decision tree assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting Survival in the Titanic Data Set\n",
    "We will be using a decision tree to make predictions about the Titanic data\n",
    "set from Kaggle. This data set provides information on the Titanic\n",
    "passengers and can be used to predict whether a passenger survived or\n",
    "not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from pandas import Series, DataFrame\n",
    "from pylab import rcParams\n",
    "from sklearn import preprocessing\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://raw.githubusercontent.com/BigDataGal/Python-for-Data-Science/master/titanic-train.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renaming the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv(url)\n",
    "titanic.columns = ['PassengerId','Survived','Pclass','Name','Sex','Age','SibSp',\n",
    "                  'Parch','Ticket','Fare','Cabin','Embarked']\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You use only Pclass, Sex, Age, SibSp (Siblings aboard), Parch (Parents/children aboard), and Fare to predict whether a passenger survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      2\n",
       "Pclass        3\n",
       "Sex           2\n",
       "Age          88\n",
       "SibSp         7\n",
       "Parch         7\n",
       "Fare        248\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age has 177 nan values which need to be filled.\n",
    "\n",
    "A distribution plot showing age of survivors vs non-survivors for both men and women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1b31f075d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "women = titanic[titanic['Sex'] == 'female']\n",
    "men = titanic[titanic['Sex'] == 'male']\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 7))\n",
    "ax = sb.distplot(women[women['Survived'] == 1].Age.dropna(), bins=18, label = 'survived', ax = axes[0], \n",
    "                  kde =False)\n",
    "ax = sb.distplot(women[women['Survived'] == 0].Age.dropna(), bins=18, label = 'not-survived', ax = axes[0], \n",
    "                  kde =False)\n",
    "ax.legend()\n",
    "ax = sb.distplot(men[men['Survived'] == 1].Age.dropna(), bins=18, label = 'survived', ax = axes[1], \n",
    "                  kde =False)\n",
    "ax = sb.distplot(men[men['Survived'] == 0].Age.dropna(), bins=18, label = 'not-survived', ax = axes[1], \n",
    "                  kde =False)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot showing number of male and female survivors vs non-survivors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(x='Survived', hue='Sex', data=titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation\n",
    "Since the male to female survivor ratio is different so we will impute nan values differently for men and women also further on survivors and non survivors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([ 19,  28,  31,  32,  47,  82, 109, 128, 166, 186, 198, 241, 256,\n",
      "            274, 300, 303, 306, 330, 334, 347, 358, 359, 367, 368, 375, 431,\n",
      "            457, 533, 573, 596, 612, 653, 669, 697, 727, 849],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "women_survivor_age = titanic.index[(titanic['Sex']=='female') & \n",
    "                                   (titanic['Age'].isnull()) & (titanic['Survived']==1)]\n",
    "print(women_survivor_age)\n",
    "women_survivor_mean = titanic[(titanic['Sex']=='female') & (titanic['Survived']==1)].Age.mean()\n",
    "titanic.loc[women_survivor_age, 'Age'] = titanic.loc[women_survivor_age]['Age'].fillna(women_survivor_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([140, 180, 229, 235, 240, 264, 409, 415, 485, 502, 564, 578, 593,\n",
      "            680, 792, 863, 888],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "women_nonsurvivor_age = titanic.index[(titanic['Sex']=='female') & \n",
    "                                   (titanic['Age'].isnull()) & (titanic['Survived']==0)]\n",
    "print(women_nonsurvivor_age)\n",
    "women_nonsurvivor_mean = titanic[(titanic['Sex']=='female') & (titanic['Survived']==0)].Age.mean()\n",
    "titanic.loc[women_nonsurvivor_age,'Age'] = titanic.loc[women_nonsurvivor_age]['Age'].fillna(women_nonsurvivor_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([17, 36, 55, 65, 107, 298, 301, 444, 507, 547, 643, 692, 709, 740,\n",
      "            828, 839],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "men_survivor_age = titanic.index[(titanic['Sex']=='male') & \n",
    "                                   (titanic['Age'].isnull()) & (titanic['Survived']==1)]\n",
    "print(men_survivor_age)\n",
    "men_survivor_mean = titanic[(titanic['Sex']=='male') & (titanic['Survived']==1)].Age.mean()\n",
