titanic_3rd_try.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আগেরটার সাথে এখানে চেঞ্জ হিসেবে থাকবে শুধু নাল ভ্যালু এবং আউটলেয়ার ফিক্স করবো যার যার কলামের মেডিয়ান এবং মোড দিয়ে । "
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "এখানে কিন্তু যার যার কলামের মেডিয়ান আর মোড দিয়ে ফিক্স করেছি ভ্যালু । আগেরটার মতো ফুল ডেটাসেটের মেডিয়ান দিয়ে নয় । \n",
    "মনে রাখবেন ক্যাটাগরিক্যাল ভ্যালু ফিক্সের ক্ষেত্রে মেডিয়ান ইউজ করতে পারবেন না । কারণ মেডিয়ান হয় শুধু নিউমেরিক্যাল ভ্যালুর । "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.head()"
   ]
  },
  {
   "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             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "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": "markdown",
   "metadata": {},
   "source": [
    "আউটলেয়ার যার যার কলামের মেডিয়ান দিয়ে ফিক্স করা হলো । "
   ]
  },
  {
   "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": [
    "df.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আউটলেয়ার ফিক্সড!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    </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>B96 B98</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>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>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>B96 B98</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>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>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>B96 B98</td>\n",
       "      <td>S</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  \n",
       "0         A/5 21171   7.2500  B96 B98        S  \n",
       "1          PC 17599  71.2833      C85        C  \n",
       "2  STON/O2. 3101282   7.9250  B96 B98        S  \n",
       "3            113803  53.1000     C123        S  \n",
       "4            373450   8.0500  B96 B98        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['Name']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_outlier(feature):\n",
    "    Q1 = feature.quantile(0.25)\n",
    "    Q3 = feature.quantile(0.75)\n",
    "    \n",
    "    IQR = Q3-Q1\n",
    "    \n",
    "    lower_bound = Q1-(1.5*IQR)\n",
    "    upper_bound = Q3+(1.5*IQR)\n",
    "    \n",
    "    return feature.index[(feature<lower_bound)|(feature>upper_bound)].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['Fare','Age']\n",
    "\n",
    "for col in num_cols:\n",
    "    indx = detect_outlier(df[col])\n",
    "    df[col].loc[indx] = df[col].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আউটলেয়ার যার যার কলামের মেডিয়ান দিয়ে ফিক্স করা হলো । "
   ]
  },
  {
   "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": [
    "df.plot(kind='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "আউটলেয়ার ফিক্সড!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "    </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>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>55</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>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  \\\n",
       "0            1         0       3    1  22.0      1      0     523   7.2500   \n",
       "1            2         1       1    0  38.0      1      0     596  14.4542   \n",
       "2            3         1       3    0  26.0      0      0     669   7.9250   \n",
       "3            4         1       1    0  35.0      1      0      49  53.1000   \n",
       "4            5         0       3    1  35.0      0      0     472   8.0500   \n",
       "\n",
       "   Cabin  Embarked  \n",
       "0     47         2  \n",
       "1     81         0  \n",
       "2     47         2  \n",
       "3     55         2  \n",
       "4     47         2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = ['Sex','SibSp','Parch','Ticket','Cabin','Embarked']\n",
    "\n",
    "for col in cat_cols:\n",
    "    df[col] = df[col].astype('category')\n",
    "    #print(col,'---->', dict(enumerate(df[col].cat.categories)))\n",
    "    df[col] = df[col].cat.codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>28.476992</td>\n",
       "      <td>0.507295</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>338.528620</td>\n",
       "      <td>17.383622</td>\n",
       "      <td>53.639731</td>\n",
       "      <td>1.536476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>9.793559</td>\n",
       "      <td>1.005995</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>200.850657</td>\n",
       "      <td>12.713016</td>\n",
