Titanic-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')"
   ]
  },
  {
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "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": [
    "# get some information on the dataset to see how many cateogorical columns and how many numerical columns are there\n",
    "\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let us first check the count of how many survived. \n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(train['Survived'])\n",
    "plt.title('Count plot for Survived', size = 20)\n",
    "plt.text(x=-0.01, y =250, s = round(train['Survived'].value_counts(normalize = True)[0]*100,2))\n",
    "plt.text(x=0.95, y =150, s = round(train['Survived'].value_counts(normalize = True)[1]*100,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see 61 % percent the total population did not survive and 38% did survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of the survived and non survived, how many of them are male amd female\n",
    "plt.figure(figsize=(10,5))\n",
    "plot = sns.countplot(train['Survived'], hue = train['Sex'])\n",
    "plt.title('Count plot for Survived (Female/Male)', size = 20)\n",
    "for p in plot.patches:\n",
    "    txt = str(p.get_height().round(2))\n",
    "    txt_x = p.get_x() \n",
    "    txt_y = p.get_height()\n",
    "    plot.text(txt_x,txt_y,txt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looks like males had a higher mortality rate than that of the females. Also, the number of females who survived are more than the number of males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Now lets look at the passenger class feature and its relation with survive and sex\n",
    "plt.figure(figsize=(10,5))\n",
    "plot = sns.countplot(train['Survived'],hue = train['Pclass'])\n",
    "plt.title('Count plot for Survived (Pclass)', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The lowest class which is pclass 3 has a higher mortality which is why the survive count is less and also is exactly opposite to that of class 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAJkCAYAAAC75O2BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVxU9f7H8fcAiqKpaZCpqGWN5YZL5RpmuRsmtFwyxTLXm3XLsnJt0RbNq+WumVZuiZW5lFBmeV1S1Motl5vJot0Q8YoiggLn94e/mevIIhjjfIXX8/HokefMlzOfORxm3vM93/M9NsuyLAEAAMDjvDxdAAAAAC4gmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmBWwk2dOlV169bN8V/Tpk3Vo0cPffDBBzp//rynyywQx2tZu3btFW9j48aN2rVrV5G3xbWrKI4r08TGxqpPnz5q0qSJmjZtqlmzZuXZNrf3h9tvv12NGjVShw4d9Oqrr+rPP/+8ojruu+8+3XnnnVf6MoqtO++8U/fdd1+B2s6ZM0f33nuvGjRooHvuuUdpaWlurg7u5uPpAmCG+++/X3fccYckKSsrS6mpqdq+fbsmTpyonTt3atq0aR6u0P0WL16s119/XdOnTy/Stri23X333RoyZIhuvvlmT5dSZF5++WX98ssv6tChg2655ZbLhqPrrrtOffr0cS5blqWzZ89q586d+vTTT/Xdd9/ps88+U9WqVd1dOi6yYcMG/fOf/1RAQIAiIiLk6+srPz8/T5eFv4hgBklS+/btFRYW5rLOsiwNHjxY3377rX788Ue1bNnSQ9VdHcnJyW5pi2tb8+bN1bx5c0+XUaT27t2rqlWrFvgLV4UKFfTMM8/k+tiYMWO0dOlSTZ06VW+++WZRlonL+PXXXyVJzz77rB555BEPV4OiwqlM5MlmsznD2rZt2zxcDYCicv78eVWqVKlItvXUU09JktavX18k20PBnTt3TpJ0/fXXe7gSFCWCGfLl7e0tSSpdunSOx77++muFh4ercePGatKkicLDw/XVV1+5tBk5cqTq1q2rt99+22X99u3bdccddygkJMT55pIbx/ieAwcOaNy4cWrRooWaNWumJ554Qjt27CjQa9i1a5f+/ve/q3nz5mrYsKG6du2qWbNmuTxv7969nb0HTz/9tOrWrZvn9vJrm52drcWLF6tHjx5q1KiRmjVrpieffFKbNm0qUK2SdOLECb311lu677771KhRI3Xq1EmTJ0/WmTNnXNodO3ZMY8aMUdu2bdWgQQO1bdtWY8aM0bFjx1zaOfZhbGysJkyYoDZt2igoKEjh4eHavXu3srOz9cEHH+i+++5T48aN9fDDD2vr1q05XnNwcLCOHj2qQYMGqUmTJmrVqpWGDRumP/74I8drOHjwoIYNG+asrWnTpgoPD1d0dLRLu1deeUV169bVrl271LVrVzVs2FDh4eFy3CkuLi5OL774olq1aqUGDRqoS5cumj17doHGPW7dulV169bVsmXLtGjRIrVv315BQUHq3r27vvjii1zbLl68WEOHDlWjRo3Upk0b7dixI88xZtu2bdPAgQPVvHlzNWvWTOHh4bmOQ9u7d6/z+GvUqJEefPBBLVmyRJfeDe/48eMaMWKEOnTooIYNG6pNmzYaNmyY4uLiLvtapQsf0rNmzVLXrl3VoEEDNW/eXIMHD9bu3budbRyvRZL279+vunXrFngsU15uvPFGSdLJkydd1h85ckQjR45UcHCwgoKC1K1bN82bN++yv7szZ85o+vTpevDBB9WkSRM1bNhQHTt21IQJE3KMn9q9e7cGDhyoNm3aqGHDhurUqZMmTpyo1NTUK2qXl4Iez479e+jQIU2aNMk59qtbt25asmRJju2eOHFCr7/+uu655x4FBQXpiSee0IEDBwpUU926dXO8DzmO6/Pnz+vjjz/Wo48+qmbNmqlBgwZq166dxowZoxMnTji3ceTIEdWtW1fvv/++xo0bp8aNG6t58+Zas2aNpAtnTZYsWaLQ0FA1atRId911lwYNGuTsqYN7cCoTebIsS8uXL5e3t7fat2/v8tj48eM1b948+fv764EHHpAk/fDDDxo6dKh+/fVXDRs2TJI0fPhwbdy4UQsWLFCPHj10xx13KC0tTcOHD5e3t7cmTJiQa+i71PDhw5WQkKCQkBCdOXNGUVFR6tOnj2bNmqU2bdrk+XNr167VP/7xD3l5eal9+/a64YYbtGXLFk2ePFkbNmzQ/PnzVbp0aYWGhkqSYmJi1LVrV91yyy15bjOvttnZ2Xr++ecVFRWlwMBAPfTQQ0pLS9N3332np556SqNHj9bjjz+e7+tMSkrS3/72Nx09elTNmzdXp06d9Ouvv2rWrFnauXOn5s6dKx8fH8XHx+uxxx7T8ePH1apVK3Xp0kUHDhzQ0qVLtW7dOi1ZskSBgYEu237uueeUkpKibt266T//+Y+io6PVr18/3Xffffrhhx/UqVMnZWRkaOXKlRo0aJCioqKcH7qSlJ6eroiICPn4+Cg8PFyHDh3SypUrtXXrVi1btszZdteuXerdu7dKly6tjh07qnLlyoqLi9N3332nZ599VrNmzVK7du1cahs8eLAaNmyo1q1by8/PTzabTXv37lWfPn2Unp6ujh07qlq1atq+fbsmTZqkbdu2afbs2c4vDvlZsmSJ9u/fry5duqhixYpau3athg8frqNHj+Y4PTd9+nT5+fmpV69e+u2331SvXj1t3rw5xzZXrFih4cOHq0yZMrr//vt1/fXXKzo6Wk8//bTefPNNPfzww5Iu9CINGTJEpUqVcu6LDRs26LXXXtOvv/6qsWPHSpIyMjLUv39/HTx4UB06dFDnzp0VHx+vr776Shs3btSaNWvy7eHKyMjQk08+qR07dshutzuPjbVr12rDhg1677331L59e+d4uWnTpumGG25QeHi4rrvuusvuw/w4guPFx8rBgwfVu3dvpaSk6N5779XNN9+smJgYjR8/XgcOHND48eNz3VZmZqaefPJJ7dq1S23atFGbNm105swZrVu3Th9++KGOHDmiKVOmSJIOHz6sJ598Ul5eXurcubMqVKign3/+WR988IF2796tjz/+uFDt8nIlx7PjC0vHjh3l4+OjlStX6rXXXpOfn58efPBBSRcCaK9evXTo0CG1bNlSdrtdMTEx6t27t9LT01WhQoV86xoyZIhiYmJc3occ44RfeOEFRUdHq1mzZnr00Ud17tw5bdy4UUuXLtXevXv1+eefu2wrMjJSkvTYY4/p999/V+PGjSVdGIu4YsUK3XbbbQoPD9fZs2e1Zs0ahYeHa/bs2cV+eIvHWCjRpkyZYtntdmvw4MHWlClTrClTpljvv/++9dZbb1k9evSw6tevby1evNjlZ7Zt22bZ7XarR48eVnJysnN9cnKy9cADD1h2u92KiYlxrl+/fr1lt9utRx55xMrOzrbeeOMNy263W7Nnzy5wfU2bNrXi4uKc63/55RerXr161v33329lZma6tP32228ty7Ks06dPW3fddZfVtGlTa8+ePc6fPX/+vPXCCy9YdrvdmjZtWo7ncvx8Qeq6uO3y5cstu91u9e3b1zpz5oxzfXx8vNW6dWurXr16Vnx8fL7bHTZsmGW326358+e7rB89erRlt9ut6Ohoy7IsKyIiwrLb7VZkZKRLu0WLFll2u92KiIjIUWu7du2slJQU5/qhQ4c69+2ff/7pXD916lTLbrdbixYtcq7r1auXZbfbrYceesg6e/asc/2HH35o2e126+WXX3au69u3r1WvXj3rt99+c6ntq6++sux2uzV06FDnupdfftmy2+3WkCFDXNpmZ2dbDzzwgNWwYUNr9+7dLo+99dZblt1utxYuXJj7Tvx/W7Zssex2u2W32601a9Y41x8/fty6//77rXr16lmHDx92aRsUFGQdO3bMZTuX/q5PnjxpNWvWzGrRooX1+++/O9slJydbbdq0se6++27r3LlzVlpamtWiRQurRYsWVkJCgrNdVlaW9cwzz1h2u9364YcfLMuyrHXr1ll2u916//33XZ577ty5BXqt06ZNs+x2u/XKK69Y58+fd67fvXu31ahRI+vOO++0Tp8+7Vxvt9ut7t2757vNi9u2a9cu18eysrKsIUOGWHa73XrnnXec63v27GnVrVvXebxa1oXfad++fS273e78e2zXrp3VrFkzZ5vVq1dbdrvdmjRpksvznD592mrVqpV1xx13WGlpaZZlWdY777xj2e1268cff3RpO2DAAMtut1sHDx4sVLu8FOZ4vvhv7eL3xh07dlh2u93q2bOnc937779v2e12a+rUqc5158+fd/5d5rXPL5bb+9DPP/9s2e1264UXXnBpe/78eef7s+O4TUhIsOx2u1W3bl1r3759Lu2//vpr5+u7+JiKj4+37r77buuee+6xMjIyLlsjCo9TmZAkfffdd5o2bZqmTZum6dOn66OPPtKvv/4qPz8/nTp1SllZWc62ju7yl156SZUrV3aur1y5sl544QVJcvlGFhwcrLCwMO3cuVOvvvqqFi1apCZNmjjHphREr169VLNmTedyUFCQunbtqoSEBP3yyy+5/szatWuVkpKiiIgI1a9f37nex8dHI0aMUJkyZXJ8c/wrli9fLknOb8YOgYGBGjx4sDIzM/Xll1/m+fPnzp3Tt99+q9q1a+uJJ55weWzgwIEaNGiQ/P399eeff2rLli268847cwz47dmzpxo2bKgtW7boyJEjLo+FhYW5fAtv2rSpJKlbt24uvR2NGjWSJB09ejRHjUOHDlWZMmWcy3369FH16tUVHR3tPDX8xBNP6N1331WdOnVcftYxgD63Cyc6derksrxz504dPHhQDz/8sBo0aODy2D/+8Q+VKlUqx+nIvDRp0kSdO3d2LlepUkUDBw5UZmam85SNQ7NmzeTv75/v9tavX6/Tp0+rT58+LldqVq5cWcOHD1e/fv2UlpamdevW6cSJE+rXr59q1KjhbOfl5ZXj7yQ7O1vShcHc6enpzrY9e/bUDz/8oJ49e+Zb0/Lly1W2bFmNHDlSPj7/OxHSoEED9ezZU6dOndI333yT7zbyc+rUKU2dOtX535QpUzRu3Dg98MAD+uabb1S7dm0NHjxYkvTnn39q+/btatWqlTp27Ojchs1m09ChQzVkyJA8e8nr1auncePG5Tj+y5cvr3r16ikrK0spKSmS/rfPfvrpJ5e2b7/9tn788UfddttthWqXlys5nh966CGX98amTZuqQoUKio2Nda776quvVKFCBQ0cONC5zsfHRy+//LJsNlu+NeWnatWqeuedd/SPf/zDZb2Pj4+aNWuWa821atXS7bff7rLus88+k6Qcx1RgYKDCw8OVmJiYa28y/jpOZULShTepi6/KTEtL0++//64pU6Zo0qRJio2NdY4T279/v7y8vJx/5BdzrNu/f7/L+uHDh2vTpk1aunSp/Pz8NGHChAKdhnK4++67c6xr1KiRVq5cqf379+dai6OGu+66K8djlStX1s0336x9+/bp9OnTf/l0juP5brzxxhynEKW898vF4uPjlZaW5jyNcLHq1avr+eeflyStW7dOkvKc4qBp06bavXu39u/f7xIILg62klS2bFlJcmkjSb6+vpKUY+yfzWbL8Zze3t6qX7++vvnmG8XHx+vWW2/VPffcI+nCadn9+/crPj5ehw8fdo4JvDjkX/z6LrZ3715JF/bJ1KlTc7QvV66cDhw4IMuyLvshltsVlY7weenv49I6cuP4mdx+T127dnX+e8+ePZIuvJbcXoO3t7dzW61atVJgYKC+//57tW7dWq1atVJwcLDuvfde3XTTTfnWk5qaqoSEBDVt2lTly5fP8XizZs00b968fI+9yzl9+rTLFZw2m01+fn6qWbOmBg0apL59+zpDv2OMVG77p379+i5fki5188036+abb1ZGRoZ27typw4cPKz4+Xnv37lVMTIyk/x0/oaGhWrJkid5//30tXbpUwcHBCg4Odp4Odyhou7xcyfGc29Qq5cuXd45pS09PV2xsrO6++26VKlXKpV1AQIBq1KjhDJSFVbVqVYWGhiozM1N79+517sN9+/Y5g9Sl2770PUC6cNz6+vpq0aJFOR47fPiwJGnfvn269957r6hO5I1ghlz5+fmpQYMGmjZtmtq3b68vvvhC/fv31y233KLU1FT5+vrm+q33uuuuU9myZXX27FmX9RUqVFCLFi20YsUK3XTTTZf9sLnUxT06DjfccIMk5TmA17E+tw8r6cIb4L59+3T27NkiCWapqanOmnJ7LkkuvSGXcvQE5FXvxc8jKc+a83quvD6ECjLGT7pw5VdubR2v+fTp05Kk//znPxo7dqzWrVsny7Lk5eWl2rVrq1mzZnkOGr64F0660EMjXZinacOGDXnWdObMmcvuL8f+uJijV+zSY8cRSvPjqO1yz+vYH5deEHMxx++8bNmyioyM1MyZM7VmzRp98803+uabb+Tl5aUOHTrojTfeyHOMmeOikMIeD4VRvXp15xeCyynocZyb7OxszZ49W/Pnz3dup0qVKmrSpImqV6+uQ4cOOS+auP322xUZGalZs2Zp/fr1ioyMVGRkpPz8/BQREaHnnntONputwO3yciXHc25/JzabzVm747WVK1cu15+vWLGi/vvf/xZ8x13i008/1fTp050XAlWoUEFBQUGqU6eOdu7cmePCk9yO+9OnTyszMzPfKVUcrwNFi2CGfJUuXVpNmjRRVFSUDhw4oFtuuUXlypXT2bNnc+1pysjIUHp6eo7Lt7du3aqVK1eqUqVKOnTokGbNmpXnvEi5ye1DxfHBl9el4o43vUuvUnRwfMAW1bQB5cqVy/O5HG9g+T2Xo95Lr750SEtLk5+f31V/XQ4ZGRn5Pt/1118vy7I0YMAA/fbbbxo4cKDat2+v2267TWXKlNHx48e1bNmyAj2XI0RePJC+KOv+K/vIUVtuv6dz587Jy8tLPj4+znYfffRRgQZJV65cWSNHjtSIESN04MABbdiwQStWrFB0dLS8vLz03nvv5fpznjoe8pLf/snOzta5c+dyBHGHefPm6b333tPdd9+t/v3764477nCG6H79+unQoUMu7W+//Xa99957OnfunH7++Wf961//0hdffKFZs2bpxhtvdJ4CLmi7SxXV8XypihUrSvrfe9il/srs/WvWrNGrr76qunXr6tVXX1X9+vWdX4RfffVV7dy5s0DbcbzX/PDDD1dcC64MY8xwWY43dkcIc4xF2L59e462O3bskGVZuvXWW53r0tLSNHLkSPn6+mrx4sWqU6eOZs+eXahTKxdf8u/w888/S/rfaalLOa5Qym1ajdTUVO3bt0+1atVyfrstzLiO3NrefvvtOnXqlA4ePJjjMce+uni/XOrmm29WqVKlcr3NU2Jiopo0aaLRo0c7X1du+1+6MI2DzWbL97muxJkzZ3J8MEoXxoNdf/31CgwM1IEDB5xXFj7//PNq2LCh80PY8bOXflvPjWNKB8fpwIudP39e77zzjhYsWFCgunM7dhzjEoOCggq0jYvZ7XZJyvX39OGHHyooKEgxMTH5voaTJ0/qzTff1IoVKyRd+J2NGzdO8fHxzl6e/v37a9myZfLz88vzdy1d6JmqUaOGDh8+7DIVgoNjDsKiPh7y4njdue2fn3/+WY0bN9bMmTNz/dnVq1fL29tbM2fOVHBwsDOUWZal33//3flvSfryyy81duxYWZal0qVLq3nz5ho2bJjztLHj776g7XJTVMfzpcqUKaM6derkGFMoXXi/jY+PL/Q2HVavXi1J+uc//6n27du7nJ24dB/mp27duvrzzz+VlJSU47Hvv/9ekydP/kunx5E3ghnytXPnTsXExKhixYrO8UWOsWiTJk1y+SA4ceKEJkyYIEnOS8IlaeLEiUpISNDTTz+tOnXq6I033lBmZqaGDx+uzMzMAtXx4YcfuvQI/PTTT1q1apXq16+fY9CqQ/v27XXddddp8eLFzjFL0oVL8t98802lp6e71OkY4JrfvGr5tXXslzfffNPlG29CQoKmT5+uUqVKqVu3bnlu09fXV506ddKhQ4dyfBN33MuwZcuWqlatmpo3b669e/fmmBtp2bJl+umnn9S8eXO33B5n4sSJLq953rx5SkhIUGhoqLy9vZ0h99LBxSdPnnQeGwX5nd91112qUaOGPvvsM2cAd5gzZ47mz5/v8jvNz7fffusSbJKSkjRz5kz5+fmpS5cuBdrGxdq3by8/Pz8tWLDA5QKJkydPaunSpSpXrpwaN26sDh06qHz58po7d65zTI7Du+++q08++cT5AZyUlKQFCxZo3rx5Lu2OHz+ujIyMy459Cw0NVXp6ut566y2X/bt3714tXLhQFSpU+MvzlRVUYGCgmjRpoo0bN7qchnbMl2dZllq3bp3rz/r6+iorKytHwJwxY4ZzXzte3y+//KKFCxfmuIDDcdFLtWrVCtUuN0V1POcmNDRUaWlpmjhxojMoWZalSZMmXfE2pf+dljx+/LjL+i+//NI5Tq8g2w8NDZVlWRo7dqzL3/yxY8f02muvac6cOdz+yU04lQlJF65gvPhDJisrS7/99pt++OEHZWVlOa9ilC58aD755JOaP3++unfv7pzD5/vvv1dSUpL69+/vHHC/bds2LV68WHa7XU8++aSkC4PWw8LC9Pnnn2v27Nl6+umnL1vf6dOnFRoaqg4dOig1NVXR0dEqU6aMcx6o3JQvX15vvfWWnn/+eYWHh6tDhw6qUqWKtmzZooMHD+rOO+9U//79ne0d49hmzpypffv2aciQIXmOOcqt7YMPPqh169YpOjpa3bt3V3BwsHMes9OnT2v06NE5BuBf6qWXXtKOHTs0atQoRUdH67bbbtPu3bu1bds2tW/f3jm4/I033tDjjz+u1157Td98843q1q2rgwcPatOmTQoICMh3v/wVP/30kx566CG1bNlShw4d0saNG3Xbbbc5f4e1a9dWo0aNtH37dvXs2VNNmzbVf//7X61du1bnzp1T2bJlCzR2xtvbW+PHj1f//v3Vq1cv3X///QoMDNSePXu0ZcsW1ahRQ0OHDi1QzeXKldMTTzyhzp07q3z58lq7dq2OHz+usWPHXvYKzNxUqlRJY8aM0fDhwxUaGqr7779f5cqVU1RUlJKSkjRt2jSVLl1apUuX1rhx4/Tiiy8qNDRU7du3V0BAgGJiYrR79241bNhQffv2lXQh7DVp0kRLlizRwYMH1bhxY+dxLumyp/379++vjRs3atWqVTpw4IBatGih5ORkrV27VpZlafLkyVc05utKvf766+rVq5fz9F/16tW1ZcsW/frrr4qIiMizl7t79+765Zdf9Nhjj6lLly4qVaqUtm7dqr1796pKlSpKTk52TmTbr18/rVmzRi+++KKioqJUq1YtHT16VN988438/f3Vq1evQrXLTVEdz7np06eP1q1bpwULFmjPnj0KCgrSL7/8ooMHD6pKlSpXtE3pwj786quvNGTIEHXr1k3ly5fX7t27FRMTk2Mf5icsLMz5fnbgwAHdc889ziuZT548qRdeeOGy72e4MvSYQZLrdBnTpk3T3LlztWvXLrVr106ffPKJevTo4dL+lVde0bvvvqvq1atr1apVWrNmjW6++WZNnTpVL774oiTp7NmzGjFihKQLQeLiq48cU23MnDmzQDNdjxkzRu3bt9dXX32lDRs2qF27dlq6dGm+V3hJUseOHbV48WK1bt1aGzZscE6k+NJLL+mjjz5yGaTbtWtXdenSRQkJCVq8eHGu00Xk19Zms+m9997TqFGjVK5cOX322Wf6/vvv1bhxY3300UeXnVxWuhD4li1bpr/97W86cOCAPvnkE/3xxx8aPHiwJk+e7GxXu3Ztff7553r00Uf122+/aeHChYqNjVXv3r315Zdfuu0N8+OPP1ZAQICWLl2qf//734qIiNDixYudH/peXl6aMWOGwsLCdOTIES1YsEDbt29XcHCwPv/8c7Vu3VqxsbEFOlVz5513atmyZercubO2b9/u3Be9e/fW0qVLcx3Un5vu3bvr5Zdf1vbt2/Xll18qMDBQs2fP/kv3FgwNDdW8efN0xx13KDo6WpGRkapRo4bmzJmjDh06ONt16dJFCxcuVIsWLbRhwwYtXLhQZ86c0d///nd99NFHzvFhpUuX1uzZs9W/f3+dOHFCixYtUlRUlIKCgrRgwYJ8J1GWLvSSfPTRR3r22Wd1/vx5LVmyRFu2bHH+nVw6QbS7Oe640KVLF8XExGjBggU6e/ashg8fruHDh+f5cz179tTo0aNVqVIlLVu2TKtWrVK5cuU0adIkvfHGG5L+d+unGjVqaMmSJeratav27Nmj+fPna9u2berevbsiIyOdX54K2i43RXk8X6p06dKaN2+eBgwYoMTERGfvt2Pi7it17733avLkyapZs6ZWrVql5cuXKyMjQ2PGjNHcuXMlFez2WTabTVOmTNHIkSNVtmxZLVu2TGvWrNGtt96q6dOna8CAAVdcI/Jns67kBDlwlUydOtU5t9rV/nDB//Tu3VsxMTHatm3bZWckN8XWrVsVERGhiIgIjRw50tPlAECB0GMGAABgCIIZAACAIQhmAAAAhmCMGQAAgCHoMQMAADAEwQwAAMAQxWaC2f/+94yyszkrCwAAzOXlZdP11+d+A3upGAWz7GyLYAYAAK5pnMoEAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDIW2d+8uPfXU4/r11z2eLgUAgGKFYIZCmzlzqizL0owZ73u6FAAAihWCGQpl795dSks7I0lKSztDrxkAAEWIYIZCmTlzqssyvWYAABQdghkKxdFbltcyAAC4cgQzFIqfX7l8lwEAwJUjmKFQBg9+xmX573//h4cqAQCg+CGYoVDq12/k7CXz8yunevUaeLgiAACKD4IZCm3w4GdksyWzHM0AACAASURBVNnoLQMAoIjZLMuyPF1EUUhOTlV2drF4KQAAoJjy8rKpSpXyeT9+FWsBAABAPghmAAAAhiCYAQAAGMLHnRvv3bu3Tpw4IR+fC0/zxhtv6MyZM3r77beVkZGhLl266Pnnn5ck7du3TyNHjtSZM2d055136vXXX3f+HAAAQEngtuRjWZZiY2P1/fffOwNWenq6OnfurAULFuimm27SwIEDtX79erVt21bDhg3TuHHj1LhxY40YMUKRkZHq2bOnu8oDAAAwjttOZf7++++SpL59+6p79+5auHChdu3apVq1aikwMFA+Pj4KCQlRVFSUjh49qvT0dDVu3FiSFBYWpqioKHeVBgAAYCS39ZidOnVKLVu21OjRo3X+/HlFRESoX79+8vf3d7YJCAhQYmKijh075rLe399fiYmJhXq+/C49BQAAuBa4LZg1adJETZo0cS4//PDDmjJlipo1a+ZcZ1mWbDabsrOzZbPZcqwvDOYxAwAApvPYPGbbt2/Xjz/+6Fy2LEvVq1dXUlKSc11SUpICAgJUtWpVl/XHjx9XQECAu0oDAAAwktuC2enTpzVhwgRlZGQoNTVVy5cv19ChQ3X48GHFxcUpKytLq1evVnBwsKpXry5fX1/t2LFDkrRixQoFBwe7qzQAAAAjue1UZrt27bRz50716NFD2dnZ6tmzp5o0aaJ33nlHzzzzjDIyMtS2bVt17txZkjRx4kSNGjVKqampql+/viIiItxVGgAAgJG4VyYAAMBVwr0yAQAArhEEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAzh9mA2fvx4vfLKK5KkzZs3KyQkRB07dtTkyZOdbfbt26ewsDB16tRJI0eOVGZmprvLAgAAMI5bg9mPP/6o5cuXS5LS09M1YsQIzZgxQ19//bX27Nmj9evXS5KGDRumMWPGKDo6WpZlKTIy0p1lAQAAGMltwezkyZOaPHmyBg0aJEnatWuXatWqpcDAQPn4+CgkJERRUVE6evSo0tPT1bhxY0lSWFiYoqKi3FUWAACAsdwWzMaMGaPnn39eFSpUkCQdO3ZM/v7+zscDAgKUmJiYY72/v78SExPdVRYAAICxfNyx0WXLlummm25Sy5Yt9cUXX0iSsrOzZbPZnG0sy5LNZstzfWFVqVL+rxcOAADgQW4JZl9//bWSkpL04IMPKiUlRWlpaTp69Ki8vb2dbZKSkhQQEKCqVasqKSnJuf748eMKCAgo9HMmJ6cqO9sqkvoBAADcwcvLlm9nkluC2fz5853//uKLLxQTE6PXX39dHTt2VFxcnGrUqKHVq1froYceUvXq1eXr66sdO3aoWbNmWrFihYKDg91RFgAAgNHcEsxy4+vrq3feeUfPPPOMMjIy1LZtW3Xu3FmSNHHiRI0aNUqpqamqX7++IiIirlZZAAAAxrBZllUszv9xKhMAAJjucqcymfkfAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMIRbg9n777+vrl27qlu3bpo/f74kafPmzQoJCVHHjh01efJkZ9t9+/YpLCxMnTp10siRI5WZmenO0gAAAIzjtmAWExOjLVu2aOXKlfr888+1YMEC7d+/XyNGjNCMGTP09ddfa8+ePVq/fr0kadiwYRozZoyio6NlWZYiIyPdVRoAAICR3BbM7r77bn3yySfy8fFRcnKysrKydOrUKdWqVUuBgYHy8fFRSEiIoqKidPToUaWnp6tx48aSpLCwMEVFRbmrNAAAACP5uHPjpUqV0pQpUzRv3jx17txZx44dk7+/v/PxgIAAJSYm5ljv7++vxMTEQj1XlSrli6xuAAAAT3BrMJOkZ599Vv3799egQYMUGxsrm83mfMyyLNlsNmVnZ+e6vjCSk1OVnW0VWd0AAABFzcvLlm9nkttOZR46dEj79u2TJJUtW1YdO3bU1q1blZSU5GyTlJSkgIAAVa1a1WX98ePHFRAQ4K7SAAAAjOS2YHbkyBGNGjVK586d07lz5/Tdd98pPDxchw8fVlxcnLKysrR69WoFBwerevXq8vX11Y4dOyRJK1asUHBwsLtKAwAAMJLbTmW2bdtWu3btUo8ePeTt7a2OHTuqW7duqly5sp555hllZGSobdu26ty5syRp4sSJGjVqlFJTU1W/fn1FRES4qzQAAAAj2SzLKhYDsxhjBgAATOexMWYAAAAoHIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZgAAAIYgmAEAABiCYAYAAGAIghkAAIAhCGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQoVzE6dOuWuOnAN2bp1s/r27alt27Z4uhQAAIqVAgWz33//XV27dlW3bt2UmJioLl266NChQ+6uDYb64IMZkqTZs6d5uBIAAIqXAgWzcePGaeTIkapSpYpuvPFG9erVS2PGjHF3bTDQ1q2blZ2dLUnKzs6m1wwAgCJUoGB28uRJtW7d2rn8+OOPKzU11W1FwVyO3jIHes0AACg6BR5jlpGRIZvNJklKSkpy9pqgZLn0985xAABA0fEpSKPHHntMTz31lJKTk/XPf/5TX331lfr16+fu2gAAAEqUAgWzRx55RLVr19YPP/ygzMxMjR071uXUJkqOSpUq6+TJE87l66+v7MFqAAAoXmyWZVmXa9SnTx99/PHHV6OeK5acnKrs7Mu+FPxF8fGxeu21Ec7l119/W4GBtTxYEQAA1w4vL5uqVCmf9+MF2cjp06eVlpZWZEXh2lWzZm1VqnShl+z66ysTygAAKEIFOpVZtmxZtWvXTnXr1pWfn59z/axZs9xWGMz13HMvavz4sXruuWGeLgUAgGKlQKcyly9fnuv60NDQIi/oSnEqEwAAmO5ypzILFMwuZVmW4uLiVLt27b9SW5EimAEAANNdLpgV6FTmp59+qgkTJujs2bPOdZUrV9amTZv+eoUAAACQVMBgNmfOHM2fP18zZ87Uc889p++//15//vmnu2sDAAAoUQp0VWalSpUUFBSkO+64Q8nJyRo8eLC2bdvm7toAAABKlAIFMx8fH6WkpKhWrVratWuXJCkrK8uthcFc8fGxevrpp5SQEOfpUgAAKFYKFMweffRRDRw4UPfee6+WLl2qsLAw1alTx921wVBz5kzX2bNnuYE5AABFrEBjzB5++GF17dpVfn5+Wrp0qXbv3q02bdq4uzYYKD4+Vn/8cVSS9McfR5WQEMckswAAFJF8e8xGjx7t/Hd6erok6cYbb1T79u1VpkwZ91YGI82ZM91lmV4zAACKTr7BbM+ePc5/P/XUU24vBuZz9JbltQwAAK5cvsHs4rlnr2AeWhRD1apVz3cZAABcuQIN/pckm83mzjpwjRgw4GmX5YEDh3ioEgAAip