titanic_5th_try.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#আমরা এখানে গত ট্রাইতে যা যা বলেছি তা তা ট্রাই করবো । তাই ঐ অংশগুলোর ডেস্ক্রিপশন দেবো । বাকিগুলো একই থাকছে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # Apply the default seaborn theme, scaling, and color palette\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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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>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",
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       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data = [df, test] \n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Col           2\n",
       "Major         2\n",
       "Sir           1\n",
       "Jonkheer      1\n",
       "Lady          1\n",
       "Mme           1\n",
       "Capt          1\n",
       "Countess      1\n",
       "Ms            1\n",
       "Don           1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Rev         2\n",
       "Col         2\n",
       "Ms          1\n",
       "Dr          1\n",
       "Dona        1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(df[\"Title\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Name']\n",
    "del test['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(df[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"].fillna(df.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n",
    "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n",
    "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n",
    "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "যথারীতি আমরা বয়সের নাল ভ্যালু ফিক্স করেছি টাইটেল ভেদে । যেমন ধরেন, \n",
    "Mrs টাইটেল ধারী সবার মিডিয়ান কত তা বের করলাম । তারপর ঐ টাইটেলের মিসিং ভ্যালু গুলো এখানে পাওয়া মিডিয়ান দিয়ে ফিক্স করে দিলাম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(df[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</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>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin  Embarked  Title  \n",
       "0   7.2500   NaN         0      0  \n",
       "1  71.2833   C85         1      2  \n",
       "2   7.9250   NaN         0      1  \n",
       "3  53.1000  C123         0      2  \n",
       "4   8.0500   NaN         0      0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Fare\"].fillna(df.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে আমরা কেবিন ক্লাসের সাথে ভাড়ার গ্রুপবাই করেছি । মানে ধরেন, \n",
    "কেবিন ক্লাস ১ এর ভাড়ার মেডিয়ান বের করে তারপর ঐ কেবিন ক্লাসের যতগুলি ভাড়ার ফিল্ড নাল আছে তা ফিক্স করে দিয়েছি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n",
    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
    "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare Cabin  Embarked  Title  \n",
       "0   0.0   NaN         0      0  \n",
       "1   2.0   C85         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G6             4\n",
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "D              3\n",
       "F33            3\n",
       "              ..\n",
       "D47            1\n",
       "C82            1\n",
       "A23            1\n",
       "A7             1\n",
       "D9             1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Cabin\"].fillna(df.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে দেখেন আমি নাল ভ্যালু গুলোকে ফিক্স করেছি গ্রুপ বাই করে । গ্রুপ বাই করেছি তাদের কেবিন ক্লাস এবং কেবিন নাম্বার ধরে । \n",
    "মানে ধরেন কেবিন ক্লাস ১ এর যেসব কেবিন গুলো আছে তাদের মিডিয়ান দেখলাম কত । তারপর ঐ ক্লাসের যে কয়টি কেবিনের নাল ভ্যালু আছে তা মিডিয়ান দিয়ে ফিক্স করে দিলাম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"FamilySize\"] = df[\"SibSp\"] + df[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
    "for dataset in train_test_data:\n",
    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
    "df = df.drop(features_drop, axis=1)\n",
    "test = test.drop(features_drop, axis=1)\n",
    "df = df.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = df.drop('Survived', axis=1)\n",
    "target = df['Survived']\n",
    "train_data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Survived    891 non-null    int64  \n",
      " 1   Pclass      891 non-null    int64  \n",
      " 2   Sex         891 non-null    int64  \n",
      " 3   Age         891 non-null    float64\n",
      " 4   Fare        891 non-null    float64\n",
      " 5   Cabin       891 non-null    float64\n",
      " 6   Embarked    891 non-null    int64  \n",
