Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic EDA and ML Modeling\n",
    "\n",
    "Let's import some libraries to get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "Let's start by reading the train.csv and test.csv file into a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Let's begin some exploratory data analysis! We'll start by checking out missing data!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d45f5dc8>"
      ]
     },
     "execution_count": 5,
     "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.heatmap(train.isnull(),cbar=False,yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4dcb2c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAErCAYAAAB981BrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAY2ElEQVR4nO3de7RkVXWo8W920zwCtsEORkQBFdGoQcAXkgyVaEwkaiJKDJIML1HRkQxtQzT3ahwSYWhuvJi3BkgIYkISZRiDb0SUhyDysnkp6A3ExyVGUfCBgA3M+8fa1af24ZxuTO+1qvrs7zdGjT5Vp7tmVZ99Zq0911xrR2YiSWpj1axfgCSNiUlXkhoy6UpSQyZdSWrIpCtJDW23uW/+4qrDbW2QtFXOuunKmcT9pQc/fiZxAc6+54xY7nuOdCWpIZOuJDVk0pWkhky6ktTQZifStPLNapIDZjvRIc2KSXfkTHyqzWOsz/KCJDXkSFdSVWPs090cR7qS1JBJV5IasrwwcnYvSG2ZdEfOxKfaPMb6LC9IUkMmXUlqyPKCpKpsGetzpCtJDTnSHTm7F6S2TLojZ+KT2rK8IEkNOdIdOcsLUlsmXUlV+eHaZ9IdOX8hVJstY33WdCWpIUe6I2dNV2rLpDtyJj6pLcsLktSQI11JVXk21WfSlVSV3Qt9lhckqSFHuiNn94LUlkl35Ex8UluWFySpIUe6kqrybKrPpCupKrsX+ky6kqqa1+Q3KyZdSVU50u1zIk2SGnKkK6mqeR1xzopJd+RcHKHaLC/0mXRHbl4PTGmlsqYrSQ050pVUlWdTfSbdkbOmq9qs6fZZXpCkhhzpjty8jgaklcqRriQ15Eh35KzpSm050pWkhhzpjpyjTaktR7qS1JBJV5IaMulKUkMmXUlqyKQrSQ2ZdCWpIVvGRs7FEVJbJt2RM/FJbZl0JVXlB3ufNV1JasiR7shZ01VtbmLeZ9IduXk9MLVyeIz1mXQlVeVIt8+kK6mqeU1+s2LSHTlruqrNkW6fSXfk5vXAlFYqW8YkqSGTriQ1ZHlBUlWWsPpMuiPnRJpqcyKtz6Q7cvN6YEorlTVdSWrIpCtJDZl0Jakhk64kNWTSlaSGTLqS1JBJV5Iask935FwcIbVl0h05E5/UluUFSWrIka6kqjyb6nOkK0kNOdIdOSfSVJu7jPWZdEduXg9MaaWyvCBJDTnSlVSVZ1N9Jt2Rs6ar2qzp9llekKSGHOmO3LyOBqSVypGuJDVk0pWkhiwvSKrKElafSVdSdbPoYJjXZG/SlVTdvCbAWTDpjpx9uqrNPt0+k+7IzeuBKa1Udi9IUkMmXUlqyPLCyFnTldoy6Y6ciU9qy/KCJDVk0pWkhiwvjJw1Xaktk+7ImfiktiwvSFJDJl1JasikK0kNWdOVVJXzBn0mXUlVuctYn+UFSWrIpCtJDVleGDkXR0htmXRHzsSn2jzG+ky6kqpyIq3PpCupqnlNfrNi0pVUlSPdPrsXJKkhk64kNWTSlaSGTLqS1JATaSPn4gipLZPuyJn4pLYsL0hSQ450JVXl2VSfSVdSVS6O6DPpjpwTaVJbJt2RM/GpNo+xPpOupKosL/SZdEfO8oLUlkl35Ex8Ulv26UpSQyZdSWrI8sLIWdOV2jLpjpyJT2rL8oIkNWTSlaSGTLqS1JA13ZFzIk1qy5GuJDXkSHfkHG2qNo+xPke6ktSQI92Rs6ar2txlrM+kO3LzemBKK5XlBUlqyKQrSQ2ZdCWpIZOuJDVk0pWkhuxekFSVHTJ9Jt2Rs09Xtdmn22fSHbl5PTCllcqkK6kqP9j7TLqSqrK80GfSHTlrulJbJt2RM/FJbZl05emfqvLn3GfSlb8UqsoP9T6T7shZ05XaMumOnIlPtXmM9bn3giQ15EhXUlXWdPtMuiNnTVdqy6Q7ciY+1eYx1mdNV5IacqQrqSprun2OdCWpIUe6kqqa1xHnrJh0JVVleaHPpCupqnlNfrNi0pVUlSPdPifSJKkhR7qSqprXEeesmHQlVWV5oc+kK6mqeU1+s2LSlVSVI90+k+7IucuY1JZJd+RMfFJbJl1JVfnB3mfSlVSVNd0+F0dIUkMmXUlqyKQrSQ2ZdCWpIZOuJDVk94Kkqua1i2BWTLoj54o01WbLWJ9Jd+Tm9cCUViprupLUkCNdSVV5NtVn0h05a7qqzZpun0l35Ob1wJRWKmu6ktSQI11JVXk21edIV5IacqQrqSon0vpMupKqmtfkNysmXUlVOdLts6YrSQ2ZdCWpIZOuJDVk0pWkhky6ktSQ3QuSqprXLoJZMelKqsqWsT6TrqSq5jX5zYo1XUlqyJGupKosL/Q50pWkhky6ktSQ5QVJVc3raf6smHQlVWVNt8+kO3JeDVhqy5quJDXkSHfkHG1KbZl0JVXlB3ufSXfkrOmqNifS+qzpSlJLmVnlBhxd67nnNbbveRyxxxbX9zzsreZI9+iKzz2vsX3P44g9trizjL3i3rPlBUlqyKQrSQ3VTLonV3zueY3tex5H7LHFnWXsFfeeoysYS5IasLwgSQ2ZdCWpIZOuJDVk0pXmVEScc18e07ZlkL0XIuIBm/t+Zn5niDhbeA2PAL6emXdGxDOA/YD3ZOatleP+NPA24MGZ+ZyIeAzw1Mw8pXLc44G3ZOZd3f21wF9k5lGV487k/U7FfxDwZCCBSzPzGy3idrH3APZi6vcmM8+vEGdH4CeAn4qIXYHovrUWePDQ8TbzOgI4Enh4Zh4XEXsCD8rMSyrH3Q14BbA3/f/r364Y80OUY2pJmfn8oWINNdK9HLis+/NbwJeAL3dfXz5QjC15P3B3ROwDnAI8DPinBnHfDZzFwi/Dl4DXNoi7HfC5iNgvIp4NXEqb/+t3M5v3S0S8HLgEOAx4EXBxRFT7RVwU+0+AC4E3Aa/vbq+rFO6VlJ/lo4Eruq8vB84E3lkp5lLeBTwVOKK7//1G8c8E7g98EvjI1K2mE4B3ADcCtwN/291+AFwzaKSB1yqfCBw6df85wDsarZO+ovvz9cCru68/3yDupYtjARsavedndQfITcA+jWLO8v1eD6ybur8OuL5h7B1axJqK+eqW8ZaIP/mdmv5ZX9kgbpPjaZnY59+Xx7bmNnRN90mZ+dHJncz8GPD0gWMsZ2NEHAG8FPhw99iaBnFvi4h1dKcmEXEQ8N3aQSPiacBfAMcB5wJ/HREtTj1n8n47X6eMtia+D3ytUewbaHM8Tfv7iHhTRJwMEBGPjIjnNoy/MSJWs/Cz3g24p0HcD0fEoQ3iLGW3iHj45E5EPAzYbcgAQ++ne3NEvAn4R8oP6jeBbw8cYzlHAa8C3pqZN3b/Wf/YIO4xwAeBR0TEhZQf0IsaxD0BODwzvwAQEYcBn6KcktY0q/cL8P8oJZUzKcfXrwKXRMQxAJn5p0MHjIi/6mL9ENjQTWTdOfl+Zr5m6JhT/p5SVji4u/914AwWBhW1/SXwAeCBEfFWys/5TQ3irgfeGBF3AhspNe3MzLUNYv8ecG5E3NDd35tS7hnMoCvSugm1Y4GndQ+dT5nsqT6Rtuh17Ao8NDOvahRvO+BRlIPj+szc2CDm6sy8e9Fj6zKz+ofcLN5vF/fYzX0/M99SIeZLtxDztKFjTsW+LDOfGBGfz8wDuseuzMxmu3NHxKOBZ1J+1udk5hdbxZ6ViNiBhcHLdZl55+b+/o/9/EMm3VmKiHOB51NG7xsok3jnZeYxleOuBn6Fe8+0Dj7qWhR30kWwR2b+csOuicOWePi7wNWZ+c2asRe9jl2BW7PRARwROwN3TD7oup/7Dpn5w4oxL6IkvAsz88CuQ+efM/PJtWJOxV4FXJWZj6sdayrmozPzuog4cKnvZ+YVDV7DT1DO5vbKzFdExCOBR2XmYGcXQ7WMNWu32Iz7Z+b3uhnuUzPz2IhoMdL9EHAHcDVt6l0T7wZOBf6wu/8l4L2Uzo2aXkaZ0f50d/8ZwMXAvhFxXGb+w9ABI+LNwPu6X8gdgI8B+wN3RcRLMvOTQ8dcwjmUicsfdPd3Aj7Bwql/DccCHwceGhGnAz8H/I+K8TbJzHsi4sqI2DMzv9oiJiXZHU3pIrjXSwJ+ocFrOJVS0nlqd3/wks5QNd0TBnqerbFdROwO/DoLiaiFh2Tmfg3jTfxUZr4vIt4AkJl3RcTdW/pHA7gH+JnM/C/YNOL+G+AplHLS4EkXeDFwfPf1SymtjrsB+wKnUVqLatsxMycJl8z8QTcqqiYzz46IK4CDKKf36zPz5poxF9kduDYiLgFum3pdVQZRmXl09+chNZ7/PnpEZr64m5QnM2/v+pUHM0jSzczzutOt0zLzN4d4zv+G4yj9o5/JzEu7GcgvN4j7sYh4dmZ+okGsabPqIth7knA73wT2zczvRESt2u6PpsoIv0Q5xb4b+GJXX27htog4cHKKGxFPoLTrVdOdObyZrkc1IlZFxOmZeWTNuFMGr5HfF93ikN8Bfp5yfF8AnJiZdzQI/6OI2ImF36tHMDVxOoTBDtjMvDsidouI7TPzR0M9748R/wzKacDk/g3ACxuEvhj4QFcDaznTOqsuggsi4sMs/F+/EDi/q3nWWv13Z0Q8Dvgv4BD6ixKqjjanrAfOiIibuvu7U0bgNe0ZEW/IzD/uyipnUBZLNJGZ57WKtch7KO2Af9XdP4JyBnV4g9jVSzpDdy+cBBxISQbTpyNVJ5W62DtS6o2PBXacil11xVLXWvJrlImk6pM6EfEk4GuZ+Y1ulPdKSuL7AvDm2p0i3anWYZRRCJSWwN0z83crxnwKpYywG/DnmXl89/ihwG9l5hGb+/