    "titanic.loc[men_survivor_age, 'Age'] = titanic.loc[men_survivor_age]['Age'].fillna(men_survivor_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([  5,  26,  29,  42,  45,  46,  48,  64,  76,  77,\n",
      "            ...\n",
      "            793, 815, 825, 826, 832, 837, 846, 859, 868, 878],\n",
      "           dtype='int64', length=108)\n"
     ]
    }
   ],
   "source": [
    "men_nonsurvivor_age = titanic.index[(titanic['Sex']=='male') & \n",
    "                                   (titanic['Age'].isnull()) & (titanic['Survived']==0)]\n",
    "print(men_nonsurvivor_age)\n",
    "men_nonsurvivor_mean = titanic[(titanic['Sex']=='male') & (titanic['Survived']==0)].Age.mean()\n",
    "men_nonsurvivor_mean\n",
    "titanic.loc[men_nonsurvivor_age, 'Age'] = titanic.loc[men_nonsurvivor_age]['Age'].fillna(men_nonsurvivor_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will convert the sex to different columns for male and female using one hot encoding as preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>71.2833</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  female\n",
       "0         0       3  22.0      1      0   7.2500     0       1\n",
       "1         1       1  38.0      1      0  71.2833     1       0\n",
       "2         1       3  26.0      0      0   7.9250     1       0\n",
       "3         1       1  35.0      1      0  53.1000     1       0\n",
       "4         0       3  35.0      0      0   8.0500     0       1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehotencoder = preprocessing.OneHotEncoder()\n",
    "X = onehotencoder.fit_transform(titanic['Sex'].values.reshape(-1,1)).toarray()\n",
    "df = pd.DataFrame(X.astype(int), columns=['male', 'female'])\n",
    "df.head()\n",
    "titanic = pd.concat([titanic, df], axis=1)\n",
    "titanic.drop(['Sex'], axis=1, inplace=True)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  male  female  0  1  2\n",
       "0         0  22.0      1      0   7.2500     0       1  0  0  1\n",
       "1         1  38.0      1      0  71.2833     1       0  1  0  0\n",
       "2         1  26.0      0      0   7.9250     1       0  0  0  1\n",
       "3         1  35.0      1      0  53.1000     1       0  1  0  0\n",
       "4         0  35.0      0      0   8.0500     0       1  0  0  1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = onehotencoder.fit_transform(titanic['Pclass'].values.reshape(-1,1)).toarray()\n",
    "df = pd.DataFrame(X.astype(int))\n",
    "df.head()\n",
    "titanic = pd.concat([titanic, df], axis=1)\n",
    "titanic.drop(['Pclass'], axis=1, inplace=True)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the null values have been filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "male        0\n",
       "female      0\n",
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will divide the ages to different groups so that it becomes a categorical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['Age'] = titanic['Age'].astype(int)\n",
    "titanic.loc[ titanic['Age'] <= 11, 'Age'] = 0\n",
    "titanic.loc[(titanic['Age'] > 11) & (titanic['Age'] <= 18), 'Age'] = 1\n",
    "titanic.loc[(titanic['Age'] > 18) & (titanic['Age'] <= 22), 'Age'] = 2\n",
    "titanic.loc[(titanic['Age'] > 22) & (titanic['Age'] <= 27), 'Age'] = 3\n",
    "titanic.loc[(titanic['Age'] > 27) & (titanic['Age'] <= 33), 'Age'] = 4\n",
    "titanic.loc[(titanic['Age'] > 33) & (titanic['Age'] <= 40), 'Age'] = 5\n",
    "titanic.loc[(titanic['Age'] > 40) & (titanic['Age'] <= 66), 'Age'] = 6\n",
    "titanic.loc[ titanic['Age'] > 66, 'Age'] = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will divide the dataset into X and y for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic.drop(\"Survived\",axis=1)\n",
    "y = titanic[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split\n",
    "Now we will split into train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting into model\n",
    "We will now fit decision tree model onto the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(random_state = 0)   \n",
    "model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.89      0.83       154\n",
      "           1       0.82      0.66      0.73       114\n",
      "\n",
      "    accuracy                           0.79       268\n",
      "   macro avg       0.80      0.77      0.78       268\n",
      "weighted avg       0.79      0.79      0.79       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will see the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[137,  17],\n",
       "       [ 39,  75]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, predictions)"
   ]
  }
 ],
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