titanic_4th_try.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "অনেক তো ছোট ছোট ডুব দিলাম । চলেন একটা বড় ডুব দেই ! আমি নিজেও জানিনা এই বড় ডুবে আমার রেজাল্ট কতোটা ভালো আসবে । কিন্তু এটাই বেস্ট প্র্যাকটিস । এখানে আমরা ডেটা সাইন্সের অতি দরকারি ব্যাসিকগুলো নিয়ে কাজ করবো । \n",
    "ফিচার ইঞ্জিনিয়ারিং এবং ফিচার সিলেকশন খুবই দরকারি জিনিস ডেটা সাইন্সে । এর আগে আমরা ডিরেক্ট প্রেডিকশনে গিয়েছি । কিন্তু এবার আমরা ক্রস ভ্যালিডেশন করে তারপরে প্রেডিকশনে যাবো । একই সাথে আমরা ডিসিশন ট্রির সাথে সাথে র‍্যান্ডম ফরেস্ট মডেল ট্রেইন করে দেখবো কিরকম আসে রেজাল্ট । যাতে আমাদের হাতে বেস্ট মডেল সিলেক্ট করার অপশন থাকে । "
   ]
  },
  {
   "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = ['Age']\n",
    "categorical = ['Cabin','Embarked']\n",
    "for num in numerical:\n",
    "    df[num].fillna(df[num].median(),inplace=True)\n",
    "\n",
    "for cat in categorical:\n",
    "    df[cat].fillna(df[cat].value_counts().index[0],inplace=True)\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানেও যার যার কলামের মেডিয়ান (নিউমেরিক্যাল গুলোতে), এবং মোড(ক্যাটাগরিক্যাল গুলোতে) দিয়ে নাল ফিক্স করেছি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = ['Age','Fare']\n",
    "categorical = ['Cabin']\n",
    "for num in numerical:\n",
    "    test[num].fillna(test[num].median(),inplace=True)\n",
    "\n",
    "for cat in categorical:\n",
    "    test[cat].fillna(test[cat].value_counts().index[0],inplace=True)\n",
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": [
    "test.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "২ ডেটাসেটেরই ভাড়ার ফিল্ডে অনেক আউটলেয়ার!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 = (df\n",
    "        .groupby(['Sex', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আমাদের আগের ট্রাইগুলোতে আমরা কোন ফিচার ইঞ্জিনিয়ারিং করি নি । এখানে সেটা শুরু । আমরা যদি আমাদের ডেটাসেটের দিকে তাকাই তাহলে দেখবো যে আমাদের কিন্তু মহিলারা বেশি বেঁচে ছিলেন এবং যারা মা ছিলেন তারা মহিলাদের ভিতর বেশি বেঁচে ছিলেন । \n",
    "তাই আমরা এখানে প্যাসেঞ্জারদের নাম থেকে তাদের টাইটেল বের করে নেবো । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Major         2\n",
       "Col           2\n",
       "Don           1\n",
       "Capt          1\n",
       "Mme           1\n",
       "Ms            1\n",
       "Countess      1\n",
       "Jonkheer      1\n",
       "Lady          1\n",
       "Sir           1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "তো এখানে দেখতে পাচ্ছি আমাদের কোন ধরনের লোক কতজন ছিলেন জাহাজে । বিবাহিতা মহিলা দেখা যাচ্ছে 125 জন । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "plot = (df\n",
    "        .groupby(['Title', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "উপরের কথার প্রমাণ পেলেন কি? মহিলারা বেশি বেঁচেছেন । এবং বিবাহিতা মহিলারা বেশি বেঁচেছিলেন তাদের ভিতরে । কারণ বাচ্চাদের সাথে মায়েদের বাঁচানো জরুরি ছিলো ।"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Dr          1\n",
       "Dona        1\n",
       "Ms          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": "markdown",
   "metadata": {},
   "source": [
    "এবার আমরা তাদের টাইটেলের বিপরীতে নিউমেরিক্যাল ভ্যালু সেট করে দিলাম । যেহেতু আমরা মডেলে কোন প্রকার ক্যাটাগরিক্যাল ভ্যালু দিয়ে ট্রেইন করতে পারবো না । "
   ]
  },
  {
