Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../Machine Leaning/titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WE NEED TO PREDICT WHETHER THE PASSENGER HAS SURVIVED OR NOT"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Lets begin with EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use seaborn to create a sample heatmap to see where we are missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<p>891 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()  # if True then the value is null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaca75d0>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Null values shown in yellow color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7889350>"
      ]
     },
     "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.set_style('whitegrid')\n",
    "sns.countplot(x='Survived', data=train)\n",
    "\n",
    "# Lets us know how many people survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad49f50>"
      ]
     },
     "execution_count": 15,
     "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',hue='Sex', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xad84610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2bdt2ybTZs2d3OH9TU5NmzZqlqVOnKi8vT+Xl5f73XC6XBg4cqPj4eLlcroum22y2QOIAAIIkoCuGk5KSlJSUpGuuuUbHjx9XU1NTh/OePHlSRUVFmjdvnvLz8yVJt9xyi2prayVJe/fuVVZWljIzM1VTUyOv16vGxkZ5vd4uRwEAgOAKaCQwefLki14//PDDHc67du1anTt3TlVVVaqqqpIkLVy4UMuWLVNFRYWGDRumnJwcWSwWZWVlqbCwUF6vVyUlJT34NQAAV8Lw+Xy+rmY6cuSI/98nTpzQ0qVL9ac//SmkwTridDrZd9tHnHxjXqQjBCwpr7zrmYA+rKNtZ0AjgW9+S7darZo/f37wkgEAIiagEli/fr3OnDmjL7/8UikpKey7B4CrREAHht98801NnjxZa9euVWFhof7whz+EOhcAIAwCGgn89re/1datWxUXF6eWlhb99Kc/1cSJE0OdDQAQYgGNBAzD8F8EFh8fL6vVGtJQAIDwCGgkkJqaquXLlysrK0sHDhxQampqqHMBAMIgoJHApEmTNGjQIL333nvaunWrpk2bFupcAIAwCKgEli9frvHjx6ukpESbN2/W8uXLQ50LABAGAZVAdHS0brjhBknS0KFDA3quAACg9wvomEBycrIqKio0evRoHTp0SIMHDw51LgBAGAT0lb6srEyJiYl65513lJiYqLKyslDnAgCEQUAjAavVqunTp4c4CgAg3Ni5DwAmRgkAgIlRAgBgYpQAAJgYJQAAJkYJAICJUQIAYGKUAACYWMhK4ODBg7Lb7ZKk+vp6jR07Vna7XXa7XX/+858lSatXr1Z+fr4mT56sQ4cOhSoKAKADAV0x3F3r1q3Tjh07FBsbK0n68MMPNWPGDBUVFfnnqa+vV11dnaqrq9XU1KTi4mJt2bIlFHEAAB0ISQmkpqaqsrJS8+fPlyQdPnxYR44c0a5du5SWliaHw6EDBw4oOztbhmEoOTlZHo9Hp0+f7vIh9m63W06nMxSxEUQZGRmRjtBtrFcwo5CUQE5OjhoaGvyvR40apYKCAo0cOVJr1qzRiy++KJvNpoSEBP88cXFxam5u7rIErFZrn9zAoPdjvcLVrKMvOWE5MDx+/HiNHDnS/+8PP/xQ8fHxcrlc/nlcLpdsNls44gAA/l9YSmDmzJn+A7/79u3TiBEjlJmZqZqaGnm9XjU2Nsrr9XY5CgAABFdIdgd925IlS1RaWqqYmBglJSWptLRU8fHxysrKUmFhobxer0pKSsIRBQDwDYbP5/NFOkR3OJ1O9t32ESffmBfpCAFLyiuPdAQgpDradnKxGACYGCUAACZGCQC9yAWPN9IRuq0vZsb/hOXAMIDA9LdEKe21f0Q6Rrccnfr9SEdADzASAAATowQAwMQoAQAwMUoAAEyMEgAAE6MEAMDEKAEAMDFKAABMjBIAYCo+T1ukI3RbKDNzxTAAUzEsMX3qDrdSaO9yy0gAAEyMEgAAE6MEAMDEKAEAMDFKAABMLGQlcPDgQdntdknS0aNHNWXKFE2dOlVPP/20vN6vHkKxevVq5efna/LkyTp06FCoogAAOhCSEli3bp0WLVokt9stSSorK9PcuXP12muvyefzadeuXaqvr1ddXZ2qq6tVUVGhpUuXhiIKAKATISmB1NRUVVZW+l/X19frtttukyTdeeedeu+993TgwAFlZ2fLMAwlJyfL4/Ho9OnToYgDAOhASC4Wy8nJUUNDg/+1z+eTYRiSpLi4ODU3N6ulpUUJCQn+eb6enpiY2Omy3W63nE5nKGIjiDIyMiIdodt6w3rVF/9uUu/42wWKv/HFwnLFcFTU/wYcLpdLAwcOVHx8vFwu10XTbTZbl8uyWq199j8iejfWqyvH3y70evo37qhEwnJ20C233KLa2lpJ0t69e5WVlaXMzEzV1NTI6/WqsbFRXq+3y1GAWbX7PJGOAOAqFZaRwIIFC7R48WJVVFRo2LBhysnJkcViUVZWlgoLC+X1elVSUhKOKH1StGHRqrN/iXSMbvlZQm6kIwAIQMhKICUlRZs2bZIkpaena8OGDZfMU1xcrOLi4lBFAAB0gYvFAMDEKAEAMDFKAECPcOJC38ZDZQD0SF87cYGTFi7GSAAATIwSAAATM2UJXPB4Ix0BAHoFUx4T6G+JUtpr/4h0jIAdnfr9SEcAcJUy5UgAAPAVSgAATIwSAAATowQAwMQoAQAwMUoAAEyMEgAAE6MEAMDEKAEAMDFKAABMjBIAABOjBADAxMJ6A7n7779fNptN0lcPoi8sLNQzzzwji8Wi7OxszZ49O5xxAMD0wlYCbrdbkrR+/Xr/tIkTJ6qyslJDhw7Vo48+qvr6eo0YMSJckQDA9MK2O+ijjz7S+fPnVVRUpIceekh/+9vf1NraqtTUVBmGoezsbO3bty9ccQAACuNIoH///po5c6YKCgr0xRdf6JFHHtHAgQP978fFxenLL7/scjlut1tOp7NHWTIyMnr0eVyderpeBQPrJjoSqvUzbCWQnp6utLQ0GYah9PR02Ww2nT171v++y+W6qBQ6YrVa+R8FIcF6hd6sp+tnRyUStt1Bmzdv1vLlyyVJx48f1/nz5zVgwAD9+9//ls/nU01NjbKyssIVBwCgMI4E8vPz9dRTT2nKlCkyDEPPPvusoqKi9MQTT8jj8Sg7O1u33npruOIAABTGEujXr5+ee+65S6Zv2rQpXBEAAN/CxWIAYGKUAACYGCUAACZGCQCAiVECAGBilAAAmBglAAAmRgkAgIlRAgBgYpQAAJgYJQAAJkYJAICJUQIAYGKUAACYGCUAACZGCQCAiVECAGBilAAAmBglAAAmRgkAgImF7UHzHfF6vVqyZIk+/vhj9evXT8uWLVNaWlqkYwGAKUR8JPDWW2+ptbVVv//97/WLX/xCy5cvj3QkADCNiJfAgQMHNHbsWEnS6NGjdfjw4QgnAgDzMHw+ny+SARYuXKgJEybohz/8oSTprrvu0ltvvaXo6Mvvqfrggw9ktVrDGREA+jy3263Ro0dfMj3ixwTi4+Plcrn8r71eb4cFIOmyvwQA4MpEfHdQZmam9u7dK+mrb/k33XRThBMBgHlEfHfQ12cHffLJJ/L5fHr22Wc1fPjwSEYCANOIeAkAACIn4ruDAACRQwkAgIlRAgBgYpSACXm9XpWUlKiwsFB2u11Hjx6NdCTgIgcPHpTdbo90DFOI+HUCCL9v3qrjgw8+0PLly7VmzZpIxwIkSevWrdOOHTsUGxsb6SimwEjAhLhVB3qz1NRUVVZWRjqGaVACJtTS0qL4+Hj/a4vFovb29ggmAv4nJyen07sGILgoARPq7q06AFy9KAET4lYdAL7G1z8TGj9+vN59911NnjzZf6sOAObEbSMAwMTYHQQAJkYJAICJUQIAYGKUAACYGCUAACZGCcD0Xn75ZU2fPl1FRUWaOXNmj26j8cwzz6ixsfGKP//zn/9ctbW1V/x5oLu4TgCm9umnn+rtt9/W66+/LsMw5HQ6tWDBAu3YseOKlrdw4cIgJwRCi5EATC0xMVGNjY3avHmzjh8/royMDG3evFl2u12fffaZJOn1119XZWWlGhoalJeXJ7vdrnXr1ulHP/qRvr7MZunSpdq5c6f/cw8++KAaGhokSW+++aaWLVum5uZmzZkzR3a7XXa7XR9//LEk6dVXX9X999+vRx55hNt6I+woAZhaYmKi1qxZo/fff1+FhYXKzc3V7t27O5z/xIkT+vWvf61HHnlEN998s/7+97+rtbVVdXV1uvvuu/3z5efna/v27ZKkbdu2adKkSVq7dq1uv/12rV+/XqWlpVqyZImam5v1yiuvaNOmTaqqqlJbW1vIf2fgm9gdBFM7evSo4uPjVVZWJkn65z//qUcffVRJSUn+eb55UX1KSor69esnSZo0aZK2bdumEydO6J577rnoJnw/+clPNGXKFBUUFKilpUU33XSTPvnkE+3fv19vvvmmJOncuXP6/PPPdcMNN/iXOWrUqJD/zsA3MRKAqX388cdasmSJ3G63JCk9PV02m00JCQk6ceKEJOnDDz/0zx8V9b//Ze644w45nU5t2bJF+fn5Fy03Pj5eI0eOVFlZmR588EFJ0rBhwzR9+nStX79ev/rVr5SXl6ehQ4fq008/1YULF+TxeOR0OkP9KwMXYSQAU5swYYI+++wzFRQUaMCAAfL5fJo/f75iYmL0y1/+Utddd50GDx582c8ahqGcnBy99957SktLu+T9goICPfzww/4b9D322GNauHChNm3apJaWFs2ePVuJiYn62c9+psmTJysxMZGnaSHsuIEcAJgYu4MAwMQoAQAwMUoAAEyMEgAAE6MEAMDEKAEAMDFKAABM7P8A4+wUgz56OVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass', data=train, palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xadcc270>"
      ]
     },
     "execution_count": 17,
     "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='darkred', bins=40)\n",
    "\n",
    "# dropping the Age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xac85a70>"
      ]
     },
     "execution_count": 18,
     "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": [
    "train['Age'].hist(bins=30, color='darkred', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6559330>"
      ]
     },
     "execution_count": 19,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x156a4f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAD5CAYAAAD2kUYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAYoUlEQVR4nO3dXWxT9/3H8c+J3SYQO4oitIeIh4aWaQEWUBTBLgytViAIrYVWoeFBQSKsKwzMcgEKuHkAhZIiNqSWAH3QegOdMkIqxMWkjiEiFKBJhQaIzF3VidGVpGg0rUgs7IDP+V/0X1eMESeOg38479cVPv7F53s+Lfn4nDgHy3EcRwAAIKUyUj0AAACgkAEAMAKFDACAAShkAAAMQCEDAGAAChkAAAMMqZC/+uorPf300/rnP/+pa9euaeXKlVq1apXq6+tl27YkqampSWVlZVqxYoUuX748qkMDAJBu3PEW3LlzR3V1dcrKypIkNTY2qqqqSnPnzlVdXZ1OnTql/Px8dXZ2qqWlRT09PfL7/WptbY2784sXLyozM3PkR/H/IpFIUl9vLCG7kSG/xJFd4sgucanKLhKJaPbs2f/zubiFvGfPHq1YsULvvPOOJKmrq0tz5syRJM2fP19nz55VQUGBfD6fLMtSfn6+otGoent7lZeXN+hrZ2ZmqrCwcLjH80DBYDCprzeWkN3IkF/iyC5xZJe4VGUXDAYf+Nygl6w/+OAD5eXlad68ebFtjuPIsixJUnZ2tvr6+tTf3y+PxxNb8912AAAwNIOeIbe2tsqyLJ0/f17BYFDV1dXq7e2NPR8KhZSTkyOPx6NQKHTPdq/XG3fnkUhk0HcLwxUOh5P6emMJ2Y0M+SWO7BJHdokzMbtBC/n999+P/bmiokI7duzQ3r171dHRoblz5+rMmTP6+c9/rsmTJ2vv3r1at26dvvzyS9m2HfdytcQla5OQ3ciQX+LILnFklzgTL1nH/Rnyf6uurlZtba327dunqVOnqrS0VC6XSyUlJSovL5dt26qrqxvRwAAAjDVDLuTDhw/H/nzkyJH7nvf7/fL7/cmZCgCAMYYbgwAAYAAKGQAAA1DIAAAYgEIGAMAAaVXIkwomDWld+G54lCcBAGB4hv1rTybzZHlk7bTirnPqnYcwDQAAQ5dWZ8gAADyqKGQAAAxAIQMAYAAKGQAAA1DIAAAYgEIGAMAAFDIAAAagkAEAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGCDuv/YUjUZVU1Ojq1evyuVyqbGxUX19fVq/fr2eeOIJSdLKlSu1ZMkSNTU1qa2tTW63W4FAQEVFRaM9PwAAaSFuIZ8+fVqS1NzcrI6ODjU2NuoXv/iF1q5dq8rKyti6rq4udXZ2qqWlRT09PfL7/WptbR29yQEASCNxC3nBggV65plnJEnd3d2aMGGCrly5oqtXr+rUqVOaMmWKAoGALly4IJ/PJ8uylJ+fr2g0qt7eXuXl5Y32MQAA8MiLW8iS5Ha7VV1drZMnT+rNN9/UjRs3tHz5cs2cOVOHDh3SgQMH5PV6lZubG/ua7Oxs9fX1UcgAAAyB5TiOM9TF//nPf/TSSy+publZP/zhDyVJn332mRoaGvTss88qEono5ZdfliQtW7ZM77333qCFfPHiRWVmZo7wEL5XWFgoa6cVd51T7ygYDCZtv+kgHA4rKysr1WM8ssgvcWSXOLJLXCqzKyws/J/b454hHz9+XDdu3NArr7yicePGybIsbdq0SbW1tSoqKtL58+c1Y8YMFRcXa+/evVq3bp2+/PJL2bYd9+w4MzPzgYONtlTt11TBYJBMRoD8Ekd2iSO7xKUqu8FOBuMW8qJFi7R9+3atXr1ad+/eVSAQ0I9//GM1NDToscce04QJE9TQ0CCPx6OSkhKVl5fLtm3V1dUl9SAAAEhncQt5/PjxeuONN+7b3tzcfN82v98vv9+fnMkAABhDuDEIAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIABKGQAAAxAIQMAYAAKGQAAA1DIAAAYgEIGAMAAFDIAAAagkAEAMACFDACAAdzxFkSjUdXU1Ojq1atyuVxqbGyU4zjatm2bLMvStGnTVF9fr4yMDDU1NamtrU1ut1uBQEBFRUUP4xgAAHjkxS3k06dPS5Kam5vV0dERK+SqqirNnTtXdXV1OnXqlPLz89XZ2amWlhb19PTI7/ertbV11A8AAIB0ELeQFyxYoGeeeUaS1N3drQkTJqitrU1z5syRJM2fP19nz55VQUGBfD6fLMtSfn6+otGoent7lZeXN6oHAABAOohbyJLkdrtVXV2tkydP6s0339Tp06dlWZYkKTs7W319ferv71dubm7sa77bPlghRyIRBYPBER7C9woLC4e8Npn7TQfhcJhMRoD8Ekd2iSO7xJmY3ZAKWZL27NmjLVu26KWXXlIkEoltD4VCysnJkcfjUSgUume71+sd9DUzMzOHVaLJlKr9mioYDJLJCJBf4sgucWSXuFRlN9ibgLifsj5+/LjefvttSdK4ceNkWZZmzpypjo4OSdKZM2dUUlKi4uJitbe3y7ZtdXd3y7ZtLlcDADBEcc+QFy1apO3bt2v16tW6e/euAoGAnnzySdXW1mrfvn2aOnWqSktL5XK5VFJSovLyctm2rbq6uocxPwAAaSFuIY8fP15vvPHGfduPHDly3za/3y+/35+cyQAAGEO4MQgAAAagkAEAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGIBCBgDAABQyAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIAB3IM9eefOHQUCAV2/fl0DAwPasGGDfvSjH2n9+vV64oknJEkrV67UkiVL1NTUpLa2NrndbgUCARUVFT2M+QEASAuDFvKJEyeUm5urvXv36uuvv9YLL7ygjRs3au3ataqsrIyt6+rqUmdnp1paWtTT0yO/36/W1tZRHx4AgHQxaCEvXrxYpaWlsccul0tXrlzR1atXderUKU2ZMkWBQEAXLlyQz+eTZVnKz89XNBpVb2+v8vLyRv0AAABIB4MWcnZ2tiSpv79fmzdvVlVVlQYGBrR8+XLNnDlThw4d0oEDB+T1epWbm3vP1/X19cUt5EgkomAwmITD+FZhYeGQ1yZzv+kgHA6TyQiQX+LILnFklzgTsxu0kCWpp6dHGzdu1KpVq/Tcc8/p1q1bysnJkSQtXLhQDQ0NevbZZxUKhWJfEwqF5PV64+48MzNzWCWaTKnar6mCwSCZjAD5JY7sEkd2iUtVdoO9CRj0U9Y3b95UZWWltm7dqrKyMknSunXrdPnyZUnS+fPnNWPGDBUXF6u9vV22bau7u1u2bXO5GgCAYRj0DPmtt97SrVu3dPDgQR08eFCStG3bNu3evVuPPfaYJkyYoIaGBnk8HpWUlKi8vFy2bauuru6hDA8AQLoYtJBrampUU1Nz3/bm5ub7tvn9fvn9/uRNBgDAGMKNQQAAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGIBCBgDAABQyAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIABKGQAAAzgHuzJO3fuKBAI6Pr16xoYGNCGDRv01FNPadu2bbIsS9OmTVN9fb0yMjLU1NSktrY2ud1uBQIBFRUVPaxjAADgkTdoIZ84cUK5ubnau3evvv76a73wwgv66U9/qqqqKs2dO1d1dXU6deqU8vPz1dnZqZaWFvX09Mjv96u1tfVhHQMAAI+8QQt58eLFKi0tjT12uVzq6urSnDlzJEnz58/X2bNnVVBQIJ/PJ8uylJ+fr2g0qt7eXuXl5Y3u9AAApIlBCzk7O1uS1N/fr82bN6uqqkp79uyRZVmx5/v6+tTf36/c3Nx7vq6vry9uIUciEQWDwZEeQ0xhYeGQ1yZzv+kgHA6TyQiQX+LILnFklzgTsxu0kCWpp6dHGzdu1KpVq/Tcc89p7969sedCoZBycnLk8XgUCoXu2e71euPuPDMzc1glmkyp2q+pgsEgmYwA+SWO7BJHdolLVXaDvQkY9FPWN2/eVGVlpbZu3aqysjJJ0vTp09XR0SFJOnPmjEpKSlRcXKz29nbZtq3u7m7Zts3lagAAhmHQM+S33npLt27d0sGDB3Xw4EFJ0quvvqpdu3Zp3759mjp1qkpLS+VyuVRSUqLy8nLZtq26urqHMjwAAOli0EKuqalRTU3NfduPHDly3za/3y+/35+8yQAAGEO4MQgAAAagkAEAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGIBCBgDAAGOykMN3w0ldBwDASMX9xyXSUZY7S9ZOK+46p955CNMAADBGz5ABADANhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwwJAK+dKlS6qoqJAkdXV1ad68eaqoqFBFRYX+/Oc/S5KamppUVlamFStW6PLly6M3MQAAaSjurTPfffddnThxQuPGjZMk/f3vf9fatWtVWVkZW9PV1aXOzk61tLSop6dHfr9fra2tozc1AABpJu4Z8uTJk7V///7Y4ytXrqitrU2rV69WIBBQf3+/Lly4IJ/PJ8uylJ+fr2g0qt7e3lEdHACAdBL3DLm0tFRffPFF7HFRUZGWL1+umTNn6tChQzpw4IC8Xq9yc3Nja7Kzs9XX16e8vLxBXzsSiSgYDI5g/HsVFhYm7bW+k8z5TBYOh8fMsY4G8ksc2SWO7BJnYnbD/teeFi5cqJycnNifGxoa9OyzzyoUCsXWhEIheb3euK+VmZk5KiWaTKbPlyzBYHDMHOtoIL/EkV3iyC5xqcpusDcBw/6U9bp162If2jp//rxmzJih4uJitbe3y7ZtdXd3y7btuGfHAADge8M+Q96xY4caGhr02GOPacKECWpoaJDH41FJSYnKy8tl27bq6upGY1YAANLWkAp54sSJOnr0qCRpxowZam5uvm+N3++X3+9P7nQAAIwR3BgEAAADUMiDCN8Nj8paAAD+27B/hjyWZLmzZO20hrTWqXdGeRoAQDrjDBkAAANQyAAAGIBCBgDAABQyAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIABKGQAAAxAIQMAYIAhFfKlS5dUUVEhSbp27ZpWrlypVatWqb6+XrZtS5KamppUVlamFStW6PLly6M3MQAAaShuIb/77ruqqalRJBKRJDU2Nqqqqkp//OMf5TiOTp06pa6uLnV2dqqlpUX79u3Tzp07R31wAADSSdxCnjx5svbv3x973NXVpTlz5kiS5s+fr3PnzunChQvy+XyyLEv5+fmKRqPq7e0dvakBAEgz7ngLSktL9cUXX8QeO44jy7IkSdnZ2err61N/f79yc3Nja77bnpeXN+hrRyIRBYPBRGe/T2FhYdJeKxHJPJaHLRwOP9Lzpxr5JY7sEkd2iTMxu7iF/N8yMr4/qQ6FQsrJyZHH41EoFLpnu9frjftamZmZKS/RZHqUjyUYDD7S86ca+SWO7BJHdolLVXaDvQkY9qesp0+fro6ODknSmTNnVFJSouLiYrW3t8u2bXV3d8u27bhnxwAA4HvDPkOurq5WbW2t9u3bp6lTp6q0tFQul0slJSUqLy+Xbduqq6sbjVkBAEhbQyrkiRMn6ujRo5KkgoICHTly5L41fr9ffr8/udMBADBGcGMQAAAMQCEDAGAAChkAAANQyAAAGIBCBgDAABQyAAAGoJABADAAhQwAgAEoZAAADEAhAwBgAAoZAAADUMgAABiAQgYAwAAUMgAABqCQAQAwAIUMAIABKGQAAAxAIQMAYAB3ol+4bNkyeb1eSdLEiRNVXl6u1157TS6XSz6fT5s2bUrakAAApLuECjkSiUiSDh8+HNu2dOlS7d+/X5MmTdKvf/1rdXV1acaMGcmZEgCANJfQJetPPvlEt2/fVmVlpdasWaOPP/5YAwMDmjx5sizLks/n0/nz55M9KwAAaSuhM+SsrCytW7dOy5cv17/+9S+9/PLLysnJiT2fnZ2tf//730kbEgCAdJdQIRcUFGjKlCmyLEsFBQXyer365ptvYs+HQqF7CvpBIpGIgsFgIiP8T4WFhUl7rUQk81getnA4/EjPn2rklziySxzZJc7E7BIq5GPHjunTTz/Vjh07dOPGDd2+fVvjx4/X559/rkmTJqm9vX1IH+rKzMxMeYkm06N8LMFg8JGeP9XIL3FklziyS1yqshvsTUBChVxWVqbt27dr5cqVsixLu3fvVkZGhrZs2aJoNCqfz6dZs2YlPDAAAGNNQoX8+OOP6/e///19248ePTrigQAAGIu4MQgAAAagkB+y8N1wUtcBANJDwnfqQmKy3Fmydlpx1zn1zkOYBgBgCs6QAQAwAIUMAIABKGQAAAxAISdJqj6ExYfEACA98KGuJEnVh7X4kBgApAfOkAEAMACFbCguMQPA2MIla0NxKRoAxhbOkAEAMACFDACAAShkAAAMQCEDAGAAChkAAANQyAAAGIBCxj24FScApAa/hzxGhO+GleXOeuDzhYWFklL7+8/xZhzuOgB4lCS1kG3b1o4dO/SPf/xDjz/+uHbt2qUpU6YkcxdI0KNQtNwMBcBYltRC/utf/6qBgQH96U9/0sWLF/X666/r0KFDydwFHkEU7YOl6qoAVyMA8yS1kC9cuKB58+ZJkmbPnq0rV64k8+VhEL5RP9hwsuFfCQNSY1LBpCGte5jf65JayP39/fJ4PLHHLpdLd+/eldvNj6rTzVC/oUvJ/6Zu+tndo5ANMNZ5sjzGvSm1HMdJ2t4aGxs1a9YsLVmyRJI0f/58nTlz5oHrL168qMzMzGTtHgAAo0UiEc2ePft/PpfUU9fi4mKdPn1aS5Ys0cWLF/WTn/xk0PUPGgoAgLEmqWfI333K+tNPP5XjONq9e7eefPLJZL08AABpK6mFDAAAEsOdugAAMACFDACAAShkAAAMkBa/IMwtO4fu0qVL+t3vfqfDhw/r2rVr2rZtmyzL0rRp01RfX6+MjAw1NTWpra1NbrdbgUBARUVFqR475e7cuaNAIKDr169rYGBAGzZs0FNPPUV+QxCNRlVTU6OrV6/K5XKpsbFRjuOQ3TB89dVXevHFF/Xee+/J7XaT3RAtW7ZMXq9XkjRx4kSVl5frtddek8vlks/n06ZNm8zqDycNfPjhh051dbXjOI7zt7/9zVm/fn2KJzLTO++84/zyl790li9f7jiO47zyyivORx995DiO49TW1jp/+ctfnCtXrjgVFRWObdvO9evXnRdffDGVIxvj2LFjzq5duxzHcZze3l7n6aefJr8hOnnypLNt2zbHcRzno48+ctavX092wzAwMOD85je/cRYtWuR89tlnZDdE4XDYWbp06T3bnn/+eefatWuObdvOr371K+fKlStG9UdaXLLmlp1DM3nyZO3fvz/2uKurS3PmzJH07U1czp07pwsXLsjn88myLOXn5ysajaq3tzdVIxtj8eLF+u1vfxt77HK5yG+IFixYoIaGBklSd3e3JkyYQHbDsGfPHq1YsUI/+MEPJPH3dqg++eQT3b59W5WVlVqzZo0+/vhjDQwMaPLkybIsSz6fT+fPnzeqP9KikB90y07cq7S09J7bmDqOI8v69tZx2dnZ6uvruy/L77aPddnZ2fJ4POrv79fmzZtVVVVFfsPgdrtVXV2thoYGlZaWkt0QffDBB8rLy4sVhsTf26HKysrSunXr9Ic//EE7d+7U9u3bNW7cuNjzD8oulf2RFoXs8XgUCoVij23b5v7ZQ5CR8f1//lAopJycnPuyDIVCsZ/BjHU9PT1as2aNli5dqueee478hmnPnj368MMPVVtbq0gkEttOdg/W2tqqc+fOqaKiQsFgUNXV1fec+ZLdgxUUFOj555+XZVkqKCiQ1+vVN998E3v+Qdmlsj/SopCLi4tj98weyi078a3p06ero6NDknTmzBmVlJSouLhY7e3tsm1b3d3dsm1beXl5KZ409W7evKnKykpt3bpVZWVlkshvqI4fP663335bkjRu3DhZlqWZM2eS3RC8//77OnLkiA4fPqzCwkLt2bNH8+fPJ7shOHbsmF5//XVJ0o0bN3T79m2NHz9en3/+uRzHUXt7eyw7U/ojLU4jFy5cqLNnz2rFihWxW3YivurqatXW1mrfvn2aOnWqSktL5XK5VFJSovLyctm2rbq6ulSPaYS33npLt27d0sGDB3Xw4EFJ0quvvqpdu3aRXxyLFi3S9u3btXr1at29e1eBQEBPPvkk/+8liL+3Q1NWVqbt27dr5cqVsixLu3fvVkZGhrZs2aJoNCqfz6dZs2bpZz/7mTH9wa0zAQAwQFpcsgYA4FFHIQMAYAAKGQAAA1DIAAAYgEIGAMAAFDIAAAagkAEAMACFDACAAf4PaDJP0NWviBwAAAAASUVORK5CYII=\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": "markdown",
   "metadata": {},
   "source": [
    "# Removing the NULL values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6690fd0>"
      ]
     },
     "execution_count": 22,
     "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, palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that weather passengers in the higher classes tend to be older. We'll will use this average age valuews( THE MID LINE ) to input missing age values using Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):  ## if there is null value in Age column\n",
    "        \n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "        \n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age', 'Pclass']].apply(impute_age ,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x6673b90>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEnCAYAAAAKMZAQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdGklEQVR4nO3df3zP9f7/8fsbe5v83JqlsiihOHwOwpkOaU51jkqITCJl6KzUDGmjGZWlkR9dcopCy48dOxf6YTUdumQ5JUonUisbGWEbov1g0/b6/LHL+/2ZX32+34/X89nO3K6Xi8uF7XJ5PebH7p6v54/H0+M4jiMAgBW1fusvAAAuJYQuAFhE6AKARYQuAFhE6AKARYQuAFhU59c+eVutwba+DgCXkPUHv7Ja746r/stqvX9WpF3wc4x0AcAiQhcALCJ0AcAiQhcALCJ0AcAiQhcALCJ0AcAiQhcALCJ0AcAiQhcALCJ0AcAiQhcALCJ0AcCiX+0yBgAm2O76Zbur2a8hdAFYV/NbO174c0wvAIBFhC4AWEToAoBFhC4AWEToAoBFhC4AWEToAoBFhC4AWMThCADW2T6sUJ0QugCsq+kn0n4NoQvAuuoUgrYRugCsY6QLABZVpxC0jdAFYN2lPNJlyxgAWEToAoBFhC4AWMScLgDrqtMcq22ELgDrWEgDAFjBSBeAddVp5GkboQvAOqYXAABWELoAYBGhCwAWEboAYBGhCwAWEboAYBGhCwAWEboAYBGhCwAWEboAYBGhCwAWEboAYBENbwBYV50a0NhG6AKwji5jAAArCF0AsIjQBQCLCF0AsIjQBQCLCF0AsIjQBQCL2KcLwLrqtG/WNkIXgHUcjgAAWEHoAoBFTC8AsK46ve7bRugCsI45XQCAFYQuAFhE6AKARYQuAFhE6AKARYQuAFhE6AKARezTBWBdddo3axsjXQCwiNAFAIsIXQCwiDldANZdyr0XCF0A1lWnELSN0AVgHSNdALCoOoWgbSykAYBFhC4AWEToAoBFzOkCsO5SXkhjpAsAFhG6AGAR0wsArKtOr/u2EboArGNOFwBgBaELABYRugBgEXO6AKyrTnOsthG6AKxjIQ0AYAWhCwAWMb0AwLrq9LpvG6ELwDrmdAEAVhC6AGARoQsAFhG6AGARoQsAFhG6AGARoQsAFrFPF4B11WnfrG2MdAHAIkIXACxiegGAdRwDBgBYQegCgEWELgBYROgCgEWELgBYROgCgEWELgBYxD5dANZVp32zthG6AKzjcAQAwApGugCsq04jT9sIXQDWMb0AALCC0AUAi5heAGBddXrdt43QBWDdpTynS+gCsK46haBtzOkCgEWELgBYxPQCAOsu5TldRroAYBGhCwAWMb0AwLrq9LpvGyNdALCIkS4A61hIAwBYQegCgEWELgBYROgCgEWELgBYROgCgEWELgBYxD5dANZVp32ztjHSBQCLGOkCsI4TaQAAKwhdALCI0AUAiwhdALCI0AUAiwhdALCI0AUAi9inC8C66rRv1jZCF4B1HI4AAFhB6AKARYQuAFhE6AKARYQuAFjE7gUA1lWn3QS2EboArGPLGADACka6AKyrTiNP2whdANYxvQAAsILQBQCLCF0AsIjQBQCLCF0AsIjQBQCLCF0AsIjQBQCLOBwBwLrqdFjBNkIXgHWcSAMAWEHoAoBFTC8AsK46ve7bRugCsI45XQCAFYQuAFhE6AKARczpArCuOs2x2sZIFwAsInQBwCKmFwBYx5YxAIAVhC4AWEToAoBFhC4AWEToAoBFhC4AWEToAoBF7NMFYF112jdrG6ELwDoORwAArCB0AcAiQhcALCJ0AcAiQhcALCJ0AcAitowBsK46beGyjdAFYN2lvE+X0AVgXXUKQdsIXQDWXcojXRbSAMAiRroArKtOI0/bCF0A1jG9AACwgtAFAIsIXQCwiNAFAIsIXQCwiNAFAIsIXQCwiH26AKyrTvtmbSN0AVjH4QgAgBWELgBYxPQCAOuq0+u+bR7HcZzf+osAgEsF0wsAYBGhCwAWEboAYBGhCwAWEboAYBGhCwAWEboAYBGhi9/EDz/8oE2bNunw4cNiqzguJYTu/4eKigqVl5fr888/V1lZmfF6NTWYli9frmnTpmnu3LnKyMjQM888Y6VuRUWFjhw5UqP+LCUpLS3tjF+npKRYqVtUVKTvvvtOJSUlVurVFBd9DDguLu6Cn0tKSrrYx59j27ZtF/xc165dXa/nk5ycrLCwMB08eFC7du1SSEiIZs2aZaze8uXL9c9//lMnTpxQ//79lZubq4SEBGP1Zs+erdjYWNWqVUuFhYWaMmWKFixYYKRWenq6Vq5cqREjRmjkyJG69957jdSp6oMPPtDzzz+vRo0aqbi4WImJibr55puN1Ttw4IDWr1+vkydP+j/22GOPuVpj3bp1+vDDD/XZZ59py5YtkqTy8nLt3r1bI0aMcLXW2TIyMvTKK6+ovLxcf/7zn+XxeBQdHW2kVlFRkTIzM88Y6PTv39/1OhEREfJ4PP5f16lTR7/88ou8Xq/ef/991+pcdOj27dtXkrRq1Sp16tRJnTt31s6dO7Vz586L/uLOZ9WqVZKk3NxcnT59Wh06dNA333yj+vXr68033zRSU5K++OILTZo0ScOHD9ebb76pBx980FgtyX4web1ejRw5UiNGjNCCBQv00EMPGavlG2n6/oF7vV5jtXwWLlyotLQ0XX755Tpy5IgeeeQRo6E7YcIE9ezZUyEhIcZq9OzZU02bNtXx48c1ZMgQSVKtWrUUFhZmrKbPsmXLtHr1ao0aNUrR0dG69957jYVudHS0QkNDdeWVV0rSGcHopoyMDDmOo+nTpysyMlIdO3bUN998o5UrV7pa56JDt2fPnpKkpUuXavTo0ZKkLl26GPumffHFFyVJY8aM0cKFC1WnTh2Vl5drzJgxRur5VFRUaMeOHWrevLnKysp07Ngxo/VsB9O4ceM0efJkxcTEKD4+XgMGDDBW684779SwYcN08OBBjR49Wn/605+M1fJp0qSJLr/8cklSSEiIGjRoYLReYGCg6yPbszVu3Fjdu3dX9+7d9emnn2r//v3q2LGjmjRpYrSuVBnuXq9XHo9HHo9H9erVM1bLcRzNnj3b2PN9fN9jvj9HSWrXrp327t3rah3XuoyVlJTo008/VYcOHfTll1/q9OnTbj36vAoKCvw/Ly8vNx6C99xzj5555hnNnDlTycnJxl/f7rrrLqvB9MADD6h9+/bauHGjEhMT9e233xqbax0+fLh69Oih77//Xtddd53atm1rpE5VDRo00KhRo9S1a1d9/fXXOnXqlP8/8NjYWNfq+L5BQ0JC9O6776p9+/b+/zivvfZa1+pU9eKLL+rw4cPKyclRQECAFi1a5P+9mXLTTTdpwoQJysvLU0JCgjp06GCsVtu2bfXVV1/pxhtv9H/M5CCkYcOGmjdvnjp27Kgvv/xSV199tavPd63LWE5OjubPn6/s7Gy1atVKCQkJatq0qRuPPq8VK1YoJSVFbdq0UXZ2tsaNG+ef6jDt0KFD/lcdk3JycqwF00cffaTevXv7f52SkmLsP5az1wECAgLUrFkzDRs2TI0bNzZSc+3atRf8nJuj+uHDh5/34x6Px9gC17Bhw7RixQr/1Nd9992n1atXG6nlU1hYqC+//NL/7zMiIsJYrX79+qmoqMj/a4/Ho40bNxqrV1JSorVr1yo7O1vXXXed7r//ftWuXdu157s20m3VqpWxhZfzGTZsmO655x7t2bNHzZs3V3BwsNF6KSkpCgwM1M8//6w1a9aoZ8+ev7qIeLF27Nih9PR0lZaW6rPPPpMkJSYmGqt30003ad68ecrPz1fv3r11yy23GKtVWlqqsLAw3XTTTfrqq6+0c+dOBQcHa/LkyXrllVdcr5eVlaUBAwaorKxMaWlp8nq9uvfee1Wrlvubd3zrCqWlpcrJyVG7du20YcMGo3+e5eXlKi0tlcfjUXl5uZHf19nGjBmjVatWqVevXsZrvfPOO8ZrVFW3bl15vV4FBQWpTZs2OnHihKv5ctGh+8c//vGCn9u8efPFPv6Cdu/erWnTpqmwsFB33323WrdurVtvvdVYvfT0dL355puKiopSenq68YW0yZMna/To0WrUqJHROj7x8fHq1auXtm7dqpCQEE2ZMkXLly83UuvYsWP+19+ePXvq4YcfVkxMjIYNG+Z6raVLl+q9997TqlWr9MILL+jgwYO66qqrNHPmTE2dOtX1ej6TJk1SeHi4f07w/fff15w5c4zUevDBBzVw4EAdO3ZMgwcP1siRI43Uqapx48Z64403dO211/pD/tey4P9ixowZSkhI0JAhQ85ZPEtNTXW1VlUJCQkKDQ3VJ598ot/97neaPHmyFi9e7NrzLzp0fcH6888/WwsISXr22WeVlJSkqVOnatCgQYqKijIauh6PRwUFBQoJCZHH49GJEyeM1ZKkFi1aaODAgUZrVHX8+HENGjRI77zzjjp37mx0L2tRUZFycnLUqlUr5eTkqKSkRD/99JOR/Z6ZmZlKTU2Vx+PRunXrtH79ejVu3FiRkZGu16oqLy9PQ4cOlSSNHj36gtMObvjLX/6iHj16aN++fWrevLmrr8IXEhQUpKysLGVlZfk/5nbo+nZDmJ6fPltubq6ee+45ff7554qIiNCiRYtcfb5r0wtjx471b+eypUWLFvJ4PAoODlb9+vWN1urevbseeOABzZkzRzNnztTtt99utN4dd9yh8ePHq1WrVv6PmV4Nz8nJkSQdPnzY6CtqQkKCJk2apPz8fAUGBmrAgAF677339Mgjj7heq1atWqpdu7Z27dqlsLAw/5yxjQMSe/fu1bXXXqvc3FxVVFQYq/PMM8/o6aefVseOHfXxxx/r2Wef1fr1643Vk87dg5+fn+96Dd92u4qKCr3wwgv64Ycf1Lp1a02aNMn1WlX5FuY9Ho+Kiopc/15wLXRtvG6cXS81NVUnT55Uenq68VH2+PHjNX78eElShw4dFBAQYLTeypUrddttt1l7e5g6dari4+OVnZ2t6OhoPfvss8ZqdezYUYmJiVq+fLn+9a9/6ejRo3r00UeN1du7d6/WrFnjX+zZvXu38XnPKVOmKCYmRkePHlVoaKhmzJhhrFaDBg00e/ZslZSUaPfu3XrttdeM1fJZsGCBVq5cqdOnT+vUqVNq2bKl0tPTjdSKj