cDxF9FOcW/lIWujetqd210/9enU+YLDgE+lpl/VjPmovgHURLfzwDbA6uB22oPKJbq0GjZtdGdQU5KOhcPXdIZenHETZSC8yrgflO3Fv4BeBDl9PM84CH0G+lr+TJwTatZdOAkYHImcTClfv1O4BYa7LLfvc9/p4zqX0CZXa/aRpSZn8vMR2fmuknC7R7/aO2E28W5h7KycmNmXpOZV9dMuBFxYDeDfwBlAcyLKcfZecvN7Ffy15RR5pcpE4cv7x6r7fNdwgc2fehe2CDupKTz7cz8SNex8J1uxDtcjBq5IiJ2zszbtvw3B435+cw8ICKuysz9ImINcFZmVp3xjIh3Aw+nzKhPN81XGd1Pf+JHxDuBb2XmH3X3N2Tm/pXi7gv8BuWX8NuUTonXZeZeNeIt8xrWUU7/JrW+zwDHNepNfgtwFfCvtT9gI+LTm/l21j6mp17HpE/4qslkcURclJlVOjYi4mrKz3UN5Yziq939vYAvtGhf636fr19c0pn8jg1h0EmIiHgqpWVpF0o96vHAKzPzd4aMs4zJyOPWrv73DUrvbG03drftu1ttqyNiuyy7iz2T/mWia04qXUeZ0HheZv5fgIj4vYrxlvIvlA6JSa3+SEryf1aD2McAO1Pa1O6gYu1+xrP3034YEdtTVuK9HfhPyv9BLS2XOC/nKOD0riuoSkln6Jru5yiTOR+cWkFzTaNPqJdTdhrbj9Jrtwulxnli7dgtRcQfAocCNwN7AgdmZkbZXe20zPy5SnFfQBnpHkyZaPgX4O8y82E14i3zGi7PzCcseuyyzHxiq9fQUkS8DXh7dtuTdgtCfj8zWyzFJSL2okxebk9ZHnt/4F2TD90G8R9If36mWr/worLNGkoZ70K6vvchF2YMnnQz8ykxw2WLrUXZBOQPuPcEXrVTwK7etTvwiUkZpzv932XIg2OZ2DtTJg6PoDSrnwZ8oEXLXEScQNlC9H3dQy8CHpuZm10ePGD8XYFH0v85D76f7lS8Tb9HU49dkZlV67qNF0QsFf/5lAUSD6a0JO4FfDEzH1sxZrOSztCno1+LiIOB7E5LXkPlSZboNjtZToPOidMpp7jPpWy481LKEuRqMvPiJR77Us2YU3Fuo7zn06PstXE48L8oq7OqiIjvU2p7QTnNnyzAWE1ZIVY96XZnUuspE7QbKLPbn6XuKqnVEbFDdmv/u/7RHSrGm/g3ShcSEfH+zGzRejnteMr/7ye7eZpDWNjTt4rMPKTrUjk8M99bM9bQ3QuvAn4X2IOyfG7/7n5N99vCrbZ1WfY72JiZ53Utagdt6R+tBJn5ncw8qfbETmbeLzPXdn+uysw13W1Vg37oifXAk4CvdDXXA6j84UrZJe+ciHhZlM3az6acWdQ2vQLr4cv+rXo2dpOjqyJiVWZ+mpJLquq6VGrnq2FHul0/W6vVMpOYM1k1M2UygfefEfErlLa5h8zw9aw4MQcboVA2u7kjIuhGn9dFxKNqBszMt3cz+pNdvo7PzLNqxpyEXubrVm6NiF0ok6anR8Q3gbsaxT47Il5HOXudXmswWP/70DXdv1zi4e8Cl2XmmYMFWjr2aZS16dOTDu/I+osjnkuZ1X8opZF8LWU7yw/WjDsmEXFyZh69qO626cBt0UIVER+gzGy/llJSuAVYk5mz2my7mih7eNxGSfQ7UfYShsqrLbvJ4J+mlG9up5yJH0mp6X4kM6tfjioiblzi4czMwUb8Qyfdkyn7UE4vEb2WkpBuyMxq19JaZtLhXo9p2xMRTwa+mt1FKKPscftC4D+APxpyFHIfX8/TKTP5H88KS94j4jOZ+fNTtexN36LdZt7NdcvL35iL9sGOiCcCx2bm82bzyoY19ETaPsAv5MIVav+GMsHyi5SljDWtiohdM/OWLvYDqNi3GmW7weVkTq2c0lY7ka4XN8pliv4YeDWlzncyFfec6JaXv4pybF8NnJL19yTYGUotu3KcebP34oQLkJmXRcTerV5E1+f/GPpdKu8Z6vmHTkp7UA6YyW5XO1Mu1X13lEtv1PQO4LMRcQZldPDrwFsrxltqxd3OlP0f1rGwFaG23uqp0eyLgZMz8/3A+yNiQ+XYp1Hq9hdQLrT6GMqkWk0r48oCP74dN/O9nVq8gChXJ3kG5ef8UcrP/DOUTXgGMXTSfTtl9cq5lFOhpwFv63o7q+55mpnviYjLKPW2AA7L7vphleJt2mg5Iu5H+UU8irJoYKlNmPXfN6tVeACPycyfBYiIUyiXgK/tgZtrhWzQBjkrl0bEKzLzb6cfjIiXUTYWb+FFwOMpV0A+Ksp+0X83ZIChuxdOiYiPAk+mJL43ZuZkK7zXDxlrYonTvxMn5Y3auhLGMZRi/2mU1WG3tIg9Mv9M2ezlZsoEywWwaeKl9h7Cmza2ybJRfOVwQOk/3oV+69YYvJayTeqRLCTZJ1JWxL2g0Wu4PctVM+6KiLWUxRmDts0NvuFNROxBmW2cvl5YzVU776V/+vcfNSfspuL+H8oWhycD78ypqwpoeLNahTc1kw/92fxqk1otVp3Ns24xxGTrgGsz81MNY78LeCNlyfvvUxbfbMjMowaLMXD3wp9Qam7XsnC9sMyK10iLiKunTv+2Ay5pccBGxD2UXcXuYkQzzKrPrpv50E3erV1qcm9rDF0P+zXKlTNrT5pNm8XpH5k59Go+aeKZs34BYxblitfT24fOddK9gbJDT8uk+/iI+F73dQA7dfcdcWqb1LrvWAu68sI+lHkEgFdGxLNywCujDJ10f0jpXjiH/oberxk4ziaZubrWc0sanacDj8uu7tqtdB10jcHQSfeD3U2StkXXU/ap/kp3/6EMXF6o0b2wE7BnZl4/6BNLUiUR8SFKDff+lN3kLunuPwW4KDMHuzrJ0JfreR5wAqWv7mERsT/lGlbVuhckaQAntAo0dMvY5ZQVYefmwpUjNrV0SdK2oFsYMb3WYLDJzaFrundl5ncXtW2NdR25pG1MRBxN2Tfldspag6DksMFWpQ2ddK+JiJdQ1so/knK5nosGjiFJtbyect29m2sFGLrB/9WUCzTeSelz+x5lPbUkbQv+nYVN26sYvHth0xNHrAZ2zszvbfEvS9IciIgDgFOBz1FprcGgI92I+KeIWNtt5XgtcH1EVNldTJIqOAn4FHAxZaezyW0wQ3cvbMjM/but2Z4A/E/g8szcb7AgklRJRFyUmQfXjDF0TXdNRKyhbHxzZmZuxO4FSduOT0fE0RGxe0Q8YHIbMsDQ3QsnUS4WeCVwfkTsRZlMk6RtwUu6P98w9digLWPVJtI2BVi4zIokjd7QE2nru4m0iIhTIuIKygo1SZpbEfEHU18fvuh7bxsy1tA13d/uWsSeDexGuVDj/x44hiQN7Temvn7Dou/98pCBhk66k/W/hwKnZuaVU49J0ryKZb5e6v5WGTrpXh4Rn6Ak3bO6S5Pfs4V/I0mzlst8vdT9rTJ0n+4qYH/ghsy8NSLWAXsMfWE3SRrS1FWfp6/4THd/x8xcM1SsQVvGuuvF3wjsGxE7DvncklRLy8t+Db2J+cuB9cBDgA3AQcBnsYNBkoDha7rrKZe6+EpmHgIcAHxr4BiStM0aOunekZl3AETEDpl5HfCogWNI0jZr6GXAX4+InwT+DTg7Im4Bbho4hiRts2rup/t0ypU1P56ZP6oSRJK2MYMk3a5T4VXAPsDVwCnutyBJ9zZU0n0vsBG4AHgOZSJt/VY/sSStMEMl3U2XWY+I7YBLMvPArX5iSVphhupe2Dj5wrKCJC1vqJHuZAkd9JfRBZCZuXarg0jSClB9E3NJ0oKhF0dIkjbDpCtJDZl0Jakhk64kNfT/ATarAw7SkDp+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(),cbar=False,yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small enough for reasonable replacement with some form of imputation. Looking at the Cabin column, it looks like we are just missing too much of that data to do something useful with at a basic level. We'll probably drop this later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's continue on by visualizing some more of the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4dd4c48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4eaec88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train,hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4f25f08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train,hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4f87a08>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train,hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d4ffac48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(),kde=False,color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d50b86c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d514dc88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(color='green',bins=40,figsize=(8,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d5213608>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "We want to fill in missing age data instead of just dropping the missing age data rows. One way to do this is by filling in the mean age of all the passengers.\n",
    "\n",
