Decision Tree Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Datasets/Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": "code",
   "execution_count": 5,
   "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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30, 87000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred.reshape(len(y_pred), 1)\n",
    "Y_test = Y_test.reshape(len(Y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
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      " [1 0]\n",
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      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred, Y_test), axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53  5]\n",
      " [ 3 19]]\n",
      "0.9\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(Y_test, y_pred)\n",
    "print(cm)\n",
    "acc = accuracy_score(Y_test, y_pred)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "x_set, y_set = sc.inverse_transform(X_train), Y_train\n",
    "x1, x2 = np.meshgrid(np.arange(start=x_set[:,0].min() - 10, stop=x_set[:,0].max()+10, step=1),\n",
    "                     np.arange(start=x_set[:,1].min()-1000, stop=x_set[:,1].max()+1000, step=1))\n",
    "\n",
    "plt.contourf(x1,x2, classifier.predict(sc.transform(np.array([x1.ravel(), x2.ravel()]).T)).reshape(x1.shape),\n",
    "                                                   alpha=0.75, cmap=ListedColormap(('red', 'green')))\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(),x2.max())\n",
    "\n",
    "for i,j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(x_set[y_set==j ,0], x_set[y_set==j, 1], c=ListedColormap(('red', 'green'))(i), label=j)\n",
    "    \n",
    "plt.title('Decision Tree Classification (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}