98B/9nZ2crJSVFlmUpKyvL+W+HSpUqub1AmKVmzdqqVq26/vjjqKpVq87AfwAAilC+98q8/fbbZbPZcj2NabPZtG/fPrcWVxjcK/PqiY+P1fjxY/XKK2MIZgAAFIJbbmJuIoIZAAAw3eWCWYHHmAEAAMC9CGYAAACGIJgBAAAYgmAGAABgCIIZAACAIQhmAAAAhiCYAQAAGIJgBgAAYAiCGQAAgCEIZii0rVs3q2/fntq2bYunSwEAoFghmKHQ5s6dJUmaM2eGhysBAKB4IZihULZu3aysrExJUlZWJr1mAAAUIYIZCsXRW+ZArxkAAEWHYIZCcfSW5bUMAACuHMEMheLt7ZPvMgAAuHIEMxRKv36DXJYHDPi7hyoBAKD4IZihUJo3byXJ9v9LNt11VwtPlgMAQLFCMEOhnDz5X9lsF4KZl5dNKSknPVwRAADFB8EMhbJq1XJ5eV04bGw2L61c+YWHKwIAoPggmKFQfvxxk8s8Zj/+uMnDFQEAUHwQzFAoLVu2dl6J6e3to5YtW3u4IgAAig+CGQolJCRUXl6OMWZe6t49zMMVAQBQfBDMUCiVKl2vNm3aymazqU2bYFWsWMnTJQEAUGwwOygKLSQkVEePHqG3DACAImazLMvydBFFITk5VdnZxeKlAACAYsrLy6YqVcrn/fhVrAXFxNatm9W3b09t27bF06UAAFCsEMxQaHPnzpIkzZkzw8OVAABQvBDMUChbt252mceMXjMAAIoOwQyF4ugtc6DXDACAokMwQ6E4esvyWgYAAFeOYIZCccz6n9cyAAC4cgQzFEq/foNclgcM+LuHKgEAoPghmKFQmjdv5XKvzLvuauHhigAAKD4IZig0R68ZvWUAABQtZv4HAAC4Spj5HwAA4Brh1mA2bdo0devWTd26ddOECRMkSZs3b1ZISIg6duyoyZMnO9vu27dPYWFh6tSpk0aOHKnMTKZhAAAAJYvbgtnmzZu1ceNGLV++XF9++aX27t2r1atXa8SIEZoxY4a+/vpr7dmzR+vXr5ckDRs2TGPGjFF0dLQsy1JkZKS7SgMAADCS24KZv7+/XnnlFZUuXVqlSpVSnTp1FBsbq1q1aikwMFA+Pj4KCQlRVFSUjh49qvT0dDVu3FiSFBYWpqioKHeVBgAAYCS3BbPbbrvNGbRiY2O1Zs0a2Ww2+fv7O9sEBAQoMTFRx44dc1nv7++vxMREd5UGAABgJLdP2/7vf/9bAwcO1EsvvSRvb2/FxsY6H7MsSzabTdnZ2bLZbDnWF0Z+VzgAAABcC9wazHbs2KFnn31WI0aMULdu3RQTE6OkpCTn40lJSQoICFDVqlVd1h8/flwBAQGFei6mywAAAKbz2HQZ//nPf/T0009r4sSJ6tatmyQpKChIhw8fVlxcnLKysrR69WoFBwerevXq8vX11Y4dOyRJK1asUHBwsLtKAwAAMJLbJpgdN26cPv/8c9WsWdO5Ljw8XLVr19bbb7+tjIwMtW3bVsOHD5fNZtP+/fs1atQopaamqn79+nr77bdVunTpAj8fPWYAAMB0l+sxY+Z/AACAq4SZ/wEAAK4RBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwhI+nC0D+Nm36lzZuXO/pMlykpJyUJFWsWMnDlbhq06atWrcO9nQZAABcMXrMUGgpKSlKSUnxdBkAABQ7NsuyLE8XURSSk1OVnV0sXorxxo8fK0l6+eXRHq4EAIBri5eXTVWqlM/78atYCwAAAPJBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADOHWYJaamqoHHnhAR44ckSRt3rxZISEh6tixoyZPnuxst2/fPoWFhalTp04aOXKkMjMz3VkWAACAkdwWzHbu3KnHHntMsbGxkqT09HSNGDFCM2bM0Ndff609e/Zo/fr1kqRhw4ZpzJgxio6OlmVZioyMdFdZAAAAxnJbMIuMjNSrr76qgIAASdKuXbtUq1YtBQYGysfHRyEhIYqKitLRo0eVnp6uxo0bS5LCwsIUFRXlrrIAAACM5eOuDb/55psuy8eOHZO/v79zOSAgQImJiTnW+/v7KzExsdDPV6VK+SsvFoVSqpS3JMnf/zoPVwIAQPHitmB2qezsbNlsNueyZVmy2Wx5ri+s5ORUZWdbRVIr8nf+fJYkKSnptIcrAQDg2uLlZcu3M+mqXZVZtWpVJSUlOZeTkpIUEBCQY/3x48edpz8BAABKkqsWzIKCgnT48GHFxcUpKytLq1evVnBwsKpXry5fX1/t2LFDkrRixQoFBwdfrbIAAACMcdVOZfr6+uqdd97RM888o4yMDLVt21adO3eWJE2cOFGjRo1Samqq6tevr4iIiKtVFgAAgDFslmUVi4FZRTHGbPHiT5SQEFdEFRVf8fEX9lHNmrU8XIn5AgNrqWdPvmgAAC643Bizq9Zjdi1ISIjTgX//Ju8ylTxditGysy5clflbwnEPV2K2rPSTni4BAHCNIZhdwrtMJfnVut/TZaAYSIv7ztMlAACuMdwrEwAAwBAEMwAAAEMQzAAAAAxBMAMAADAEwQwAAMAQBDMAAABDEMwAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADEEwAwAAMATBDAAAwBAEMwAAAEMQzAAgF+vWfau+fXtq/frvPF0KgBKEYAYAuVi48CNJ0scfz/NsIQBKFIIZAFxi3bpvJVn/v2TRawbgqiGYAcAlHL1lDvSaAbhaCGYAkIN1mWWgeFi27FP17dtTy5dHeroU/D+CGQDkYLvMMlA8rFmzUpK0atWXHq4EDgQzALhEr15PuCz36dPXM4UAbrRs2acuy/SamYFgBgCXuO++DvpfL5lNbdve78lyALdw9JY50GtmBoIZAOSiS5cQSVJIyIMergRASUIwA4Bc7Ny5Q5K0Y8c2D1cCoCQhmAHAJeLjY/XHH0clSX/8cVQJCXEerggoel26dHdZDgnp4aFKcDGCGQBcYs6c6S7Ls2dP81AlgPs88ki4y3Jo6KMeqgQXI5gBwCUcvWV5LQPFhaPXjN4yc/h4ugAAME21atVdwli1atU9WA3gPo88Ep6j5wyeRY8ZAFxiwICnXZYHDhzioUoAlDT0mF0kJeWkstJPKi2OGxbjr8tKP6mUFP7ErkU1a9Z29ppVq1ZdgYG1PF0SgBKCHjMAyMWAAU+rbNmy9JahWONemebh6/xFKlaspKRTmfKrxSzf+OvS4r5TxYqVPF0GrlDNmrU1ffqHni4DcKuL75XJVZlmoMcMAIASiHtlmolgBgBACcS9Ms1EMAMAADAEwQwAAMAQBDMAAEog7pVpJoIZAAAlEPfKNBPBDACAEop7ZZqHYAYAQAlVtmxZl//D8whmAACUUF98sVSSFBm5xMOVwIFgBgBACbR69QqX5aioVR6qBBcjmAEAUAI5essc6DUzA8EMAADAEAQzAAAAQxDMAAAogcLC/uay/Oijj3moElyMYAYAQAn0wAMPuix37hzioUpwMR9PFwAAmzb9Sxs3rvd0GS5SUk5KkipWrOThSly1adNWrVsHe7oMAG5CjxkA5CIlJUUpKSmeLgNwG6bLMBM9ZgA8rnXrYON6gcaPHytJevnl0R6uBHCP3KbL4HSm59FjBgAAYAiCGQAAgCEIZgAAlEBMl2EmghkAACWQv7+/y3KVKv55tMTVRDADAKAEmjt3lsvynDkzPFQJLkYwAwCgBMrKysx3GZ5BMAMAADAEwQwAAMAQTDB7iaz0k0qL+87TZRgtOzNdkuTlU8bDlZgtK/2kpBs8XQYA4BpCMLtIYGAtT5dwTYiPj5Mk1QwkdOTvBo4pAEChEMwu0rNnhKdLuCZwqxoAANyDMWYAAACGIJgBAAAYglOZQAmzePEnSkiI83QZxnOMpXScukfeAgNrMRSkADZt+pc2blzv6TLyZcrx3qZNW7VuHezpMjyCYAaUMAkJcYr9bb+qlufPPz9+ypYkpf/5m4crMdufqUxKeq2qXLmKTpxIdi5XqcIFXSbgnRkogaqW99GTjSp7ugwUA/N3nfB0CdeM1q2DjesF6tu3p/Pf7747xYOVwIExZgAAlFCVK1eRJD366GMergQO9JgBAFBC+fsHyN8/QJ07h3i6FPw/eswAAAAMQTADAAAwBMEMAADAEAQzAAAAQxDMAAAADMFVmQCAYoc7XBQMd7gouKt1hwuCGVDCpKSc1H9TM5kYFEXiz9RMXZ9y0tNl5JCQEKeDvx+Qd8XSni7FaNneWZKkQ8mHPVyJ2bJSzl215yKYASXQuSyLW+lcRpZlSZK8bTYPV2K2c1mWp0vIk3fF0qoYXM3TZaAYSPnXH1ftuQhmQAnToEGQKlas5OkyjOc4xVOzZi0PV2K+wED2EVBUCGZACXM1xkgUB44xNy+/PNrDlQAoSbgqEwAAwBAEMwAAAENwKhMAUOykpJxUZnK6kldytWG+sv///3TT5C/LUorP1bn6mGAGACh2Kle+QSkpKZ4uw3gZGemSJN9SZTxcieFKXTimrgaCGQCg2HnxxeGeLuGawEUu5qHzEgAAwBAEMwAAAEMYFcxWrVqlrl27qmPHjlq0aJGnywEAALiqjBljlpiYqMmTJ+uLL75Q6dKlFR4erubNm+vWW2/1dGkAAABXhTE9Zps3b1aLFi1UqVIl+fn5qVOnToqKivJ0WQAAAFeNMT1mx44dk7+/v3M5ICBAu3bt8mBFZti06V/auHG9p8tw4biHoONqHlO0adNWrVsHe7oMXAGO84LjOL92cZwXXEk+zo0JZtnZ2bLZbM5ly7Jcli+nSpXy7ijL4ypUKKtSpbw9XYaLKlUqS5JxdVWoUFb+/td5ugxcAY7zguM4v3ZxnBdcST7OjQlmVatW1fbt253LSUlJ/9fO3YU0uQdwHP9NXS8rHF3kCAKD3iTSC8OCUXkRmbVBCQqNQqKim0CKIAqCLkYprpteLopIwYJgkGYXRUZEFmpClmWEVLJkJCF2sWOpK/eci06C51jn7OB6/ur3c+UeNvgNnouvz/MwZWVl/efPDwwMKpGwUjHNVrm5BcrNLbB7xpTR3/+H3RPwP3CeJ4fzfGriPE/OdD3P09Icv7yYZMwzZl6vV62trfr06ZOGhobU1NSkjRtn5mVMAAAwMxlzxczj8ejw4cMqLy/X169fVVpaqry8PLtnAQAA/DYOy7Kmxf2/6XorEwAATB9T5lYmAADATEeYAQAAGIIwAwAAMARhBgAAYAjCDAAAwBCEGQAAgCEIMwAAAEMQZgAAAIYgzAAAAAxBmAEAABiCMAMAADAEYQYAAGAIwgwAAMAQhBkAAIAhCDMAAABDEGYAAACGIMwAAAAMQZgBAAAYIsPuAZMlLc1h9wQAAIBf+rdecViWZf2mLQAAAPgFbmUCAAAYgjADAAAwBGEGAABgCMIMAADAEIQZAACAIQgzAAAAQxBmAAAAhiDMAAAADEGY9uqL6AAABF5JREFUAQAAGIIwQ9IGBwfl9/sVjUbtngKkxIULF+Tz+eTz+VRdXW33HCAlzp49q23btsnn86m2ttbuOfgLYYakdHZ2KhAIKBKJ2D0FSImWlhY9fvxYDQ0Nunnzpl69eqV79+7ZPQuYVO3t7Wpra9OtW7d048YNXb16VT09PXbPgggzJCkcDuvkyZPKysqyewqQEgsXLtSxY8c0a9YsOZ1OLV26VB8+fLB7FjCp1q5dq7q6OmVkZGhgYECjo6NyuVx2z4KkDLsHYGo5deqU3ROAlFq+fPnY35FIRHfu3NH169dtXASkhtPp1Llz51RTU6Pi4mJ5PB67J0FcMQOACb1580Z79+7V0aNHtWTJErvnAClRUVGh1tZW9fX1KRwO2z0HIswA4B+ePn2qPXv26MiRIyopKbF7DjDp3r17p9evX0uS5s6dq6KiInV3d9u8ChJhBgDj9PX16eDBgzpz5ox8Pp/dc4CUiEajOnHihOLxuOLxuO7fv681a9bYPQviGTMAGOfKlSsaGRlRVVXV2LGdO3cqEAjYuAqYXIWFhXrx4oV27Nih9PR0FRUV8Y+IIRyWZVl2jwAAAAC3MgEAAIxBmAEAABiCMAMAADAEYQYAAGAIwgwAAMAQ/FwGgGkpGo1q8+bNWrFixdgxy7JUXl6u0tLSCT9TX1+vu3fv6tKlS79rJgCMQ5gBmLbmzJmjxsbGsdcfP36U3+/X6tWrlZOTY+MyAJgYYQZgxvB4PMrOzlYkEtHDhw/V0NCgjIwMZWdnj/tBWUl6/vy5QqGQ4vG4+vv75fV6dfr0aX379k3BYFAdHR1yOp1avHixKisrNXv27AmPz5s3z6ZvC2AqIswAzBjPnj1Tb2+vhoaGVF9fr3A4LLfbrcrKSl27dk0ej2fsvXV1daqoqNC6dev0+fNnbdq0SV1dXRoeHlZ7e7tu374th8OhUCik7u5uJRKJCY/n5+fb+I0BTDWEGYBpa3h4WNu3b5ckjY6OasGCBQqFQnr06JGKi4vldrslScePH5f0/RmzH6qqqtTc3KyLFy+qp6dHIyMj+vLli3JycpSenq6ysjKtX79eW7ZsUV5enmKx2ITHASAZhBmAaevvz5j90NLSIofDMfY6FospFouNe8/u3bu1cuVKbdiwQVu3blVnZ6csy1JmZqYaGxvV0dGhtrY2HTp0SPv27dOuXbt+ehwA/ivCDMCM4/V6VV1drf3792v+/Pk6f/68LMvSqlWrJH0PtZcvX+ry5ctyu9168uSJent7lUgk9ODBA9XU1Ki2tlYFBQWyLEtdXV0/PQ4AySDMAMw4hYWFevv2rQKBgCRp2bJlCgaDampqkiRlZmbqwIEDKikpkcvlksfjUX5+vt6/f6+ysjI1NzfL7/fL5XLJ7XYrGAxq0aJFEx4HgGQ4LMuy7B4BAAAAfvkfAADAGIQZAACAIQgzAAAAQxBmAAAAhiDMAAAADEGYAQAAGIIwAwAAMARhBgAAYIg/AdMHC4k9Dh5OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets look at the fare for each class that a passenger paid. \n",
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(x=train['Pclass'], y=train['Fare'])\n",
    "plt.title('Box plot to compare prices of Pclass and fare', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pclass 1 has a higher range of ticket fare than compared to pclass 3 which definitely makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(x=train['Pclass'], y=train['Age'])\n",
    "plt.title('Box plot to compare prices of Pclass and Age', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We cannot really make an inference out of this as all the classes have all age groups "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# However lets see the distibution of age\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(train['Age'], hist = False)\n",
    "plt.title('Distribution of Age', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most of the passengers on board were young within the age 15 to 40. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets check from which place did most of the passengers begin their journey\n",
    "plt.figure(figsize=(10,5))\n",