49XVFSUOnfurG3btik+Pl5Lly41UkuSYmJiNHToUBUUFGjIkCGaMmWKq893LXRtvG5UNXPmTL3yyisKCgrS119/reeee85YLUnauHGj/x+Z4zg6fvy43n33XWP1GjdubHzvsSTt2rVLU6ZMUVpamkaNGqXExEQVFxfr0KFDateunau1ysrKlJ6erhUrVsjr9aqoqEgbN25UYGCgq3WqeuKJJ/Tkk0/q6quvVmxsrLZu3apJkyZp/vz5xmpKlScn3377baM1fMaPH69Zs2Zp3759Rg8IVZWZmanMzEzNnDlTDz30kKZPn26sVu3atf0LkREREXrjjTeM1ZKkTp06af369Tp27JiCgoK0f/9+V5/vWugmJSVp7969ys3NVdu2bRUaGurWo89rwYIFuu+++3T99dcbrePz8ssv6+mnn1Zqaqq6d++uTz75xGi9oKAgJSQkqF27dv5FBN+pIzfNnTtXzz//vAICAjRv3jwtXrxYLVq0UFRUlPr06eNqrYiICN11112aPXu2WrZsqaioKKOBK1WOqqv2Jvj973+vDRs2GH9T2bRpk0aOHGl0fvXsQc2RI0f8HzO5iC1VHjbxer0qLi5WixYtzjju7Bbf76FevXpavHixunbtqh07dhg95SdVniZcsGCBgoODlZqaqqVLl7o6XeNa6FbtFTBgwADt27fPaK+Azp07Kzk5WcXFxRo4cKD69u1r9Bs4KChInTp1UmpqqgYOHKg1a9YYqyVVzldLld9IJjmOoxtuuEF5eXk6efKk2rdvL0lGXr9HjBihdevW6ccff9SgQYOsNp7ZuXOnpk2bpiNHjuiqq67S9OnTje59/umnn9SzZ081b97cf2rL7RX3qsFaUlKiyy67THl5ebriiitcrXM+zZo10z/+8Q/Vq1dPc+bMOWMfrVt80xVNmjTRnj17tGfPHknmT2eGh4dr0qRJKiwsVMOGDd3f8+y4JDIy0qmoqHAeeOABx3EcZ+DAgW49+lfl5eU5MTExTpcuXYzWGTt2rLN161YnNjbWyczMdG6//Xaj9Ryn8vf2448/OgcOHHC2b99upMbIkSMdx3GctLQ0Jy4uznEcxyktLXX69etnpJ7jOM5nn33mTJw40enWrZvzwgsvON99952xWj5Dhgxxdu/e7TiO42RlZTlDhw41Wu/AgQPn/DDlpZdecpKSkhzHcZxx48Y5r776qrFaL7/8suM4jlNeXu5s377dKSwsdFJSUvx/tjbk5eUZeW5paan/x2uvveaMGjXK/2s3uTbSdSz3Cjh48KDWrl2rDz74QO3atXN1H935TJ8+XXv27NFf//pXzZ8/X48//rjRevHx8fr3v/+tkydP6tSpUwoLCzNyyig8PFyRkZE6fPiw/va3vyk3N1eJiYlGT/d169ZN3bp1088//6y3335bTz75pN566y1j9aTKDe++qai2bdsan1745ZdflJGR4T8On5+fb2wx7cMPP/S/eS1YsECRkZHG1gO2bNmi6Oho1apVS3PnzlVKSorR7XCSvUU7X7c06X/yzPcxN0/AuRa6tpuYjBs3ToMHD9aKFSuMNi+p2uyiWbNmkioXLkx1OvLZs2eP0tPTlZCQoPHjx+uJJ54wUmfMmDHq06ePgoODFRQUpNzcXA0dOlS33XabkXpVNWrUSMOHDzf6Tfv3v/9dUmWbvsTERP+8oOmGN5MnT9att96q7du3KzQ01GjPWY/Ho7KyMnm9Xv9CrylVn22yTlW2Fu0+/PBDSdLbb7+te+65x0gNycXQtdXE5PDhw2rWrJmSk5P9BxZ8zW9MNBSpOi/t8XjkOI4/cE02i65fv748Ho9KSkoUHBxstIFQ1b3A11xzja655hpjtWzz/dvo1KmTpMr/RBs2bHhG8xQTAgMDNXbsWP3www9KSkrS/fffb6xWZGSk7r77brVp00Z79uxRVFSUsVpVBxumBx4+NhbtqkpLS/vPCN2qfQgyMzONNTFZunSp4uLiNG3atDM+bqqhyG9xll6S2rdvr9dff12hoaEaP368ysvLjdarqQYNGqRmzZq53p7vf+M4jgoKClRSUqKSkhKjJxgHDx6sPn36aP/+/QoLCzPah2TXrl2KjIyU4zjKzs72/9zEQqFP1UW72bNnG1m0q6qsrEz9+/c/48yBm0e4XesyFhsbe04TkxtvvFFZWVlGmphs2LBBERERVpp7SNLjjz+u8PBwDR06VIsXL1ZWVpaRs/RV5zaLiopUt25dnTp1SqdOnfL3K8b/u6SkJMXFxWn48OH+49u1a9dWgwYNjL2pFBUV6dtvv1V2drZCQ0M1depU9e/fX5MnT3a1zsKFCxUdHa3Y2NhzRp2m+jz8+OOPF/yc2y0QpcrpoQEDBqigoECHDh1SVlaWwsPDz3g7c9vWrVvP+Vi3bt1ce75rI12bTUwk6ZNPPtH8+fMVERGhQYMGGe+Wb+ssve8oro/jOFqzZo0CAwMJ3f+Dfv36qX///lq9erU++ugjJSYmqmHDhsZOwC1fvlxLlixRnTp1NHXqVPXq1cv1/c4+X3zxhSQZ7yNRlYlgvZCXXnpJu3fvVr9+/XT11VfLcRwtW7ZMJ06cMHqCsU2bNtq8ebN++eUXOY6j/Pz86hm6ZzcxKS4uNtbERKqcay0rK9PGjRs1Y8YMnT59WsuWLTNSy8fGWfoJEyb4f75v3z499dRT6t27t+Lj443Uq+l8hz+8Xq/xwx9S5b1lGRkZKioq0pNPPmm09aFvnt/NQKhOMjMztXr1av8ovnnz5po7d64iIyONhu7jjz+uli1b6vvvv1fdunVdvxXDtdCt2sTkyiuvVEJCgrEmJj47duzQ5s2bdfToUd1xxx3G6hQVFWnixInWztJLlU3a33jjDcXFxRntnlbTORc4/GFqEcjr9crr9Rpf/JQqr5W5UAcuN2/D+K1cdtll5/w9BQQEGG9uJVW2lYyLi9Nzzz3n+tu6a6HbsWPHc05pmbzCo2/fvrrhhhs0ePBgo30Xzve6aFJeXp7i4uLUuHFjpaWlGbtJ4VLheyP5+OOPFR4eLqlyocTGteGmt1QFBgYauwKoOggMDPQvDvrs37/fyq6J0tJSnTx50r+DyE2uhe5bb72lRYsWqbS01P8xk1dqDBw40OjWGB+br4tS5d1oAQEB+sMf/nDOaNrU4khNZvvwR3Z2tiZMmOBf3a86XeT2319ISIjRC0R/axMnTlR0dLTCw8MVFhamgwcPavPmzZo1a5bRusOGDdOyZct0880365ZbblGXLl1cfb5ruxfuvPNOLVy48Iy7w0yeShsxYoSWLl1qvGHziBEj/KvcDz74oPEOR+dbOfWpqXN3puXk5Jxx+OO7774zdvjD5t/frFmzXN8RUd0UFhZq48aNys/P11VXXaXevXsbP9ji89NPPykgIMD1eq6NdMPCwvxNWmyw0VDkbDZO4BCs7rN5+MPm319ND1yp8mbe/v37W625adMmzZgxQ40aNVJJSYlmzJih7t27u/Z810a6MTExKioq0o033uifczE5mX++/YImtrP06NFD4eHhchxHW7Zs8c8LSrzuAzXR4MGD9eqrryo4OFgFBQV69NFHXe174tpI1/QJrbOd70ptE9fZzJs3z/9zm/shAfw26tev7z/V17Rp0+q7Zezuu+/W2rVrdejQIXXv3l2tW7d269Hn5Wtk7DiOvvnmG2P7ZnndBy4Nvu135eXlGjt2rLp06aIdO3a4vjblWuhOmzbN6LXFZzt71GljJwOAmsu3/a7qNjwTB2hcC13T1xafrWoDk/z8fB06dMhoPQA1m2/7XWFhobZu3XrG9lc3uRa6pq8tPltCQoK/gUmTJk0uiZVcAOY9/PDDuv7669WwYUNJlacX3dzX7Vronn1tsaleAb7ba6s2MDl16pTxI5cALg0NGzZUUlKSsee7tmXM59ixY2rUqJHq1HEtz88QFRWliRMn6oYbblDfvn2VnJzsb2Biep8ugJpvyZIlqlev3hk3jXft2tW157uWjO+//74qKipUVlam5ORkjRo1SqNGjXLr8X4XamBiq68ugJrt888/V1lZmbZt2yapcnqhWobukiVLtGjRIsXGxuqjjz7Sww8/bCR0L9TApLi42PVaAC49JSUlRtvEuha6devWlVS5sdh3n5EJv8XttQAuHa1bt9a6devUrl07/+laN7u5uTan+9RTT2nr1q16+umntWvXLhUUFBi7tdNmAxMAlxbf1U6+aCwrK/PfKu0GVxfSiouLVb9+fR05csR/YgwA/hPExMT4j/2//vrr/unR4cOH+y+odYNrq0/btm3TF198oU2bNikyMlLvvvuuW48GAOOOHj3q//mmTZv8P3e7abproZucnKyWLVsqJSVFq1atYvsWgP9YJtu4uha6devW1eWXX646deqoadOmKisrc+vRAGBc1RGtySuBXNu90KBBAz300EO6//77tWLFijNukACA6u58Vy05jqOcnBxX67i2kFZWVqbc3Fxdf/31+v7779WyZUuj1/UAgJtsXbXkWuju27dPGRkZ/h4I+fn5xq8pB4D/NK7N6fq6fG3fvl0HDhzQ8ePH3Xo0ANQYroVuYGCgxo4dqyuuuELPP/+8jhw54tajAaDGcC10HcdRQUGBSkpKVFJSohMnTrj1aACoMVwJ3aKiIj322GPasGGD+vXrpz59+qhXr15uPBoAapSLXkhbvny5lixZojp16mjq1KmELQD8iose6a5bt04ZGRlKTU1VSkqKG18TANRYFx26Xq9XXq9XwcHBXJkDAP8LV69bMHleGQBqgoue0+3Ro4fCw8PlOI62bNniv81BkubMmXPRXyAA1CQXHbq2js4BQE3g+m3AAIAL4wpdALCI0AUAiwhdALCI0AUAiwhdALDovwEuOkC/DosI+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop Cabin column because there are so  any null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>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>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>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>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>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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x67430d0>"
      ]
     },
     "execution_count": 30,
     "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(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 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          891 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  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 62.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train['Embarked'], drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'], drop_first=True)\n",
    "embark = pd.get_dummies(train['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex', 'Embarked', 'Name', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, sex, embark], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2       1  38.0      1      0  71.2833     0  0  0\n",
       "2              3       3  26.0      0      0   7.9250     0  0  1\n",
       "3              4       1  35.0      1      0  53.1000     0  0  1\n",
       "4              5       3  35.0      0      0   8.0500     1  0  1\n",
       "..           ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train.drop('Survived', axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train['Survived']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "...     X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[154,  21],\n",
       "       [ 36,  84]], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8067796610169492"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So the model is 80.6% accurate"
   ]
  }
 ],
 "metadata": {
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