CART Classifer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fa7671b",
   "metadata": {},
   "source": [
    "# CART Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bbcdb7c",
   "metadata": {},
   "source": [
    "This notebook file consists of a decision tree model built to predict the survival on the Titanic ship. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e25b865c",
   "metadata": {},
   "source": [
    "#### Necessary Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16474699",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5480e7ce",
   "metadata": {},
   "source": [
    "### Loading The Refined Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4363934",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"ReadyToTrain.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58e9db1",
   "metadata": {},
   "source": [
    "#### Viewing the Dataset sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "629bc010",
   "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>Unnamed: 0</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9250</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>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   Age  Sex  Pclass     Fare  SibSp  Parch  Embarked  Survived\n",
       "0           0  22.0    0       3   7.2500      1      0         0         0\n",
       "1           1  38.0    1       1  71.2833      1      0         1         1\n",
       "2           2  26.0    1       3   7.9250      0      0         0         1\n",
       "3           3  35.0    1       1  53.1000      1      0         0         1\n",
       "4           4  35.0    0       3   8.0500      0      0         0         0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731796e8",
   "metadata": {},
   "source": [
    "#### Spliting The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a5b1026",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[ : , 1:8]\n",
    "Y = df.iloc[ : , 8:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e28c11",
   "metadata": {},
   "source": [
    "#### Feature Scaling Of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8a6c405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.2361547 , -2.28188924, -1.77269184, ..., -0.08185509,\n",
       "        -1.06164631, -1.46419714],\n",
       "       [-2.14514077,  2.09988449, -4.63707508, ..., -0.08185509,\n",
       "        -1.06164631,  1.01333632],\n",
       "       [-2.21340122,  2.09988449, -1.77269184, ..., -0.90511835,\n",
       "        -1.06164631, -1.46419714],\n",
       "       ...,\n",
       "       [-2.22477796,  2.09988449, -1.77269184, ..., -0.08185509,\n",
       "         2.02002239, -1.46419714],\n",
       "       [-2.21340122, -2.28188924, -4.63707508, ..., -0.90511835,\n",
       "        -1.06164631,  1.01333632],\n",
       "       [-2.17927099, -2.28188924, -1.77269184, ..., -0.90511835,\n",
       "        -1.06164631,  3.49086977]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n",
    "X =  sc.transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ff32750",
   "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=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2c40e3",
   "metadata": {},
   "source": [
    "### Building The Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c3d5eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For max depth =  1  :  0.776536312849162\n",
      "For max depth =  2  :  0.7206703910614525\n",
      "For max depth =  3  :  0.8044692737430168\n",
      "For max depth =  4  :  0.7821229050279329\n",
      "For max depth =  5  :  0.7932960893854749\n",
      "For max depth =  6  :  0.776536312849162\n",
      "For max depth =  7  :  0.7486033519553073\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "depth = [];\n",
    "\n",
    "for i in range(1,8):\n",
    "    clf_tree = DecisionTreeClassifier(criterion=\"gini\", random_state = 100, max_depth = i)\n",
    "    clf_tree.fit(X_train,y_train)\n",
    "    ypred = clf_tree.predict(X_test)\n",
    "    depth.append(accuracy_score(y_test,ypred))\n",
    "    print(\"For max depth = \",i, \" : \",accuracy_score(y_test,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf89e2dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "68e6e1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(range(1,8),depth,color=\"purple\", marker = \"o\")\n",
    "plt.xlabel(\"Depth\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.title(\"Visualization of the level with highest accuracy\")\n",
    "plt.xticks(range(1,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5870442",
   "metadata": {},
   "source": [
    "The decision tree has the highest accuracy when depth is 3. Now lets use this depth to built our classifier tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "04357a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 0 0\n",
      " 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1 0 1 0 0\n",
      " 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "clf_tr = DecisionTreeClassifier(criterion=\"gini\", random_state = 100, max_depth = 3)\n",
    "clf_tr.fit(X,Y)\n",
    "pred_tree = clf_tr.predict(X_test)\n",
    "print(pred_tree)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}