineering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Creating A column To store the title of each name in the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  1) Name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = [train, test]\n",
    "for dataset in train_test:\n",
    "    temp = []\n",
    "    for i in dataset['Name']:\n",
    "        obj = i.split(',')[1]\n",
    "        temp.append(obj.split('.')[0].lstrip())\n",
    "        \n",
    "    dataset['Title'] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              517\n",
       "Miss            182\n",
       "Mrs             125\n",
       "Master           40\n",
       "Dr                7\n",
       "Rev               6\n",
       "Mlle              2\n",
       "Major             2\n",
       "Col               2\n",
       "Lady              1\n",
       "the Countess      1\n",
       "Ms                1\n",
       "Jonkheer          1\n",
       "Don               1\n",
       "Sir               1\n",
       "Mme               1\n",
       "Capt              1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Dr          1\n",
       "Dona        1\n",
       "Ms          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * *Now Mapping the title as:*\n",
    "  \n",
    "      **1) Mr = 0**\n",
    "      \n",
    "      **2) Miss = 1**\n",
    "      \n",
    "      **3) Mrs = 2**\n",
    "      \n",
    "      **4) Others = 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"the Countess\": 3, \n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Mme\": 3,\"Capt\": 3, \"Sir\": 3}\n",
    "\n",
    "train_test = [train, test]\n",
    "\n",
    "for dataset in train_test:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>2.0</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  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q    0.0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S    2.0  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q    0.0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S    0.0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S    2.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Title</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>0</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>2</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>1</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>2</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>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  \\\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  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * As now we have a title column... There is no need for the Name column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = [train, test]\n",
    "\n",
    "for dataset in train_test:\n",
    "    dataset.drop('Name', inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  2) Age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "Title            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * As you can see there are a lot of missing values in Age .. Hence it would make sense to replace those values according to the mean of the Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x293aa7344c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot('Age', 'Title', data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "0    32.368090\n",
       "1    21.773973\n",
       "2    35.898148\n",
       "3    20.430161\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Title')['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Hence replacing of Age with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Training Set\n",
    "for index, values in train.iterrows():\n",
    "        if(math.isnan(values['Age'])):\n",
    "            if(values['Title'] == 0):\n",
    "                train.iloc[index,4] = train.groupby('Title')['Age'].mean()[0]\n",
    "            elif(values['Title'] == 1):\n",
    "                train.iloc[index,4] = train.groupby('Title')['Age'].mean()[1]\n",
    "            elif(values['Title'] == 2):\n",
    "                train.iloc[index,4] = train.groupby('Title')['Age'].mean()[2]\n",
    "            else:\n",
    "                train.iloc[index,4] = train.groupby('Title')['Age'].mean()[3]\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Test Set\n",
    "for index, values in test.iterrows():\n",
    "        if(math.isnan(values['Age'])):\n",
    "            if(values['Title'] == 0):\n",
    "                test.iloc[index,3] = test.groupby('Title')['Age'].mean()[0]\n",
    "            elif(values['Title'] == 1):\n",
    "                test.iloc[index,3] = test.groupby('Title')['Age'].mean()[1]\n",
    "            elif(values['Title'] == 2):\n",
    "                test.iloc[index,3] = test.groupby('Title')['Age'].mean()[2]\n",
    "            else:\n",
    "                test.iloc[index,3] = test.groupby('Title')['Age'].mean()[3]\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "Title            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train.Age = train.Age.astype(int)\n",
    "test.Age = test.Age.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3) Sex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = [train , test]\n",
    "\n",
    "sex_mapping = {'male':0, 'female':1}\n",
    "for df in train_test:\n",
    "    df['Sex'] = df['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4) Embarked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = [train , test]\n",
    "\n",
    "for dataset in train_test:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n",
    "    \n",
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "\n",
    "for dataset in train_test:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    270\n",
       "1    102\n",
       "2     46\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * We still have some missing values in the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Title'].fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Fare'].fillna(train.Fare.mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * Now dropping Ticket and Cabin as they have too many missing values and of no use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test = [train, test]\n",
    "\n",
    "for dataset in train_test:\n",
    "    dataset.drop(['Ticket','Cabin'], inplace = True, axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  \\\n",
       "0            1         0       3    0   22      1      0   7.2500         0   \n",