   "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>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>G6</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>G6</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>G6</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    G6        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250    G6        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500    G6        S      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plot = sns.countplot(df[\"Title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "কাউন্টপ্লট থেকেও কিন্তু এইটাই বোঝা যাচ্ছে যে প্যাসেঞ্জারদের ভিতরে পুরুষই ছিলেন বেশি । এর পরে ছিলেন অবিবাহিতা মহিলারা এবং পরবর্তীতে বিবাহিতা মহিলারা । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['Name']\n",
    "del test['Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "নাম থেকে টাইটেল আলাদা করা শেষ তো এবার আর নাম কলামের দরকার নাই । ২ ডেটাসেট থেকেই ডিলেট করে দিলাম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>G6</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>G6</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>G6</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    G6        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250    G6        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500    G6        S      0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": "markdown",
   "metadata": {},
   "source": [
    "এবার মহিলা এবং পুরুষদের ০ এবং ১ এর ভ্যালুতে কনভার্ট করে ফেললাম । এটা কিন্তু আগের বারের করা লেবেল এনকোডিংই । কিন্তু এখানে আমি নিজে বলে দিচ্ছি কে কোন নাম্বার পাবে আরকি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAELCAYAAAAybErdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAV40lEQVR4nO3cf0yV993/8dcFB7Gt9lbpOWKcIWnrwkK/lW5N7dnSQ103QBFnmesUN1pXO3UtbmzRWWAaTR3OsdC5jW7pV11q2m3MqDhCjy66kLVoqqTTuJLUOmCTusNBRT1Vfp7r/uO+77Oyj8BBvDhMn4+kqedzrnPO+yQn53nOdXEuy7ZtWwAAfExcrAcAAIw9xAEAYCAOAAADcQAAGIgDAMBAHAAABkfjcPjwYeXl5Wnu3Ll66aWXJEn19fXKzc1VZmamKioqIts2NjYqLy9PWVlZKikpUW9vr5OjAQAG4Vgc/vGPf2jDhg2qrKzU/v379d5776murk7FxcWqrKxUbW2tTp06pbq6OknSmjVrtH79eh04cEC2bauqqsqp0QAAQ3AsDn/84x81b948JScnKyEhQRUVFbrjjjuUkpKiGTNmyOVyKTc3V36/X62trers7FR6erokKS8vT36/36nRAABDcDl1xy0tLUpISNDKlSt17tw5Pf7445o5c6bcbndkG4/Ho0AgoLa2tn7rbrdbgUBgWI938eJHCof5sTcARCMuztLkyXcNeL1jcejr69Px48e1a9cu3XnnnVq1apXGjx8vy7Ii29i2LcuyFA6Hr7s+HIM9SQDA8DgWh3vuuUder1dTpkyRJH3hC1+Q3+9XfHx8ZJtgMCiPx6Pk5GQFg8HIent7uzwez7Ae7/z5EN8cACBKcXGWkpImDHy9Uw88Z84cvfXWW7p8+bL6+vr05z//WdnZ2WpqalJLS4v6+vpUU1Mjn8+n6dOnKzExUQ0NDZKk6upq+Xw+p0YDAAzBsW8Os2bN0vLly5Wfn6+enh597nOf05IlS3TvvfeqsLBQXV1dysjIUHZ2tiSpvLxcpaWlCoVCSktLU0FBgVOjAQCGYN0qp+xmtxIARC9mu5UAAP+5iAMAwEAcAAAGxw5I/6eZePd4jU9MiPUYGGM6u3p05XJnrMcARh1x+F/jExOUv/b1WI+BMeaNrUt1RcQBtx92KwEADMQBAGAgDgAAA3EAABiIAwDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGIgDAMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAGl5N3/vWvf10XLlyQy/U/D7Np0yb9/e9/1yuvvKLe3l49/fTTWrp0qSSpvr5eZWVl6urq0ty5c1VUVOTkaACAQTgWB9u21dzcrD/96U+ROAQCARUVFWnPnj0aN26cFi9erNmzZ+sTn/iEiouLtWvXLk2bNk0rVqxQXV2dMjIynBoPADAIx+Lwt7/9TZL0jW98Qx0dHXrqqad011136dFHH9WkSZMkSVlZWfL7/XrkkUeUkpKiGTNmSJJyc3Pl9/uJAwDEiGPHHC5fviyv16tf/OIX+vWvf63f/va3+vDDD+V2uyPbeDweBQIBtbW1XXcdABAbjn1zeOihh/TQQw9FLi9atEhlZWVatWpVZM22bVmWpXA4LMuyjPXhSEqaMPKhgetwuyfGegRg1DkWh+PHj6unp0der1fS/7zhT58+XcFgMLJNMBiUx+NRcnLyddeH4/z5kMJh+4bn5Q0AAwkGr8R6BOCmi4uzBv1Q7dhupStXrmjr1q3q6upSKBTS3r179eMf/1hHjhzRhQsXdO3aNR08eFA+n0+zZs1SU1OTWlpa1NfXp5qaGvl8PqdGAwAMwbFvDnPmzNGJEye0cOFChcNh5efn6zOf+YyKiopUUFCgnp4eLVq0SA8++KAkacuWLSosLFRXV5cyMjKUnZ3t1