Predicting sale using Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\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=0, splitter='best')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Decision Tree Classification to the Training set\n",
    "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[62,  6],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 a 2-D array with a single row if you really want 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 a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\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],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\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": 11,
   "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 a 2-D array with a single row if you really want 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 a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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wU2sCMjUhW5f1LOyzddlIjmf9DabQrZtvZQELmDR6EpmEXjf30sv2/du5dfOtvt9jAcDUhOZxzf36AAAykqF5XHPox7L+BlOsq6eLGzfeGHc2QmcBwNSEfMEb9lW515V+uf6GWg8AaarZpOlch8oCgKkZTQ1Nof4Dl7rSLy7882q9vyFNNZs0nWsQyWzMMqYKSl3plxJVf0O1pGkkVZrONQirAZjUKndFn5FMVfobgmrduKqCvRVk4NZcd67CdIZGBGZPnR35ccBGjfllAcCkVrmRRfm+gFpoP65f5m/cd/cnp8LYTQNf2DOF+hteGLC55/Ur0HcshTGbYc9k5OFl1D2zaMj57F5avQaHao8aq1UWAExqlRtZFHZ/Q5RmzfK3X8e2ZbQ1Lqa3bt/hbZmeI2jZtoymojQ6JqygrWUxmt937Cbk3MWc3AJNO4cWBFqH9K6hqeaosVoWWx+AiJwgIg+LyJ9E5BkRuSyuvJh0ampoomV8y+GrwmxdlpbxLTVT8FeqaeciWtqWkz0wBVTIHphCS9tyzwK9vXlpv0AB0Fu3j/bmpdXKbiBp+26HKs4aQDdwhao+JSKNwO9E5AFV/VOMeTIpU0tX+mFo2rnI1xV8Lru5ou1JlLbvdihiCwCquh3Y7j7eKyLrgeMACwDGVKB1dhQV+VJTHmhExzNxSEQfgIhMBU4HHvd4bTGwGCA73jpwjCk058RoRtUUj6MHpw295WhrRhlOYg/lItIA3A58SlVfKX5dVZer6gxVnTGicUT1M2hMClkbejrEWgMQkRE4hf8KVf15nHkxxvRX3Ia+evNqnvvLc0NOr2FkA9MnTg8jayYksQUAERHg+8B6VQ13nTNjTKhaN66C3jo42DDkNHb37AkxRyYMcdYA3gZ8EFgnImvdbV9U1XtjzJMxpoT