       "1            2         1       1    1   38      1      0  71.2833         1   \n",
       "2            3         1       3    1   26      0      0   7.9250         0   \n",
       "3            4         1       1    1   35      1      0  53.1000         0   \n",
       "4            5         0       3    0   35      0      0   8.0500         0   \n",
       "\n",
       "   Title  \n",
       "0      0  \n",
       "1      2  \n",
       "2      1  \n",
       "3      2  \n",
       "4      0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  Title\n",
       "0          892       3    0   34      0      0   7.8292         2    0.0\n",
       "1          893       3    1   47      1      0   7.0000         0    2.0\n",
       "2          894       2    0   62      0      0   9.6875         2    0.0\n",
       "3          895       3    0   27      0      0   8.6625         0    0.0\n",
       "4          896       3    1   22      1      1  12.2875         0    2.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Predictive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('PassengerId', axis = 1, inplace= True)\n",
    "trainsample = train.copy()\n",
    "testsample = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = trainsample.iloc[:, 1:]\n",
    "y = trainsample.iloc[: ,0]\n",
    "X_test = testsample.iloc[: , 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * **We will encode the variable like Pclass, Embarked and title** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical features\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "columntransformer = ColumnTransformer([(\"one_hot_encoder\", OneHotEncoder(), [0,6,7])], remainder= \"passthrough\")\n",
    "X = pd.DataFrame(columntransformer.fit_transform(X))\n",
    "\n",
    "columntransformer_test = ColumnTransformer([(\"one_hot_encoder\", OneHotEncoder(), [0,6,7])], remainder= \"passthrough\")\n",
    "X_test = pd.DataFrame(columntransformer_test.fit_transform(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * Now dropping each of the first columns after **One Hot Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop([0,3,6], inplace = True, axis = 1)\n",
    "X_test.drop([0,3,6], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Remaining Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
    "X_test.columns = [0,1,2,3,4,5,6,7,8,9,10,11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "    0    1    2    3    4    5    6    7     8    9    10       11\n",
       "0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  22.0  1.0  0.0   7.2500\n",
       "1  0.0  0.0  1.0  0.0  0.0  1.0  0.0  1.0  38.0  1.0  0.0  71.2833\n",
       "2  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  26.0  0.0  0.0   7.9250\n",
       "3  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  35.0  1.0  0.0  53.1000\n",
       "4  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  35.0  0.0  0.0   8.0500"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>12.2875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "    0    1    2    3    4    5    6    7     8    9    10       11\n",
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      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Now we need to scale the data in the 8th and` the 11th columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * We will do that usin **Sklearn.preprocessing -- StandardScaler library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc1 = StandardScaler()\n",
    "sc2 = StandardScaler()\n",
    "X.iloc[:,[8,11]] = sc1.fit_transform(X.iloc[:,[8,11]])\n",
    "X_test.iloc[:,[8,11]]= sc2.fit_transform(X_test.iloc[:,[8,11]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * Now our data is ready and its time for the **Fun Part**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * **Importing Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   **We will Also run K-Fold Cross Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8058426966292135\n"
     ]
    }
   ],
   "source": [
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)\n",
    "\n",
    "classifier = KNeighborsClassifier(n_neighbors = 13)\n",
    "\n",
    "accuracies = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')\n",
    "\n",
    "print(accuracies.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8170536828963796\n"
     ]
    }
   ],
   "source": [
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)\n",
    "\n",
    "classifier = RandomForestClassifier(n_estimators = 100, max_depth= 50)\n",
    "\n",
    "accuracies = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')\n",
    "\n",
    "print(accuracies.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7688139825218477\n"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "\n",
    "accuracies = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')\n",
    "\n",
    "print(accuracies.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8305243445692883\n"
     ]
    }
   ],
   "source": [
    "classifier = SVC()\n",
    "\n",
    "accuracies = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')\n",
    "\n",
    "print(accuracies.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5) Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8137078651685392\n"
     ]
    }
   ],
   "source": [
    "classifier = LogisticRegression()\n",
    "\n",
    "accuracies = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')\n",
    "\n",
    "print(accuracies.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  **As we can see Support Vector machines gives us the best Result we will use it for training our model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = SVC()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.fit(X , y)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalanswer = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": y_pred\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <td>893</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalanswer.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Creating our final csv file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalanswer.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  This is not my best attempt.. I am soon going to apply Grid Search on the random forest algortihm to get the best value\n",
    "#### After that i am going to use Keras and Tensorflow for the Deep Learning analysis...\n",
    "# Stay Tuned... Peace:)"
   ]
  }
 ],
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