GgAgCFYtm3f+L6YMeRm7FbKX/v6TZwIt4I3ti5ltxJuSTHbrQQA+M9FHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAICBOAAADMQBAGAgDgAAA3EAABiIAwDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGIgDAMBAHAAABuIAADA4Hocf/ehHWrdunSSpsbFReXl5ysrKUklJiXp7eyVJH374oZYuXars7GytWrVKH330kdNjAQAG4Wgcjhw5or1790Yur1mzRuvXr9eBAwdk27aqqqokSRs3blR+fr78fr8eeOABVVZWOjkWAGAIjsWho6NDFRUVWrlypSSptbVVnZ2dSk9PlyTl5eXJ7/erp6dHx44dU1ZWVr91AEDsOBaH9evXq6ioSHfffbckqa2tTW63O3K92+1WIBDQxYsXNWHCBLlcrn7rAIDYcTlxp7///e81bdo0eb1e7dmzR5IUDodlWVZkG9u2ZVlW5P8f9++Xo5GUNGFkQwMDcLsnxnoEYNQ5Eofa2loFg0F96Utf0qVLl3T16lVZlqVgMBjZpr29XR6PR1OmTNGVK1fU19en+Ph4BYNBeTyeYT/m+fMhhcP2Dc/MGwAGEgxeifUIwE0XF2cN+qHakd1KO3fuVE1Njaqrq7V69Wp9/vOfV1lZmRITE9XQ0CBJqq6uls/nU0JCgh5++GHV1tZKkvbt2yefz+fEWACAKI3q7xzKy8tVVlam7OxsXb16VQUFBZKkDRs2qKqqSvPmzdPx48f1ne98ZzTHAgD8G8u27RvfFzOG3IzdSvlrX7+JE+FW8MbWpexWwi0pJruVAAD/2YgDAMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAICBOAAADMQBAGAgDgAAA3EAABiiikMgEDDWPvjgg5s+DABgbBg0Dh0dHero6NBzzz2nS5cuRS63t7frhRdeGK0ZAQCjzDXYld/73vf09ttvS5Jmz579rxu5XMrKynJ2MgBAzAwah+3bt0uSXnzxRZWVlY3KQACA2Bs0Dv+nrKxMra2tunTpkmzbjqynpaU5NhgAIHaiisO2bdu0fft2JSUlRdYsy9KhQ4ccGwwAEDtRxWHfvn06ePCgpk6d6vQ8AIAxIKo/ZZ02bRphAIDbSFTfHLxer7Zu3aonnnhC48ePj6xzzAEAbk1RxWHPnj2SJL/fH1njmAMwOib/1zi5xiXGegyMMb3dXbp4qdux+48qDocPH76hO//pT3+qAwcOyLIsLVq0SMuWLVN9fb3KysrU1dWluXPnqqioSJLU2NiokpISffTRR3r44Ye1ceNGuVxRjQfc0lzjEtWwdXmsx8AY85m1/19SjOOwc+fO664vW7ZswNu88847Onr0qPbv36/e3l7NmzdPXq9XxcXF2rVrl6ZNm6YVK1aorq5OGRkZWrNmjV566SWlp6eruLhYVVVVys/Pv7FnBQAYkaji8P7770f+3d3drWPHjsnr9Q56m0ceeUSvvfaaXC6XAoGA+vr6dPnyZaWkpGjGjBmSpNzcXPn9ft1///3q7OxUenq6JCkvL0/btm0jDgAQI1H/CO7jAoGASkpKhrxdQkKCtm3bph07dig7O1ttbW1yu92R6z0ejwKBgLHudruve7I/AMDouKGd+lOnTlVra2tU265evVrPPfecVq5cqebmZlmWFbnOtm1ZlqVwOHzd9eFISpowrO2BaLndE2M9AnBdTr42h33MwbZtnTp1qt+vpa/nzJkz6u7u1qc+9SndcccdyszMlN/vV3x8fGSbYDAoj8ej5ORkBYPByHp7e7s8Hs+wnsj58yGFw/bQGw6ANwAMJBi8EtPH57WJgYzktRkXZw36oTqqH8G9//77kf9Onz6tadOmqby8fNDbnD17VqWlperu7lZ3d7cOHTqkxYsXq6mpSS0tLerr61NNTY18Pp+mT5+uxMRENTQ0SJKqq6vl8/mG8TQBADfTsI45tLa2qre3VykpKUPeJiMjQydPntTChQsVHx+vzMxM5eTkaMqUKSosLFRXV5cyMjKUnZ0tSSovL1dpaalCoZDS0tJUUFAwgqcFABiJqOLQ0tKib33rW2pra1M4HNbkyZP1q1/9Svfdd9+gtyssLFRhYWG/Na/Xq/379xvbpqamavfu3cMYHQDglKh2K23atEnLly/XsWPH1NDQoFWrVmnjxo1OzwYAiJGo4nD+/Hk9+eSTkctf/vKXdfHiRceGAgDEVlRx6OvrU0dHR+TyhQsXHBsIABB7UR1z+NrXvqavfvWrmjt3rizLUm1trZ5++mmnZwMAxEhU3xwyMjIkST09PTpz5owCgYC++MUvOjoYACB2ovrmsG7dOi1dulQFBQXq6urSb37zGxUXF+vVV191ej4AQAxE9c3h4sWLkd8dJCYm6plnnun3i2YAwK0l6gPSHz8RXnt7u2z7xk9VAQAY26LarfTMM89o4cKFeuyxx2RZlurr67V27VqnZwMAxEhUcVi0aJEeeOABHT16VPHx8Xr22Wf1yU9+0unZAAAxEvUpu1NTU5WamurkLACAMSKqYw4AgNsLcQAAGIgDAMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAICBOAAADMQBAGBwNA4///nPlZOTo5ycHG3dulWSVF9fr9zcXGVmZqqioiKybWNjo/Ly8pSVlaWSkhL19vY6ORoAYBCOxaG+vl5vvfWW9u7dq3379umvf/2rampqVFxcrMrKStXW1urUqVOqq6uTJK1Zs0br16/XgQMHZNu2qqqqnBoNADAEx+Lgdru1bt06jRs3TgkJCbrvvvvU3NyslJQUzZgxQy6XS7m5ufL7/WptbVVnZ6fS09MlSXl5efL7/U6NBgAYgmNxmDlzZuTNvrm5WW+++aYsy5Lb7Y5s4/F4FAgE1NbW1m/d7XYrEAg4NRoAYAgupx/g9OnTWrFihdauXav4+Hg1NzdHrrNtW5ZlKRwOy7IsY304kpIm3KyRgX7c7omxHgG4Lidfm47GoaGhQatXr1ZxcbFycnL0zjvvKBgMRq4PBoPyeDxKTk7ut97e3i6PxzOsxzp/PqRw2L7hWXkDwECCwSsxfXxemxjISF6bcXHWoB+qHdutdO7cOT3//PMqLy9XTk6OJGnWrFlqampSS0uL+vr6VFNTI5/Pp+nTpysxMVENDQ2SpOrqavl8PqdGAwAMwbFvDtu3b1dXV5e2bNkSWVu8eLG2bNmiwsJCdXV1KSMjQ9nZ2ZKk8vJylZaWKhQKKS0tTQUFBU6NBgAYgmNxKC0tVWlp6XWv279/v7GWmpqq3bt3OzUOAGAY+IU0AMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAICBOAAADMQBAGAgDgAAA3EAABiIAwDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGIgDAMBAHAAABuIAADA4HodQKKT58+fr7NmzkqT6+nrl5uYqMzNTFRUVke0aGxuVl5enrKwslZSUqLe31+nRAAADcDQOJ06c0JIlS9Tc3CxJ6uzsVHFxsSorK1VbW6tTp06prq5OkrRmzRqtX79eBw4ckG3bqqqqcnI0AMAgHI1DVVWVNmzYII/HI0k6efKkUlJSNGPGDLlcLuXm5srv96u1tVWdnZ1KT0+XJOXl5cnv9zs5GgBgEC4n73zz5s39Lre1tcntdkcuezweBQIBY93tdisQCAzrsZKSJoxsWGAAbvfEWI8AXJeTr01H4/DvwuGwLMuKXLZtW5ZlDbg+HOfPhxQO2zc8G28AGEgweCWmj89rEwMZyWszLs4a9EP1qP61UnJysoLBYORyMBiUx+Mx1tvb2yO7ogAAo29U4zBr1iw1NTWppaVFfX19qqmpkc/n0/Tp05WYmKiGhgZJUnV1tXw+32iOBgD4mFHdrZSYmKgtW7aosLBQXV1dysjIUHZ2tiSpvLxcpaWlCoVCSktLU0FBwWiOBgD4mFGJw+HDhyP/9nq92r9/v7FNamqqdu/ePRrjAACGwC+kAQAG4gAAMBAHAICBOAAADMQBAGAgDgAAA3EAABiIAwDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGIgDAMBAHAAABuIAADAQBwCAgTgAAAzEAQBgIA4AAANxAAAYiAMAwEAcAAAG4gAAMBAHAIBhTMXhD3/4g+bNm6fMzEy9/vrrsR4HAG5brlgP8H8CgYAqKiq0Z88ejRs3TosXL9bs2bN1//33x3o0ALjtjJk41NfX69FHH9WkSZMkSVlZWfL7/XrhhReiun1cnDXiGe6ZfNeI7wO3npvx2hqpcXcnxXoEjEEjeW0OddsxE4e2tja53e7IZY/Ho5MnT0Z9+8k34Y1924sLR3wfuPUkJU2I9Qj6fyt/FOsRMAY5+docM8ccwuGwLOtfJbNtu99lAMDoGTNxSE5OVjAYjFwOBoPyeDwxnAgAbl9jJg6f/exndeTIEV24cEHXrl3TwYMH5fP5Yj0WANyWxswxh6lTp6qoqEgFBQXq6enRokWL9OCDD8Z6LAC4LVm2bduxHgIAMLaMmd1KAICxgzgAAAzEAQBgIA4AAANxQAQnPsRYFgqFNH/+fJ09ezbWo9wWiAMk/evEh2+88Yb27dun3/3ud/rggw9iPRYgSTpx4oSWLFmi5ubmWI9y2yAOkNT/xId33nln5MSHwFhQVVWlDRs2cNaEUTRmfgSH2BrpiQ8BJ23evDnWI9x2+OYASZz4EEB/xAGSOPEhgP6IAyRx4kMA/XHMAZI48SGA/jjxHgDAwG4lAICBOAAADMQBAGAgDgAAA3EAABj4U1ZgBP7yl7/oJz/5iTo6OmTbtpKTk/X9739fM2fOjPVowIjwp6zADeru7tZjjz2mHTt2KC0tTZJUXV2tiooKHTp0SPHx8TGeELhx7FYCbtC1a9d05coVXb16NbK2YMEC/eAHP1BfX58OHz6sr3zlK1q4cKEWL16sd999V5L04osv6tvf/rYk6fTp0/J6vTpz5kxMngMwEL45ACOwc+dOvfzyy7rnnnv06U9/WrNnz1ZOTo4CgYAKCwv12muvafLkyTp9+rSWLVumgwcPSpKefPJJrVy5Utu3b9c3v/lNLViwIMbPBOiPOAAjFAqFdOzYMR07dkyHDh2SJOXn56uyslLJycmR7S5cuKBXX31Vqampeu+99/TUU09pwYIF+uEPfxir0YEBcUAauEENDQ169913tXz5cs2ZM0dz5szRd7/7Xc2fP1+hUEher1cvv/xyZPtz585FznTb1NSkSZMmqbGxUd3d3Ro3blysngZwXRxzAG7QlClT9Morr+j48eORtWAwqFAopCeeeEJvv/125FhCXV2dFixYoM7OTp09e1abN2/Wjh07dO+996q8vDxWTwEYELuVgBE4evSofvazn+mf//ynEhMTNXHiRD3//PPy+Xx688039ctf/lK2bcvlcqm4uFjp6elaunSpMjMz9eyzz+rSpUvKzc3Vpk2b9Pjjj8f66QARxAEAYGC3EgDAQBwAAAbiAAAwEAcAgIE4AAAMxAEAYCAOAAADcQAAGP4b5S61YJ6WUhkAAAAASUVORK5CYII=\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": 22,
   "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": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (df\n",
    "        .groupby(['Age', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার কিন্তু আমরা বয়সের আউটলেয়ার বাদ দেই নি । করেছি কি আসলে? এখানে বয়সকে ৫ ক্যাটাগরিতে ভাগ করেছি । যাতে আমাদের মডেলের বুঝতে সুবিধা হয় কোন বয়সের লোকগুলোকে বেশি গুরুত্ব দেয়া উচিৎ । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>G6</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>G6</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>G6</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    G6        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250    G6        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500    G6        S      