    "check the average age by passenger class, For example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>461.597222</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>38.233441</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>84.154687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>445.956522</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>29.877630</td>\n",
       "      <td>0.402174</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>20.662183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>439.154786</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>25.140620</td>\n",
       "      <td>0.615071</td>\n",
       "      <td>0.393075</td>\n",
       "      <td>13.675550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId  Survived        Age     SibSp     Parch       Fare\n",
       "Pclass                                                                 \n",
       "1        461.597222  0.629630  38.233441  0.416667  0.356481  84.154687\n",
       "2        445.956522  0.472826  29.877630  0.402174  0.380435  20.662183\n",
       "3        439.154786  0.242363  25.140620  0.615071  0.393075  13.675550"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_group=train.groupby('Pclass')\n",
    "class_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 38.2\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29.8\n",
    "\n",
    "        else:\n",
    "            return 25.1\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now apply that function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check that heat map again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c5d549e0c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),cbar=False,yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's go ahead and drop the Cabin column and the row in Embarked that is NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat the same procedure for test data also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1098.224299</td>\n",
       "      <td>40.918367</td>\n",
       "      <td>0.476636</td>\n",
       "      <td>0.383178</td>\n",
       "      <td>94.280297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1117.935484</td>\n",
       "      <td>28.777500</td>\n",
       "      <td>0.376344</td>\n",
       "      <td>0.344086</td>\n",
       "      <td>22.202104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1094.178899</td>\n",
       "      <td>24.027945</td>\n",
       "      <td>0.463303</td>\n",
       "      <td>0.417431</td>\n",
       "      <td>12.459678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId        Age     SibSp     Parch       Fare\n",
       "Pclass                                                       \n",