       "      <td>23.568293</td>\n",
       "      <td>0.791503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158.500000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>519.500000</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Sex         Age  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642    0.647587   28.476992   \n",
       "std     257.353842    0.486592    0.836071    0.477990    9.793559   \n",
       "min       1.000000    0.000000    1.000000    0.000000    3.000000   \n",
       "25%     223.500000    0.000000    2.000000    0.000000   23.750000   \n",
       "50%     446.000000    0.000000    3.000000    1.000000   28.000000   \n",
       "75%     668.500000    1.000000    3.000000    1.000000   33.000000   \n",
       "max     891.000000    1.000000    3.000000    1.000000   54.000000   \n",
       "\n",
       "            SibSp       Parch      Ticket        Fare       Cabin    Embarked  \n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000  \n",
       "mean     0.507295    0.381594  338.528620   17.383622   53.639731    1.536476  \n",
       "std      1.005995    0.806057  200.850657   12.713016   23.568293    0.791503  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000  158.500000    7.910400   47.000000    1.000000  \n",
       "50%      0.000000    0.000000  337.000000   14.454200   47.000000    2.000000  \n",
       "75%      1.000000    0.000000  519.500000   24.150000   47.000000    2.000000  \n",
       "max      6.000000    6.000000  680.000000   65.000000  146.000000    2.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.042939</td>\n",
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       "      <td>-0.065229</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.056554</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>-0.018086</td>\n",
       "      <td>0.013128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>-0.020426</td>\n",
       "      <td>-0.026385</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>-0.164549</td>\n",
       "      <td>0.190926</td>\n",
       "      <td>0.175690</td>\n",
       "      <td>-0.167675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>-0.280164</td>\n",
       "      <td>0.078141</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>0.319869</td>\n",
       "      <td>-0.433904</td>\n",
       "      <td>-0.248495</td>\n",
       "      <td>0.162098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.042939</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052151</td>\n",
       "      <td>-0.123164</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>0.059372</td>\n",
       "      <td>-0.102979</td>\n",
       "      <td>-0.102643</td>\n",
       "      <td>0.108262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.048426</td>\n",
       "      <td>-0.020426</td>\n",
       "      <td>-0.280164</td>\n",
       "      <td>0.052151</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.192005</td>\n",
       "      <td>-0.118873</td>\n",
       "      <td>-0.072873</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>0.118359</td>\n",
       "      <td>-0.000725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.065229</td>\n",
       "      <td>-0.026385</td>\n",
       "      <td>0.078141</td>\n",
       "      <td>-0.123164</td>\n",
       "      <td>-0.192005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423338</td>\n",
       "      <td>0.069238</td>\n",
       "      <td>0.291704</td>\n",
       "      <td>-0.001666</td>\n",
       "      <td>0.065635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.245489</td>\n",
       "      <td>-0.118873</td>\n",
       "      <td>0.423338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020003</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>0.022682</td>\n",
       "      <td>0.039798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>-0.056554</td>\n",
       "      <td>-0.164549</td>\n",
       "      <td>0.319869</td>\n",
       "      <td>0.059372</td>\n",
       "      <td>-0.072873</td>\n",
       "      <td>0.069238</td>\n",
       "      <td>0.020003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.231260</td>\n",
       "      <td>-0.072200</td>\n",
       "      <td>-0.001729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.190926</td>\n",
       "      <td>-0.433904</td>\n",
       "      <td>-0.102979</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>0.291704</td>\n",
       "      <td>0.293337</td>\n",