      " 7   Title       891 non-null    int64  \n",
      " 8   FamilySize  891 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 62.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে আমরা নতুন করে নিয়েছি KNN(K nearest neighbors) এবং Gaussian Naive bayes কে । চলেন এই দুটি দিয়ে ট্রাই করে দেখি আমাদের প্রেডিকশন রেজাল্ট বাড়ে নাকি । <p>\n",
    "To know more about <a href=\"https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/\"> KNN </a> <p>\n",
    "To know more about <a href=\"https://hackernoon.com/implementation-of-gaussian-naive-bayes-in-python-from-scratch-c4ea64e3944d\"> GaussianNB </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.85393258 0.79775281 0.7752809  0.80898876 0.80898876\n",
      " 0.83146067 0.84269663 0.74157303 0.79775281]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf = DecisionTreeClassifier(class_weight='balanced',random_state=7)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.25"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decision tree Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ডিসিশন ট্রি এর একুরেসি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.77777778 0.84269663 0.80898876 0.75280899 0.84269663 0.80898876\n",
      " 0.83146067 0.80898876 0.78651685 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13,class_weight = 'balanced',random_state=7)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.92"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "র‍্যান্ডম ফরেস্টের একুরেসি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8        0.80898876 0.80898876 0.78651685 0.87640449 0.80898876\n",
      " 0.84269663 0.79775281 0.78651685 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.48"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN এর একুরেসি । ডিসিশন ট্রি, র‍্যান্ডম ফরেস্টের চেয়ে বেটার !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85555556 0.73033708 0.75280899 0.75280899 0.70786517 0.80898876\n",
      " 0.76404494 0.80898876 0.86516854 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf =GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GaussianNB score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GaussianNB এর একুরেসি দেখি বাকিদের চেয়েও খারাপ !চলেন লজিস্টিক রিগ্রেশনটাও ট্রাই করে দেখি । লজিস্টিক রিগ্রেশন জানতে ক্লিক করুন <a href=\"https://machinelearningmastery.com/logistic-regression-for-machine-learning/\"> এখানে </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8        0.7752809  0.80898876 0.78651685 0.80898876 0.82022472\n",
      " 0.80898876 0.84269663 0.82022472 0.85393258]\n"
     ]
    }
   ],
   "source": [
    "clf =LogisticRegression()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.26"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LogisticRegression score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "লজিস্টিক রিগ্রেশন ভালো প্রেডিক্টই করেছে তবে এখনো বেস্ট KNN ই দিয়েছে । সাপোর্ট ভেক্টর মেশিনটাও ট্রাই করে দেখি । কি বলেন? সাপোর্ট ভেক্টর মেশিন সম্পর্কে জানতে ক্লিক করুন <a href=\"https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/\"> এখানে </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83333333 0.79775281 0.83146067 0.82022472 0.84269663 0.82022472\n",
      " 0.84269663 0.85393258 0.84269663 0.86516854]\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector Machine score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "বেস্ট একুরেসি সাপোর্ট ভেক্টর মেশিনই দিলো !চলেন তাইলে সাপোর্ট ভেক্টর মেশিন অনুযায়ী প্রেডিকশন রেজাল্ট সাবমিট করে দেই !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = svm.SVC()\n",
    "clf.fit(train_data, target)\n",
    "test_data = test.drop(\"PassengerId\", axis=1).copy()\n",
    "prediction = clf.predict(test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "submission.to_csv('Pred5.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File Name: Pred5.csv\n",
    "\n",
    "দেখেন তো একুরেসি টা এরকম কিনা? <img src=\"pred5.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "79% অলমোস্ট! এখন পর্যন্ত বেস্ট । আমার যা নলেজ তাতে এই পর্যন্তই পারলাম আপাততো । ক্যাগলে গিয়ে ওখানে অনেক কার্নেল পাবেন । এরকম নোটবুকের মতোই । সেখান থেকে দেখে নিতে পারেন যারা বেস্ট প্রেডিকশন করেছে তারা কি এপ্রোচে এগিয়েছে! আমি শুধু নিজে যা জানি তার উপর একটা ধারনা দেয়ার ট্রাই করলাম । আশা করি সবাই অনেক হেল্প পেয়েছেন এখান থেকে । ধন্যবাদ এই পর্যন্ত আসার জন্য । যেকোন সাজেশন থাকলে আমাকে দিবেন । যাতে এই রিপোজিটরি আরো উন্নত করা যায় । সবাই যাতে এই রিপোজিটোরিকে একটা নোট হিসেবে ইউজ করে মেশিন লার্নিং এ এটাই আমার ইচ্ছা । আল্লাহ্‌ হাফেজ !"
   ]
  }
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