    "plot = sns.countplot(train['Embarked'])\n",
    "plt.title('Count plot for Embarked', size = 20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most passengers started thier journey from Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             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",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values\n",
    "\n",
    "train.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We shall drop the cabin column, drop rows for Embarked and impute for age\n",
    "\n",
    "train = train.drop('Cabin', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We shall impute random values for age column\n",
    "\n",
    "age_random = train['Age'].dropna().sample(train['Age'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_random.index = train[train['Age'].isnull()]['Age'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['Age'].isnull(),'Age'] = age_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## drop rest of the nan values\n",
    "\n",
    "train = train.dropna()\n",
    "\n",
    "# count the null values again\n",
    "train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping unwanted columns \n",
    "train = train.drop(['PassengerId','Name','Ticket'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Since we are applying logistic regression it would make sense to convert numerical categorical columns from int to object\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[['Survived','Pclass']] = train[['Survived','Pclass']].astype('object')"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Survived Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0        0      3    male  22.0      1      0   7.2500        S\n",
       "1        1      1  female  38.0      1      0  71.2833        C\n",
       "2        1      3  female  26.0      0      0   7.9250        S\n",
       "3        1      1  female  35.0      1      0  53.1000        S\n",
       "4        0      3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Encoding categorical features\n",
    "\n",
    "train = pd.get_dummies(train,columns=['Survived','Pclass','Sex','Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age           0\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Survived_1    0\n",
       "Pclass_2      0\n",
       "Pclass_3      0\n",
       "Sex_male      0\n",
       "Embarked_Q    0\n",
       "Embarked_S    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32.09668087739029, 49.69750431670801)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_mean = train['Fare'].mean()\n",
    "fare_std = train['Fare'].std()\n",
    "fare_mean, fare_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.499958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.788503</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.486376</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.422623</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.483861</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch      Fare  Survived_1  Pclass_2  Pclass_3  Sex_male  \\\n",
       "0  22.0      1      0 -0.499958           0         0         1         1   \n",
       "1  38.0      1      0  0.788503           1         0         0         0   \n",
       "2  26.0      0      0 -0.486376           1         0         1         0   \n",
       "3  35.0      1      0  0.422623           1         0         0         0   \n",
       "4  35.0      0      0 -0.483861           0         0         1         1   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Fare'] = (train['Fare'] - fare_mean) / fare_std\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             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",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values\n",
    "\n",
    "test.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We shall drop the cabin column, impute for age and fare\n",
    "\n",
    "test = test.drop('Cabin', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We shall impute random values for age column\n",
    "\n",