       "      <td>-0.231260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129078</td>\n",
       "      <td>-0.023439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>-0.018086</td>\n",
       "      <td>0.175690</td>\n",
       "      <td>-0.248495</td>\n",
       "      <td>-0.102643</td>\n",
       "      <td>0.118359</td>\n",
       "      <td>-0.001666</td>\n",
       "      <td>0.022682</td>\n",
       "      <td>-0.072200</td>\n",
       "      <td>0.129078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.013128</td>\n",
       "      <td>-0.167675</td>\n",
       "      <td>0.162098</td>\n",
       "      <td>0.108262</td>\n",
       "      <td>-0.000725</td>\n",
       "      <td>0.065635</td>\n",
       "      <td>0.039798</td>\n",
       "      <td>-0.001729</td>\n",
       "      <td>-0.023439</td>\n",
       "      <td>0.023804</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.042939  0.048426 -0.065229   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.543351 -0.020426 -0.026385   \n",
       "Pclass         -0.035144 -0.338481  1.000000  0.131900 -0.280164  0.078141   \n",
       "Sex             0.042939 -0.543351  0.131900  1.000000  0.052151 -0.123164   \n",
       "Age             0.048426 -0.020426 -0.280164  0.052151  1.000000 -0.192005   \n",
       "SibSp          -0.065229 -0.026385  0.078141 -0.123164 -0.192005  1.000000   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.245489 -0.118873  0.423338   \n",
       "Ticket         -0.056554 -0.164549  0.319869  0.059372 -0.072873  0.069238   \n",
       "Fare            0.002589  0.190926 -0.433904 -0.102979  0.079879  0.291704   \n",
       "Cabin          -0.018086  0.175690 -0.248495 -0.102643  0.118359 -0.001666   \n",
       "Embarked        0.013128 -0.167675  0.162098  0.108262 -0.000725  0.065635   \n",
       "\n",
       "                Parch    Ticket      Fare     Cabin  Embarked  \n",
       "PassengerId -0.001652 -0.056554  0.002589 -0.018086  0.013128  \n",
       "Survived     0.081629 -0.164549  0.190926  0.175690 -0.167675  \n",
       "Pclass       0.018443  0.319869 -0.433904 -0.248495  0.162098  \n",
       "Sex         -0.245489  0.059372 -0.102979 -0.102643  0.108262  \n",
       "Age         -0.118873 -0.072873  0.079879  0.118359 -0.000725  \n",
       "SibSp        0.423338  0.069238  0.291704 -0.001666  0.065635  \n",
       "Parch        1.000000  0.020003  0.293337  0.022682  0.039798  \n",
       "Ticket       0.020003  1.000000 -0.231260 -0.072200 -0.001729  \n",
       "Fare         0.293337 -0.231260  1.000000  0.129078 -0.023439  \n",
       "Cabin        0.022682 -0.072200  0.129078  1.000000  0.023804  \n",
       "Embarked     0.039798 -0.001729 -0.023439  0.023804  1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mtx = df.corr()\n",
    "corr_mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in num_cols:\n",
    "    indx = detect_outlier(test[col])\n",
    "    test[col].loc[indx] = test[col].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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>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>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>15</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>15</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>15</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>15</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name  Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    1   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
       "3          895       3                              Wirz, Mr. Albert    1   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
       "\n",
       "    Age  SibSp  Parch  Ticket     Fare  Cabin  Embarked  \n",
       "0  34.5      0      0     152   7.8292     15         1  \n",
       "1  47.0      1      0     221   7.0000     15         2  \n",
       "2  27.0      0      0      73   9.6875     15         1  \n",
       "3  27.0      0      0     147   8.6625     15         2  \n",
       "4  22.0      1      1     138  12.2875     15         2  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in cat_cols:\n",
    "    test[col] = test[col].astype('category')\n",
    "    #print(col,'---->', dict(enumerate(df[col].cat.categories)))\n",
    "    test[col] = test[col].cat.codes\n",
    "test.head()"
   ]
  },
  {
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026751</td>\n",
       "      <td>0.023245</td>\n",