       "1       1098.224299  40.918367  0.476636  0.383178  94.280297\n",
       "2       1117.935484  28.777500  0.376344  0.344086  22.202104\n",
       "3       1094.178899  24.027945  0.463303  0.417431  12.459678"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_group=test.groupby('Pclass')\n",
    "class_group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 40.6\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 28.8\n",
    "\n",
    "        else:\n",
    "            return 24.3\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Age'] = test[['Age','Pclass']].apply(fill_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n",
       "152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n",
       "\n",
       "     Fare Embarked  \n",
       "152   NaN        S  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['Fare'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.fillna(value=12.4,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          418 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         418 non-null    float64\n",
      " 9   Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 32.8+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Categorical Features \n",
    "\n",
    "We'll need to convert categorical features to dummy variables using pandas! Otherwise our machine learning algorithm won't be able to directly take in those features as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Sex']=train['Sex'].map({'female':0, 'male':1}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Embarked'] = train['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Sex']=test['Sex'].map({'female':0, 'male':1}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Embarked'] = test['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Our data is ready for our model!\n",
    "\n",
    "# Building Machine Learning models\n",
    "\n",
    "Let's start by splitting our data into a training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=train.drop('Survived',axis=1)\n",
    "y_train=train['Survived']\n",
    "x_test=test.drop('PassengerId',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel=LogisticRegression(max_iter=1000)\n",
    "logmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8076490438695163"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tpredictions=dtree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9820022497187851"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'PassengerId': test['PassengerId'],\n",
    "                  'Survived': Tpredictions  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('titanic_predictions.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}