    "age_random = test['Age'].dropna().sample(test['Age'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_random.index = test[test['Age'].isnull()]['Age'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test['Age'].isnull(),'Age'] = age_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## impute fare\n",
    "\n",
    "test['Fare']= test['Fare'].fillna(test['Fare'].mean())\n",
    "\n",
    "# count the null values again\n",
    "test.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping unwanted columns \n",
    "test = test.drop(['Name','Ticket'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Pclass'] = test['Pclass'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0          892      3    male  34.5      0      0   7.8292        Q\n",
       "1          893      3  female  47.0      1      0   7.0000        S\n",
       "2          894      2    male  62.0      0      0   9.6875        Q\n",
       "3          895      3    male  27.0      0      0   8.6625        S\n",
       "4          896      3  female  22.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Encoding categorical features\n",
    "\n",
    "test = pd.get_dummies(test,columns=['Pclass','Sex','Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId   Age  SibSp  Parch     Fare  Pclass_2  Pclass_3  Sex_male  \\\n",
       "0          892  34.5      0      0   7.8292         0         1         1   \n",
       "1          893  47.0      1      0   7.0000         0         1         0   \n",
       "2          894  62.0      0      0   9.6875         1         0         1   \n",
       "3          895  27.0      0      0   8.6625         0         1         1   \n",
       "4          896  22.0      1      1  12.2875         0         1         0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           1           0  \n",
       "1           0           1  \n",
       "2           1           0  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.488304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.504989</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.450912</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.471536</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.398595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId   Age  SibSp  Parch      Fare  Pclass_2  Pclass_3  Sex_male  \\\n",
       "0          892  34.5      0      0 -0.488304         0         1         1   \n",
       "1          893  47.0      1      0 -0.504989         0         1         0   \n",
       "2          894  62.0      0      0 -0.450912         1         0         1   \n",
       "3          895  27.0      0      0 -0.471536         0         1         1   \n",
       "4          896  22.0      1      1 -0.398595         0         1         0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           1           0  \n",
       "1           0           1  \n",
       "2           1           0  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Fare'] = (test['Fare'] - fare_mean) / fare_std\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.model_selection import cross_val_score, cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop('Survived_1',1)\n",
    "y_train = train['Survived_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test.iloc[:,1:]\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       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, 0, 0, 1, 0, 1, 1, 1, 0, 1, 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, 0, 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, 0,\n",
       "       0, 0, 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, 0, 0, 0, 0, 1, 0, 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, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0],\n",
       "      dtype=uint8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUBMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame({'PassengerId':test['PassengerId'],\n",
    "                    'Survived':y_pred})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Survived']  = sub['Survived'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}