       "      <td>0.029099</td>\n",
       "      <td>-0.002157</td>\n",
       "      <td>0.034036</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.022608</td>\n",
       "      <td>0.029253</td>\n",
       "      <td>0.040699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.026751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108615</td>\n",
       "      <td>-0.359749</td>\n",
       "      <td>-0.008977</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.292088</td>\n",
       "      <td>-0.519219</td>\n",
       "      <td>-0.357330</td>\n",
       "      <td>0.227983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.023245</td>\n",
       "      <td>0.108615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>-0.105519</td>\n",
       "      <td>-0.169750</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>-0.147965</td>\n",
       "      <td>-0.064809</td>\n",
       "      <td>0.076281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.029099</td>\n",
       "      <td>-0.359749</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130237</td>\n",
       "      <td>-0.015546</td>\n",
       "      <td>-0.064028</td>\n",
       "      <td>0.149711</td>\n",
       "      <td>0.101841</td>\n",
       "      <td>-0.126325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.002157</td>\n",
       "      <td>-0.008977</td>\n",
       "      <td>-0.105519</td>\n",
       "      <td>-0.130237</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334818</td>\n",
       "      <td>0.014975</td>\n",
       "      <td>0.235756</td>\n",
       "      <td>0.029176</td>\n",
       "      <td>0.049236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.034036</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>-0.169750</td>\n",
       "      <td>-0.015546</td>\n",
       "      <td>0.334818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107950</td>\n",
       "      <td>0.207585</td>\n",
       "      <td>0.009311</td>\n",
       "      <td>0.051827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>-0.012301</td>\n",
       "      <td>0.292088</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>-0.064028</td>\n",
       "      <td>0.014975</td>\n",
       "      <td>0.107950</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.184240</td>\n",
       "      <td>-0.169111</td>\n",
       "      <td>0.093672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>-0.022608</td>\n",
       "      <td>-0.519219</td>\n",
       "      <td>-0.147965</td>\n",
       "      <td>0.149711</td>\n",
       "      <td>0.235756</td>\n",
       "      <td>0.207585</td>\n",
       "      <td>-0.184240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187074</td>\n",
       "      <td>-0.042164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>0.029253</td>\n",
       "      <td>-0.357330</td>\n",
       "      <td>-0.064809</td>\n",
       "      <td>0.101841</td>\n",
       "      <td>0.029176</td>\n",
       "      <td>0.009311</td>\n",
       "      <td>-0.169111</td>\n",
       "      <td>0.187074</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.179306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.040699</td>\n",
       "      <td>0.227983</td>\n",
       "      <td>0.076281</td>\n",
       "      <td>-0.126325</td>\n",
       "      <td>0.049236</td>\n",
       "      <td>0.051827</td>\n",
       "      <td>0.093672</td>\n",
       "      <td>-0.042164</td>\n",
       "      <td>-0.179306</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId    Pclass       Sex       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.026751  0.023245  0.029099 -0.002157  0.034036   \n",
       "Pclass         -0.026751  1.000000  0.108615 -0.359749 -0.008977  0.011067   \n",
       "Sex             0.023245  0.108615  1.000000  0.003631 -0.105519 -0.169750   \n",
       "Age             0.029099 -0.359749  0.003631  1.000000 -0.130237 -0.015546   \n",
       "SibSp          -0.002157 -0.008977 -0.105519 -0.130237  1.000000  0.334818   \n",
       "Parch           0.034036  0.011067 -0.169750 -0.015546  0.334818  1.000000   \n",
       "Ticket         -0.012301  0.292088 -0.047663 -0.064028  0.014975  0.107950   \n",
       "Fare           -0.022608 -0.519219 -0.147965  0.149711  0.235756  0.207585   \n",
       "Cabin           0.029253 -0.357330 -0.064809  0.101841  0.029176  0.009311   \n",
       "Embarked        0.040699  0.227983  0.076281 -0.126325  0.049236  0.051827   \n",