6rx3yfc+BF+s8Tp44RwGtxvPG9NK6cl1VuWXdmFoSVUewGf4SMQrIr4a9b2bGqifjzoYxiVHNq+rupZmq3s0bxXTONkV0fzUVAIwx8ah2LSOK6ZxtiuiBLACYwNauhd2XWfuuCU8Ui/IM54V+hsoCgAmNtUWbsEQxnbNNET2QXbYZYxKn1LTNQaZzjiLNWmcBwBiTOM3jmslI/+Ip6HTOUaRZ66wJyBiTOPk2+TBH7ESRZq2zAGCMSaQopnO2KaL7syYgY4xJKasBGGNqnt3gNTQWAIwxNc1u8Bo6awIyxtS0cjd4mfIsABhjaprd4DV0gwYAEfknERlXjcwYY0yl7AavofPTB9AE/FZEngJuAu5X1VIrRhsTmY5fn0X77ZeQ65xAdvxOmi+8kaYzH4o7WyZmzeOaPdcvTvMNXn4NWgNQ1X8BTsZZveti4DkR+aqI/FXEeTPmsI5fn0XbzZ8m1zkRyJDrnEjbzZ+m49dnxZ01EzNbv3jofI0CUlUVkR3ADqAbGAfcJiIPqOpno8ygMQDtt19C78FR/bb1HhxF++2XeNYCrLaQLnaD19AMGgBE5DLgQ8Au4EbgM6p6SEQywHOABQADEO1qbZ0TPDfnOicMOK78cRGy8tOHA0a+tgBYEDCmgJ8awDjgPaq6qXCjqvaKyDnRZMvUkunTgVW9g+4XxJos5DwGdWSzGWYWHHvV1HegD34FraC2UMsqDbphTNkdJNDblOHJUjYAiEgdsEBVr/J6XVXXR5EpY4o1N0NbG/QWxJlMxtk+wJ7JnmnkStQiat0cH8E37EV7/ByzmC0KnzxlA4Cq9ohIm4hMVtXN1cqUMcWa3Obd9nanJpDNOoV/k1ez75jNsGfqgM3Z8TsjzWOYbGoDUw1+m4CeEZEngFfzG1X13MhyZYyHpqYSBX6xs5aSufv7/TuNR7zKwTmfYfXm1ZHlz8usybMqfo9NbWCqxU8AuDLyXBgTIjntFlqOPqXfKKDc7CvQk+6l+9WG6mVk1B7W7ljL9InTK3qbrV1rqmXQAKCqEQ7tMCYaTWc+1K/Dt3XjqiG1WwfR+vYRQ3qfTW1gqsXPVBBniMhvRaRLRA6KSI+IvFKNzBmTRja1gakWP01ANwALgJ8BM3DuCZgWZaaMSbMopzawzmVTyNe4LFV9HqhT1R5V/QEwL9psGZNe0U1toLR1th1uSsp3Lnd0dQRM19QqPzWAfSIyElgrIt8EthPTNNJdjb+zscRmUPWZOs/tcfztVNoBnBfV1AbWuWwK+QkAHwTqgE8CS4ATgAvDOLiI3AScA+xU1VMH278h28CME2eEcWiTMkm+AzXuZhnrXE4vP6OA8lNA7AeuDvn4N+P0Mfwo5HSNqQk25t/EqWQAEJF1QMl5/1X1tKAHV9VHRGRq0HSMqVU25t/EqVwNIBETvYnIYmAxQHa8DYMzw0sSxvzb8NL0KhkAimf/jIuqLgeWAzSe2GgrkZlhJVuX9SzsoyqUM5KxlbPMYXYjmEm1jq4O1mxZQ+sLrazZsqbqQyKbxzWTkf7/htEVymIrZ5l+7EYwk1pJ6IDNH6dao4Bs5SxTyO+SkM+LSJ2q9gA/EJHfA18IenARuQWYAxwtIluBL6nq94Oma4wfSemAtULZxCXWG8FUdWEY6RgzFEnogDUmTn5vBMsQwY1gpvbFfRNTENXugDUmaXzfCCYiPcBdwIuqWjtLK5nIJKENPYgoJ10zphaUbMoRke+IyOvdx2OAP+Dcsft7EbGmG1O2Db0WRDfpmjG1oVwN4O2q+gn38YeBDap6vohMBO4Dbok8dybRhkMbunXAmjQr15l7sODx2cAvAFR1R6Q5MjXDFi4xpraVCwC7ReQcETkdeBvwSwARqQdGVyNzJtmqexOTMSZs