Assignment-Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement \n",
    "**Predicting Survival in the Titanic Data Set** \n",
    "\n",
    "We will be using a decision tree to make predictions about the Titanic data  set from Kaggle. This data set provides information on the Titanic  passengers and can be used to predict whether a passenger survived or  not.\n",
    "\n",
    "\n",
    "You use only Pclass, Sex, Age, SibSp (Siblings aboard), Parch  (Parents/children aboard), and Fare to predict whether a passenger  survived. \n",
    "\n",
    "NOTE:The solution shared through Github should contain the source code used and the screen shot of the output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "    </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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv(\"Titanic_dataset.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are going to use Age Column let's remove the Null values from that feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking the Null values.\n",
    "import seaborn as sns\n",
    "sns.heatmap(dataset.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akash\\.conda\\envs\\General\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset[\"Age\"].dropna(), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass', y=\"Age\", data=dataset, palette=\"winter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Age'] = dataset[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "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          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## You use only Pclass, Sex, Age, SibSp, Parch and Fare   \n",
    "\n",
    "X = dataset.drop(columns=[\"PassengerId\", 'Survived', 'Name', 'Ticket', 'Cabin', 'Embarked'])\n",
    "y = dataset.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch     Fare\n",
       "0         3    male  22.0      1      0   7.2500\n",
       "1         1  female  38.0      1      0  71.2833\n",
       "2         3  female  26.0      0      0   7.9250\n",
       "3         1  female  35.0      1      0  53.1000\n",
       "4         3    male  35.0      0      0   8.0500\n",
       "..      ...     ...   ...    ...    ...      ...\n",
       "886       2    male  27.0      0      0  13.0000\n",
       "887       1  female  19.0      0      0  30.0000\n",
       "888       3  female  24.0      1      2  23.4500\n",
       "889       1    male  26.0      0      0  30.0000\n",
       "890       3    male  32.0      0      0   7.7500\n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## handling the categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2     3    4    5        6\n",
       "0  0.0  1.0  1.0  22.0  1.0  0.0   7.2500\n",
       "1  0.0  0.0  0.0  38.0  1.0  0.0  71.2833\n",
       "2  0.0  1.0  0.0  26.0  0.0  0.0   7.9250\n",
       "3  0.0  0.0  0.0  35.0  1.0  0.0  53.1000\n",
       "4  0.0  1.0  1.0  35.0  0.0  0.0   8.0500"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import numpy as np\n",
    "ct = ColumnTransformer(transformers=[('encoder',OneHotEncoder(drop=\"first\"),[0,1])],\n",
    "                       remainder=\"passthrough\")\n",
    "X = ct.fit_transform(X)\n",
    "X = pd.DataFrame(X)\n",
    "#X = X.drop(columns=[1,2])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As instructed using Decision tree.\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[93 17]\n",
      " [22 47]]\n",
      "Accuraccuracy: 0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "print(\"Accuraccuracy:\", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying k-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 77.42 %\n",
      "Standard Deviation: 11.51 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 50)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Grid Search to find the best model and the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8245109546165885\n",
      "{'max_depth': 4, 'min_samples_leaf': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "depths = np.arange(1, 21)\n",
    "num_leafs = np.arange(1, 100, 2)\n",
    "\n",
    "param_grid = [{'max_depth':depths,\n",
    "              'min_samples_leaf':num_leafs}]\n",
    "\n",
    "gs = GridSearchCV(estimator=classifier, param_grid=param_grid, cv=10)\n",
    "\n",
    "gs = gs.fit(X_train, y_train)\n",
    "\n",
    "print(gs.best_score_)\n",
    "\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[99 11]\n",
      " [23 46]]\n",
      "Accuraccuracy: 0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "my_model = gs.best_estimator_\n",
    "\n",
    "my_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = my_model.predict(X_test)\n",
    "\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "print(\"Accuraccuracy:\", accuracy_score(y_test, y_pred))"
   ]
  }
 ],
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