       "\n",
       "               Ticket      Fare     Cabin  Embarked  \n",
       "PassengerId -0.012301 -0.022608  0.029253  0.040699  \n",
       "Pclass       0.292088 -0.519219 -0.357330  0.227983  \n",
       "Sex         -0.047663 -0.147965 -0.064809  0.076281  \n",
       "Age         -0.064028  0.149711  0.101841 -0.126325  \n",
       "SibSp        0.014975  0.235756  0.029176  0.049236  \n",
       "Parch        0.107950  0.207585  0.009311  0.051827  \n",
       "Ticket       1.000000 -0.184240 -0.169111  0.093672  \n",
       "Fare        -0.184240  1.000000  0.187074 -0.042164  \n",
       "Cabin       -0.169111  0.187074  1.000000 -0.179306  \n",
       "Embarked     0.093672 -0.042164 -0.179306  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mtx = test.corr()\n",
    "corr_mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
       "0          892       3    1  34.5      0      0     152   7.8292     15   \n",
       "1          893       3    0  47.0      1      0     221   7.0000     15   \n",
       "2          894       2    1  27.0      0      0      73   9.6875     15   \n",
       "3          895       3    1  27.0      0      0     147   8.6625     15   \n",
       "4          896       3    0  22.0      1      1     138  12.2875     15   \n",
       "\n",
       "   Embarked  \n",
       "0         1  \n",
       "1         2  \n",
       "2         1  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del test['Name']\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(random_state=7)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=7, splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df.loc[:, df.columns!='Survived']\n",
    "Y_train = df.loc[:, df.columns=='Survived']\n",
    "X_test = test.loc[:,]\n",
    "tree.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "    </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>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>55</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>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  \\\n",
       "0            1         0       3    1  22.0      1      0     523   7.2500   \n",
       "1            2         1       1    0  38.0      1      0     596  14.4542   \n",
       "2            3         1       3    0  26.0      0      0     669   7.9250   \n",
       "3            4         1       1    0  35.0      1      0      49  53.1000   \n",
       "4            5         0       3    1  35.0      0      0     472   8.0500   \n",
       "\n",
       "   Cabin  Embarked  \n",
       "0     47         2  \n",
       "1     81         0  \n",
       "2     47         2  \n",
       "3     55         2  \n",
       "4     47         2  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>9.6875</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
       "0          892       3    1  34.5      0      0     152   7.8292     15   \n",
       "1          893       3    0  47.0      1      0     221   7.0000     15   \n",
       "2          894       2    1  27.0      0      0      73   9.6875     15   \n",
       "3          895       3    1  27.0      0      0     147   8.6625     15   \n",
       "4          896       3    0  22.0      1      1     138  12.2875     15   \n",
       "\n",
       "   Embarked  \n",
       "0         1  \n",
       "1         2  \n",
       "2         1  \n",
       "3         2  \n",
       "4         2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = tree.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_ID = test.loc[:, test.columns=='PassengerId']\n",
    "Y_ID = Y_ID.values\n",
    "\n",
    "prediction = pd.DataFrame({ 'PassengerId':Y_ID.ravel(),'Survived': Y_pred.ravel()})\n",
    "\n",
    "prediction.to_csv(\"Pred3.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File Name: Pred3.csv\n",
    "\n",
    "যথারীতি সাবমিট করে ফেলেন এই সিএসভি ফাইলটিকে । \n",
    "তো নিজ নিজ কলামের মিডিয়ান দেয়ার পরে  আমাদের একুরেসি কতো এলো সাবমিশনের পরে? দেখেন তো এমন কিনা । <img src=\"pred3.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "44% Accuracy! 😢😢😢😢\n",
    "\n",
    "হতাশ হলেন নাকি?😁  হতাশ হবার কিছু নাই । এমন অনেক হবে । আবার বাউন্স ব্যাক করবো !এতক্ষন ছোট ছোট ডুব দিয়েছি । এবার দেবো বড় ডুব । ব্যাপারগুলোর সাথে ইজি করতে একবারে গভীরে গেলাম না । এখন আশা করি সবাই মোটামোটি বুঝে গিয়েছেন আসলে কি করতে হবে । তাই এবার বড় ডুব দেবো ।  চলেন চলে যাই titanic_4th_try.ipynb তে । "
   ]
  }
 ],
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