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VVRXJ0gAAXYjomcOrr77aafvUqVOaMmWKli5dqpEjRyovL0/l5eUaPny4bDZbaD+73d5pLScAQN+KaDj8r6SkJK1evTq0PXv2bG3evFlpaWmyWCyhdsMwOm2HIz5+RK/ViRuLzTYy2iX0G4zFZYzF9enTcPj0009VX1+vtLQ0SZdCwGq1KiEhodMS4F6vt9M1iXA0N7cqGGSl2K4M1P9ZvN6WHh/DWFzGWAxOQ4ZYuvxS3ad/ymoYhpYtW6Zz586pvb1db7/9tqZNm6aJEyfq6NGjamhoUEdHhyorK5WamtqXpQEAvqZPzxzGjx+vp59+WjNnzlQgEJDT6VRmZqYkqbCwUPPnz5fP55PD4VB6enpflgYA+Jo+CYddu3aFXs+aNUuzZs0y7ZOSkqItW7b0RTkAgG5whzQAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAk4iGQ2trqzIzM3Xs2DFJUnV1tdxut5xOp4qLi0P7HTp0SDk5OUpLS9OSJUsUCAQiWRYAoBsRC4eDBw9q5syZqq+vlyS1tbXJ4/Hotdde09atW1VXV6fdu3dLkl588UW99NJLev/992UYhsrKyiJVFgAgDBELh7KyMi1dulR2u12SVFtbq+TkZCUlJclqtcrtdquqqkrHjx9XW1ubJk2aJEnKyclRVVVVpMoCAITBGqkf/Oqrr3baPnnypGw2W2jbbrerqanJ1G6z2dTU1NTj3xcfP+KK7f72DsUOjenxz+vPBuJ7uh4228hol9BvMBaXMRbXJ2Lh8L+CwaAsFkto2zAMWSyWq7b3VHNzq4JBw9Rus43Uows3XFvR/dT/WzFLXm9Lj48bqP+zMBaXMRaXXctYDCZDhliu+qVa6sO/VkpISJDX6w1te71e2e12U/upU6dCU1EAgOjos3CYOHGijh49qoaGBnV0dKiyslKpqakaO3as4uLiVFNTI0mqqKhQampqX5UFALiCPptWiouLU2FhoebPny+fzyeHw6H09HRJUlFRkQoKCtTa2qoJEyYoNze3r8oCAFxBxMNh165dodcpKSnasmWLaZ/x48ervLw80qUAAMLEHdIAABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IBAGDSZ8+Q/rrZs2fr9OnTslov/fqXX35Z//73v/X73/9egUBAjz/+uGbNmhWN0gAAikI4GIah+vp6/eUvfwmFQ1NTkxYsWKBNmzYpNjZWM2bM0OTJk3Xbbbf1dXkAAEUhHD7//HNJ0lNPPaWzZ8/q4Ycf1s0336wpU6Zo1KhRkqS0tDRVVVVp3rx5fV0eAEBRuObw5ZdfKiUlRatXr9Zbb72lP/7xj2psbJTNZgvtY7fb1dTU1NelAQD+q8/PHO666y7dddddoe3p06dr+fLlevbZZ0NthmHIYrH06OfGx4/otRpvBDbbyGiX0G8wFpcxFpcxFtenz8PhwIEDam9vV0pKiqRLQTB27Fh5vd7QPl6vV3a7vUc/t7m5VcGgYWofqP9AvN6WHh/DWFzGWFzGWAxOQ4ZYuvxS3efTSi0tLVqxYoV8Pp9aW1v17rvv6je/+Y327Nmj06dP6+LFi9q+fbtSU1P7ujQAwH/1+ZnD1KlTdfDgQWVnZysYDOrRRx/V3XffrQULFig3N1ft7e2aPn267rzzzr4uDQDwX1G5z+GFF17QCy+80KnN7XbL7XZHoxwAwP/gDmkAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AAJOoLNkNAP3RN24Zpti4gfWx6PcFdO7Liz0+bmCNAgBch9g4q5YtKY92Gb3K8+r0azqOaSUAgAnhAAAwIRwAACb9Khzee+89ZWRkyOl0asOGDdEuBwAGrX5zQbqpqUnFxcXatGmTYmNjNWPGDE2ePFm33XZbtEsDgEGn34RDdXW1pkyZolGjRkmS0tLSVFVVpXnz5oV1/JAhlqv2fXP0zb1SY3/S1fvtSuwt8b1cSfRd61h8c8StvVxJ9F3rWAz7Jv8uvvKNUcN7uZLou9JYdDc+FsMwjEgV1BOvv/66Lly4oAULFkiS3nnnHdXW1uqVV16JcmUAMPj0m2sOwWBQFsvlJDMMo9M2AKDv9JtwSEhIkNfrDW17vV7Z7fYoVgQAg1e/CYcf/vCH2rNnj06fPq2LFy9q+/btSk1NjXZZADAo9ZsL0mPGjNGCBQuUm5ur9vZ2TZ8+XXfeeWe0ywKAQanfXJAGAPQf/WZaCQDQfxAOAAATwgEAYEI4AABMCIce6m5xwEOHDiknJ0dpaWlasmSJAoFAFKrsO62trcrMzNSxY8dMfYNpLFatWiWXyyWXy6UVK1aY+gfTWKxcuVIZGRlyuVwqLS019Q+msfjKr3/9ay1atMjU3tjYqFmzZik9PV3PPvuszp8/H4XqrsJA2P7zn/8YU6dONc6cOWOcP3/ecLvdxuHDhzvt43K5jI8//tgwDMNYvHixsWHDhmiU2if+/ve/G5mZmcaECROML774wtQ/WMbio48+Mh555BHD5/MZfr/fyM3NNbZv395pn8EyFn/729+MGTNmGO3t7cbFixeNqVOnGkeOHOm0z2AZi69UV1cbkydPNn7+85+b+p5++mmjsrLSMAzDWLVqlbFixYq+Lu+qOHPoga8vDjh8+PDQ4oBfOX78uNra2jRp0iRJUk5OTqf+gaasrExLly694p3sg2ksbDabFi1apNjYWA0dOlTjxo1TY2NjqH8wjcW9996rtWvXymq1qrm5WR0dHRo+/PJCdoNpLCTp7NmzKi4u1jPPPGPqa29v1/79+5WWliap/41Fv7kJ7kZw8uRJ2Wy20Lbdbldtbe1V+202m5qamvq0xr706quvXrVvMI3F7bffHnpdX1+vbdu2aePGjaG2wTQWkjR06FCVlJTozTffVHp6usaMGRPqG2xj8dJLL2nBggU6ceKEqe/MmTMaMWKErNZLH8P9bSw4c+iB7hYHZPHAywbjWBw+fFhPPfWUFi5cqG9/+9uh9sE4Fvn5+dqzZ49OnDihsrKyUPtgGot33nlHiYmJSklJuWL/ld57fxoLzhx6ICEhQQcOHAht/+/igP+7eOCpU6cG7eKBg20sampqlJ+fL4/HI5fL1alvMI3FkSNH5Pf7dccdd2jYsGFyOp369NNPQ/2DaSy2bt0qr9errKwsnTt3ThcuXNCyZcvk8XgkSbfeeqtaWlrU0dGhmJiYfrfYKGcOPdDd4oBjx45VXFycampqJEkVFRWDdvHAwTQWJ06c0PPPP6+ioiJTMEiDayyOHTumgoIC+f1++f1+7dy5U3fffXeofzCNRWlpqSorK1VRUaH8/Hzdf//9oWCQLk2/3XPPPdq6daskafPmzf1qLAiHHvj64oDZ2dnKzMzUnXfeqblz5+qTTz6RJBUVFWn58uVKT0/XhQsXlJubG+Wq+9ZgHIs1a9bI5/OpsLBQWVlZysrK0saNGwflWDgcDv34xz9Wdna2HnroId11111yuVyDciyuZsmSJdq5c6ckaenSpSorK1NGRoYOHDigF154IcrVXcbCewAAE84cAAAmhAMAwIRwAACYEA4AABPCAQBgQjgA16m9vV333Xef5syZE+1SgF5DOADXaceOHRo/frzq6up05MiRaJcD9ArucwCu0+zZs5WRkaHDhw8rEAjo5ZdfliS98cYbKi8v180336x77rlHO3fu1K5du+T3+1VUVKT9+/ero6ND3/ve91RQUKARI0ZE+Z0Al3HmAFyHzz77TB9//LHS09OVnZ2tiooKnTlzRh988IE2bdqk8vJybdq0qdNDXN544w3FxMRo06ZN2rJli+x2u4qKiqL4LgAzFt4DrsPGjRs1depUjR49WqNHj9a3vvUtlZWVyev1Kj09XbfccoskadasWdq7d68k6a9//ataWlpUXV0t6dI1i/j4+Ki9B+BKCAfgGl24cEEVFRWKjY3V/fffL+nSY1PXr18vl8ulr8/YxsTEhF4Hg0F5PB45HA5J0vnz5+Xz+fq2eKAbTCsB1+i9997TqFGj9MEHH2jXrl3atWuX/vznP+vChQuaMGGCtm/frpaWFklSeXl56Lj77rtPGzZskN/vVzAY1C9+8Qv99re/jdbbAK6IcACu0caNG/Xkk092Oiu45ZZbNHv2bL311lt6+OGH9cgjjygnJ0ctLS0aNmyYJOm5557T2LFj9eCDDyojI0OGYVzx4fNANPHXSkAEfPLJJ/r4449Dy1GXlpbq4MGD+t3vfhflyoDwEA5ABLS2tsrj8ejzzz+XxWJRYmKiXnnllU7PUwb6M8IBAGDCNQcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAk/8PDpHVTpzUDyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.countplot(df[\"Age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "বয়সের ক্যাটাগরিতে দেখা যাচ্ছে ২৬-৩৬ বয়সি মানুষ বেশি ছিলো শিপে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (df\n",
    "        .groupby(['Embarked', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এবার পোর্ট গুলিকে নাম্বারে ম্যাপিং করে দিলাম একই ভাবে । এখান থেকে কি বোঝা যায়? S বা সাউথাম্পটন পোর্ট থেকে যারা উঠেছিলেন তারা একটু বিত্তবান ছিলেন । তাই তাদের সারভাইভাল রেটটা বাকিদের চেয়ে বেশি । "
   ]
  },
  {
   "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",
       "    }\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>G6</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>G6</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>G6</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    G6         0      0  \n",
       "1  71.2833   C85         1      2  \n",
       "2   7.9250    G6         0      1  \n",
       "3  53.1000  C123         0      2  \n",
       "4   8.0500    G6         0      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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 = (df\n",
    "        .groupby(['Fare', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে ভাড়ার ফিল্ডে কিন্তু প্রচুর আউটলেয়ার ছিলো । কিন্তু এতো আউটলেয়ার যে আপনার উচিৎ হবে না এই আউটলেয়ার বাদ দেয়া । বরঞ্চ আপনি যদি ভাড়াকে ৪ ভাগে ভাগ করে ফেলেন তাহলে মডেল আর বায়াসড হবে না । সে ভাড়ার ফিল্ড থেকে ইজিলি লার্ন করেত পারবে যেহেতু মাত্র ৪ ধরনের ভাড়া হতে পারে । এখানেও দেখা যাচ্ছে নিম্নবিত্তরা সংখ্যায় অনেক থাকলেও বেচেছে অনুপাতে বাকিদের চেয়ে কম । যারা বেঁচেছে তাদের প্রায় সবই বাচ্চা আর মা হবে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>0.0</td>\n",
       "      <td>G6</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>G6</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>G6</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    G6         0      0  \n",
       "1   2.0   C85         1      2  \n",
       "2   0.0    G6         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0    G6         0      0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G6             691\n",
       "C23 C25 C27      4\n",
       "B96 B98          4\n",
       "F2               3\n",
       "F33              3\n",
       "              ... \n",
       "C118             1\n",
       "C82              1\n",
       "D47              1\n",
       "C106             1\n",
       "E10              1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (df\n",
    "        .groupby(['Cabin', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "কেবিনের হিসাবে আসি এবার । কেবিনের কিন্তু আমাদের ফুল ডেটা লাগবে না । শুধু লাগবে কোন ক্যারেক্টার দিয়ে শুরু । কেন লাগবে ?\n",
    "আসলে কেবিনের এই শুরুর ক্যারেক্টার দিয়েই বুঝতে পারবেন যে কেবিনটি কত তলায় ছিলো । নিচের তালার লোকেরা বেশি মারা গেছে যেহেতু নিচ থেকে ডুবতে শুরু করেছে শিপটি । তো বুঝাই যাচ্ছে হিসেব ঠিক আছে । উপরের তালার দিকের লোকজনই বেশি বেচেছে । \n",
    "বুঝলেন এবার ফিচার ইঞ্জিনিয়ারিং কাকে বলে? আমরা যে এখানে বুঝে নিলাম নিচের তালার দিক লোক বেশি মরেছে এই নলেজটাকে বলে ডোমেইন নলেজ । ফিচার ইঞ্জিনিয়ারিংএ এরকম ডোমেইন নলেজ অনেক দরকার । নিচের ছবিটা দেখেন । বুঝতে পারবেন কেন এরকম হিসেব করেছি । আর হিসেবটা মিললো কিনা । \n",
    "<img src=\"cabins.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": "markdown",
   "metadata": {},
   "source": [
    "যথারীতি আমরা ম্যাপিং করলাম কেবিন নাম্বারকে । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"FamilySize\"] = df[\"SibSp\"] + df[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (df\n",
    "        .groupby(['FamilySize', 'Survived'])\n",
    "        .size()\n",
    "        .unstack()\n",
    "        .plot\n",
    "        .bar(stacked=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "এখানে কি করেছি? এখানে দেখেছি ফ্যামিলি মেম্বারের হিসেবটা । মনে আছে আমরা দেখেছিলাম যার সাথে বাবা-মা আছে বা ভাই বোন বেশি তার বাঁচার চান্স বেশি ছিলো । এখানেও দেখেন যাদের ফ্যামিলি মেম্বার বেশি তাদের কিন্তু বাকিদের তুলনায় বেঁচে থাকার চান্সটা অনেক বেশি ছিলো । তবে আবার দেখা যাচ্ছে ৫ বা তার অধিকের ক্ষেত্রে হিসেবটা উলটো । তো এখানে আমরা ২ টা ফিচারকে একটা ফিচারে কনভার্ট করে ফেলেছি । আপনাদের কাছে প্রশ্ন শেষে ১ কেন যোগ করলাম? এটা আমি বলবো না । আপনারা বের করেন । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": "markdown",
   "metadata": {},
   "source": [
    "ফ্যামিলিকে ম্যাপিং করে নিলাম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": "markdown",
   "metadata": {},
   "source": [
    "টিকেট, ভাইবোন, বাবা-মা এই তিনটা ফিল্ড থেকে যা যা নেয়ার নিয়ে নিয়েছি । এবার এদের ফেলে দেই । কারণ এদের আর কাজ নাই । এখানে কলাম ড্রপ করার আরেকটা নিয়ম জেনে নিলেন কিন্তু । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 40,
     "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": "markdown",
   "metadata": {},
   "source": [
    "এবার আমরা ক্রস ভ্যালিডেশনের জন্য এখানে কি করেছি ? ট্রেইন ডেটাসেট থেকে Survived বাদে বাকি ডেটাগুলো নিয়েছি train_data তে । আর target এ নিয়েছি শুধু Survived কলামটি । এবার এই খানে ট্রেইনডেটা দিয়ে মডেলকে ট্রেইন করবো এবং এইটা দিয়েই ক্রস ভ্যালিডেশন করবো । টেস্ট ডেটাসেটে হাতই দেবো না এখন ।  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>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>2.4</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>2.4</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>2.4</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    2.4         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    2.4         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    2.4         0      0         0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": "markdown",
   "metadata": {},
   "source": [
    "এখানে এবার ডিসিশন ট্রি এর সাথে র‍্যান্ডম ফরেস্ট ও ইম্পোর্ট করে নিলাম । To know more about <a href=\"https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_random_forest.htm\"> Random Forest </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KFold কি? KFold এর কাজ হচ্ছে আমাদের এইযে ট্রেইন্ডেটা আছে তাকে সে K সংখ্যক বার ভাগ করে করে টেস্ট করবে । একটা এক্সাম্পল দেই । \n",
    "ধরেন আমার ডেটা আছে, \n",
    "<p>১,২,৩,৪,৫,৬,৭,৮,৯,১০\n",
    "<p>এবার আমাদের K এর ভ্যালু ধরেন ৫ ।\n",
    "<p>তো ১ম বার সে করলো কি মডেল ট্রেইনের জন্য রাখলো,\n",
    "<p>৩,৪,৫,৬,৭,৮,৯,১০\n",
    "<p>আর টেস্ট করার জন্য রাখলো ১,২ কে ।\n",
    "<p>এবার ২য় বারে সে কি করবে?\n",
    "<p>১,২,৫,৬,৭,৮,৯,১০ কে রাখবে ট্রেইনের জন্য আর \n",
    "<p>৩,৪ কে রাখবে টেস্টের জন্য । \n",
    "<p>এভাবে সে প্রতিবার আলাদা আলাদা টেস্ট সেট আর ট্রেইনসেট দিয়ে ভ্যালিডেশন করবে । \n",
    "<p>এটাই হলো ক্রস ভ্যালিডেশন । অর্থাৎ এক ডেটাসেট দিয়েই নানান ভাবে মডেলকে লার্ন করানো । \n",
    "<p>cross_val_score এর কাজ হচ্ছে এই ক্রস ভ্যালিডেশনগুলোর একুরেসি চেক করা । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.78888889 0.83146067 0.7752809  0.79775281 0.86516854 0.78651685\n",
      " 0.84269663 0.82022472 0.74157303 0.78651685]\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\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": "markdown",
   "metadata": {},
   "source": [
    "তো এখানে আমরা দেখতে পাচ্ছি একেকবার একেক রকম একুরেসি দিচ্ছে সে । গড় একুরেসি কিরকম হয় দেখে নেই । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decision tree Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.80898876 0.80898876 0.79775281 0.85393258 0.80898876\n",
      " 0.80898876 0.80898876 0.74157303 0.82022472]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13)\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": "markdown",
   "metadata": {},
   "source": [
    "র‍্যান্ডম ফরেস্টের ক্ষেত্রেও একই ভাবে একুরেসি দেখি । এবং দেখি যে এই দুই মডেলে কে বেটার করলো । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.25"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "র‍্যান্ডম ফরেস্ট বেশি ভোট পেয়ে জয়যুক্ত হয়ছে । তাই আমরা সাবমিশনের জন্য তাকেই চুজ করলাম । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13)\n",
    "clf.fit(train_data, target)\n",
    "test_data = test.drop(\"PassengerId\", axis=1).copy()\n",
    "prediction = clf.predict(test_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File Name: Pred4.csv\n",
    "\n",
    "সাবমিশন ফাইল রেডি করলাম । এবার ক্রিয়েট করে সাবমিট করি । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "submission.to_csv('Pred4.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "কি আসলো রেজাল্ট? একটু কি উন্নতি হয়েছে? \n",
    "<img src=\"pred4.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "75% accuracy\n",
    "\n",
    "আমাদের নেক্সট কি কি মাথায় রাখা যেতে পারে?\n",
    "<p>১। মিসিং ভ্যালু কলামের মেডিয়ান বা মোড দিয়ে ফিক্স না করে তার সাথে এটাও দেখা যেতে পারে অন্য কলামের \n",
    "সম্পর্ক কেমন । তার উপর ভিত্তি করে ফিক্স করা যেতে পারে । যেমন ধরেন, \n",
    "কেবিনের যেসব মিসিং ভ্যালু আছে তা হিসেব করতে পারি ঐ কেবিন কোন ক্লাসে আছে তার উপর বেস করে । কিংবা ভাড়ার সাথে বেস করে । \n",
    "\n",
    "২। আউটলেয়ার ফিক্স করা যেতে পারে । তবে ভাড়ার আউটলেয়ার ফিক্স করা উচিৎ হবে না । কেননা ওখানে প্রচুর আউটলেয়ার । বরঞ্চ আমরা যেভাবে ম্যাপিং করেছি সেটাই বেস্ট হবে । \n",
    "\n",
    "৩।  অন্য কোন মডেল চুজ করা । লজিস্টিক রিগ্রেসন, কে নিয়ারেস্ট নেইবর, কিংবা নিউরাল নেটওয়ার্ক । \n",
    "\n",
    "ট্রাই করে দেখবেন নাকি? আমি কি করেছি দেখতে চাইলে titanic_5th_try.ipynb ফাইলে চলে যান । "
   ]
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