5ZqAPg78P2Ai8KmCK/+zgHuizphJvmrfxGS8+VrnoMxs2EMZyeVvbQWbuSXpys0FtAGPlb9U9X7g/igzZaqn49dn0X77JeQ6J5Adv5PmC2/st5j6oJ5+P9x+CXROgPE74cIboZL3m0CCrnMwlJFcfo5ZnC44tcOOrg67QEgQUa2dKC3HirJY4s5GsqxbCA8tgz2TYcxmOGspvMFn//y6hbByORw6sm/biFdh/mJ/aaxbCHfeBD0Fbf51OTjvI/7zkFBJXkAmTGu2rCl5L8TME2YmLt2gavm+lSBaL279naoOWE3L11QQSdGw983MWPVk3NlIjI4OaGuD3vxF1p6pZH6xgpb1K2jy8Te9Zg3kDhVtPHQk2XtWMPMvKwZ9/+rV0N1TtLEnS92dK6i/ZwW5HGSz0NxMyfx0dEB7O772rZY0LTsa1UiuJI4Qq/X7VqJQUwEgzbwKyvb2gsLf1dvrbPdTiOZK/C+W2l6su9t7e0+P85NPq835HxuQp+IAVm7f/P5JCxa1rtzd0EGulpN4l3VS5n5KknIrgl1e7o2qem342TEwsKAbPx527BhYUBYX/nl+C/Bs1nvfbMj/o6WCUiUBrNJgYfwpdTf0+NHjA10tJ/Eu6yTWSuJWrq7b6P7MAC4FjnN/PgG8KfqspVO+oMsXzLkcbNvmXVCW4rcAby7xv1hqe7G6On/7gXegqaQGUi5YmKErdTd05/7OQHd5J/Eua7tvZaByo4CuBhCRR4A3qepe9/lV2DDQyHgVdOVkMv33z2T8F+AAIlA4DkAq6GNvanKCkx/Z7MCaTV1dX1NR8b7FgjZXmdK87oZev2u9576VXC0n7S7r5nHNPLvzeTTT1/ElvSNoPro5tZ3DfvoAmuh/V/BBd5uJQCUFWmFfwFDaxdvb+xf+4Dz324fQ2envOJmM04xV3ITjFWxKBbBqNVfVmtaNqyra38/optWbVztD+L0uBrTyY1Z6/Mg8/X70ieNhzlXOiLk9k9HWq9gz+x52vObnqewc9hMAfgQ8ISJ3uM/PB34YXZbSw6tTs1RBVyxfUDY1Db0NPOhVdbn98udRrsNaFerrnZrAYAGsuXlgv0eltZ1KBCnkqm3OqsGrjGvXwu7LKhjddGg0mTqht27f4U2ZniNoaVtO085FQ8lm7KOr2m+/BDonwtqL+23fNvdKSGnnsJ/poJeJyH3A291NH1bV30ebreGvVKfmxIn9O3zBKegmTnSuuMMcWhn0qrrc+2cWDfVe792iQHc3zJo1+LHy+a/GKKD6ZRW0wcWsoQGYHkHCPVlanr+B9ual5LKbyeYm09y+bMiFfxLkOid4v9D4ovf+Kegc9jsM9AjgFVX9gYgcIyInqurGKDM23JXq1OzshJaWoRd0lYyWqfSqerDRSeXeH0YTTpDaTiVmzarukNOOCSsSWdA27VyUiHyEJTt+J7nOiQNf2HscHLV14P4p6BwetE4mIl8CPkffGsAjgJ9Emak0KNf80tTkXEHPmeP8rqTgqWS0TFOTE2zyhXA26zwvNQa/eHTSjh1OzcTP+8eP987v6NHODWmtrc7vjg5fpxmpjg5YX7+C3KVT4UsZcpdOZX39ikjy1jFhBc9OW0xu1CYQJTdqE89OW0zHhMFvxDOVab7wRjIjD/Tblhl5gGN7zkjtpIZ+agAXAKcDTwGo6jYRaYw0VykQVadmpe36fq+qy9VYipt7vJTqMN69u38ekzC2f8OoFfCuxTDSbf8euwnmL2bDfdBEuFfEz01Zitbv67dN6/fx3JSl4ba1p2Nmi7Lyc1wNmPvq9F2M6WqxUUAlHFRVFREFEJEjB3uDGVxUnZpJCSxD3a+SO5kr5bdZp2f20r7CP2/kPmf72oGFcpDmou4jNle03a+0zGVUqaYzH/Kc7DBpQ1arxU8A+KmIfBcYKyIfAz4C3Bhttoa/cp2aQQqUpAYWv6ObIJqx/RXdSTymROE7ZvPAq+t1C+Gxvgn1cjlY/9w+4Ah/39meyU4Nw2u7MRHzMwro2yJyNvAK0AL8H1V9IPKcpYBX80vQKQ+iGi0TNLB4vb+UKMb2VzRv0ivHwZiBnYK8chxz3nhSv01rbvhXcoeKKsXdR7Bhg7/voG7VMnoKm5sADh5B3aplMMb/+RkzFIMGABH5hqp+DnjAY5sJWdAJ3iCa0TJBA4vX+ysZRRRURU1YD34N5n98QKHMg1+j9agPAX1NLKWGFvqdEG/agUWsX4kzjbd7cxIPLWNa96JAAaDUfQzWNGQK+WkCOhtnFFChd3lsMyFI8pQHQQOL1/vHjEneDJ/ZDR8gt1IGFMrZDYuYueoD/ZqBSg4tLFIqiDc1AR2LaP/PRaF9Bl43hlV8I5hJhXKzgV4K/APQLCJPF7zUCDwWdcbSKm1THlRrbH8lmpuh7ZlF9K7r6/DNZKC5xWPfC2+k7eZP03twVMFW73kUgo7EMiZs5WoA/wXcB3wN+HzB9r2q+pdIc5Vi1Z7yIC0qCax+mrsON7FMWgXnrO+/KtvBBth/9MCEx7xA6+zwv0g/U0EY46XcbKB7gD3AQgARmQCMAhpEpEFVg41TM56qOeVBmlQaWMtdlXsWuKfnH0wduFIbOEttnrXUO8Egy3oaE4CfTuD5wLXAscBOYAqwHnh90IOLyDzgeqAOuFFVvx40zeHAmgTCV83Auv6iEbBuATx8Dew5AcZsgXf8C3P+sgJW9b/DN+iynsYE4acT+BrgDOBBVT1dRN4BfCDogUWkDvh3nE7mrcBvReQuVf1T0LRrhS1xWF1+A+uqqe9Am9YO7SAjuyDTw5zzt8L5Fx/e3LpxFa0H7h64/3/+AXr7j/nv7YX1L25m/blvHPRw9f/6sq/J9Izx4icAHFLVThHJiEhGVR8Wkf8bwrHfCjyvqu0AInIrcB6QigBgSxwml05ZRX0ly50VmTV54FDL+ro6OLJrwPbuV473TuSV46n32H/A+5dmwPoAzBD5CQC7RaQBeARYISI7gVdDOPZxwJaC51uBvy7eSUQWA4sBstnhc3dkGOP9TXRmTQ73srpUemtKDCPNjt/JTB95qKV1C0zy+AkA5wEHgCXAIpzbU74cZaYKqepyYDlAY+MMHWT3mpHk8f6migXr7CtgZd9UEgCMeJXc7CuscDeR8zMVxKsAInIUsDLEY78InFDw/Hh3Wyqkbbx/LRl7fXWbVHL1cKDbWSFNBEbVH0n2VyvgV4NPCT09isVgTGr4GQX0ceBqnFpAL84dLgoEHdD8W+BkETkRp+BfALw/YJo1w8b7J1e1C9XCwQAjR8LUqdYMaKrDTxPQp4FTVXVXmAdW1ZPv7OQAAA9sSURBVG4R+SRwP84w0JtU9Zkwj5FkUQ5L9BpdFNWxTDA2GMDEyU8A+DOwb9C9hkBV7wXujSLtWhDFeH+vAmX9eqdpQbVvmxUy5VVriK4NBjBx8hMAvgD8WkQeBw63WqvqP0eWKzNkXgUK9BX+eVbIlFbNq3IbDGDi5CcAfBf4FbAOpw/AJFglBYcVMt7CuCr32wxngwHC0dHVkcolHYPyEwBGqOrlkefEhKKSlbeskPEW9Kq8kma4iROrtybCcNXR1UFbZxu96nyIuZ4cbZ1Olc2CQHl+AsB97s1YK+nfBGQzgiZQqZW3Cgsf6CtkgrZ1b9gA27b1PT/2WJg2Ldg5xC3oVXklzXCdndDSYh30QbS/3H648M/r1V7aX263ADAIPwFgofv7CwXbwhgGaiJQanRRqW1B2rqLC3/oe57EIOA32AUdoltpM5xN/hdMrsf7Ay+13fTxcyPYidXIiAlPqQKleNuaNcHauosL/8LtSQsAlXTsBh2ia81w1ZWty3oW9tk6+3AHU25FsLmq+isReY/X66r68+iyZaohyhEoa9Ykq0mjmsMtK22GM8E0j2vu1wcAkJEMzePswx1MuRrAbJzRP/M9XlPAAkCNq6vrW7i8eHtQ+SCSlHsOKgl2QYeBVtIMF3dgHA7y7fw2Cqhy5VYE+5L78MuqurHwNXf6BlPjZOCytWW3Fzv22NLNQIWScM9BJR27YdQW/DbDmXA0NTRZgT8EGR/73O6x7bawM2Kqr7u7su3Fpk1zgoAfcd9z0NzsNLkUKtUEYzdnmbQo1wfwWpxlH8cU9QMchbM2sAko7hXBwrgJadq0/h2++bb/IGlGIQlrLcf9fRtTrFwfQAtwDjCW/v0Ae4GPRZmpNEjCJGBRzEia5FlO4xxu2dEBzz7b/0awZ5/ty5cxcSjXB3AncKeIzFTVNVXMUyokYRKwKK6Kk3ClXYrfK/BSNaP6+qGPbnruuYE3gqk624N8Nq2z/bTi4gzpMKaInxvBLhCRZ4D9wC+B04AlqvqTSHM2zCWlnTmKq+JK0qxWs0glNS6vWoyI0zeS7x+ptMYWtL+lnDknWuluhsbP5cPfqOorOM1BLwAnAZ+JMlNpUKpNPKq28o4O5+q1tdX53dERzXEqkS+Ui4eMRpG3cjWuYk1NzvQM+e8imx3YgVzu/cbUCl+Twbm//w74maruEb/jBE1J1WwrT0J/g5dqNoNVWuMqrsW0tlb2/uKaTfFNYHlh3HNhzFD5CQArReRZnCagS0XkGJzlIU0A1WwrT0J/g5dqNoMFvemtkhFTXgG31DVTNafMsEXmTTE/cwF9XkS+CexR1R4R2QecF33Whr9qjUpJSn9DsWrOhR/0prdKamxeAVfV6USuq4unc3ztjrXQW8ecRw9V54CmJpTsAxCRzxY8PUtVewBU9VXAVgOrIdXub/CrkpuzggraCevVL9DS4l2Alwqs3d0wcybMmeP8TsLIKJNu5WoAC4Bvuo+/APys4LV5wBejypQJV1LH5kfZDFbcBl+qCaiSIOi3xpbkVb7sZjRTqFwAkBKPvZ6bBEvy2PwomsH8tsFHFQSTGnBZtyD2wQBJ7IeYc+Ls1C4pWS4AaInHXs9NwqVp0ZG42+CTEHCLC7SMZODha2IdDDD2+uQtKb77n0bw+NbHyfXkUrmkZLkA8EYReQXnan+0+xj3uc0FZBKrXBv8rFnVyUOs0054rJELwORHYd2UAftXazDA9OnVOU4lWoED3QfQomvatCwpWW4qCBuhbGpSktvgq8FrjVwAzv48rPvAwO1jNtH69pOiz1gSZXoGFP55aVhS0s99AMbUlMS2wVdJyYKr8UUYsQ8OHdG3bcQ+Rs/7CtkjG6qTuSrKdecOX90Lwqj6UWTrB14F7D+0P7VLSloAMMNOEtrg41Ryjdz6LM0fvpb22y8h1zmB7PidNF94I01n/hlIYPtMAPlmsPzVvaLkenJMHTt1QLNOcZMZpGdJSQsAZlhKU6d3sXJr5Dad8BBNZz4UY+6qw6sZrFS7fpqXlIwlAIjIe4GrgFOAt6rqk3Hkw5jhKKwCrZaHRpZqBiu1Pa1LSsZVA/gj8B7guzEd3wyB3URUO4IWaF4jiWppaGTJZrAUtOtXIpYAoKrrAWxW0dpRzRlFLdDEr5ImlEpUq1ZRrhnM9LE+ADOAVwFcrRlFkzp19XDmVShX2oTi9zjVqlWkuV2/EpEFABF5EJjo8dJSd7lJv+ksBhYDZLOTQ8qdKaVUAVxc+OeFfRNRUqeuTrIgV9WlCuU6qaNHB06eFKQJJapaRSlpbdevRGQBQFXfGVI6y4HlAI2NM2wKioiVKoBLCfvmqqROXZ1cGuiqulShXJ+pJ0Mm1CaUKGoVJhifK0qbtChX0BZ32YiEf3NVUqeuTrJSV9V+lCp8u3u7aRnfcviKP1uXpWV8S6Ar6lK1B+uYjU9cw0AvAP4NOAa4R0TWqurfxpEX01+paRTq6wfOne+1xGFQab+LNyx+r6rLjZYJuwnFOmaTJ65RQHcAd8Rx7LTyO7KmVAFcqrAPu20+7XfxhsXvVXW5QjnsETvWMZs8NgooBSoZWVOqAF6/3jvtKNrm03wX71BkZOht9aUKZSCSETvWMZssFgASKOxx8JWOrPEqgPP5KWZt83ETWsa3BLqq9iqU12xZU9UROyYeFgASJopx8GGMrKm1tvk03UwWxVW1jdhJBxsFlDDlrtaHKoyRNZUsih63fBDNB7h8EO3oiDdftcRG7KSD1QASJopx8GFdvddK27zdTBacjdhJBwsACRPFalZpG1ljN5MFZyN20sECQMJE1dZeK1fvYSh3L8OaNcMvCEY1wZqN2Bn+LAAkTNqu1qPgFURFnBvZ8jezDZ9J5ryngnhh9wsDlj+cPnF4rfplgrMAkEBRXK1HNSomiaNtvIJodzf0FM1tNlz6BbyGa+4/tJ/9uUP9trduXMWcE2dXM2sm4SwApEBUUywneerm4iDa2uq93/DtFxDmPNoXANauhd2X2aA/058FgBSIalRMtUfbJLG2kVTZnE2dbgZnASAFohoVU83RNkmubcSteCoIFJrbl8WXIVMzrE6YAlFNsVzNqZuD3iA3fKeZlgHTNgM07VwUZ6ZMjbAaQApENbQ0qnS9mnqC1jZqbSqLShQP12zduCrG3JhaYgEgBaIaWhpFuqWaerzWIwD/V/A2vNaYgSwApERUN4KFnW6pph4R54o9yBV8mm6GM8YP6wMwiVKqSaenp3YmozOmVlgNwCRKubmQ7AremHBZDcAkSnOz07RTaLh01hqTNFYDMIlinbXGVI8FAJM41tRjTHVYE5AxxqSUBQBjjEkpCwDGGJNSFgCMMSalLAAYY0xKWQAwxpiUsgBgjDEpFUsAEJFvicizIvK0iNwhImPjyIcxxqRZXDWAB4BTVfU0YAPwhZjyYYwxqRVLAFDV/1HV/OzuvwGOjyMfxhiTZknoA/gIcF+pF0VksYg8KSJPHjr0UhWzZYwxw1tkcwGJyIPARI+Xlqrqne4+S4FuYEWpdFR1ObAcoLFxhkaQVWOMSaXIAoCqvrPc6yJyMXAOcJaqWsFujDFVFstsoCIyD/gsMFtV98WRB2OMSbu4+gBuABqBB0RkrYh8J6Z8GGNMasVSA1DVk+I4rjHGmD5JGAVkjDEmBhYAjDEmpSwAGGNMSlkAMMaYlLIAYIwxKWUBwBhjUsoCgDHGpJQFAGOMSSkLAMYYk1IWAIwxJqUsABhjTEpZADDGmJSyAGCMMSllAcAYY1LKAoAxxqSUBQBjjEkpqaXleEXkJWBT3PmIwNHArrgzEYHhel4wfM9tuJ4XDN9z83NeU1T1mOKNNRUAhisReVJVZ8Sdj7AN1/OC4Xtuw/W8YPieW5DzsiYgY4xJKQsAxhiTUhYAkmF53BmIyHA9Lxi+5zZczwuG77kN+bysD8AYY1LKagDGGJNSFgCMMSalLAAkhIh8S0SeFZGnReQOERkbd57CICLvFZFnRKRXRGp+CJ6IzBORNhF5XkQ+H3d+wiIiN4nIThH5Y9x5CZOInCAiD4vIn9y/w8vizlNYRGSUiDwhIn9wz+3qStOwAJAcDwCnquppwAbgCzHnJyx/BN4DPBJ3RoISkTrg34F3Aa8DForI6+LNVWhuBubFnYkIdANXqOrrgDOAfxxG31kOmKuqbwSmA/NE5IxKErAAkBCq+j+q2u0+/Q1wfJz5CYuqrlfVtrjzEZK3As+raruqHgRuBc6LOU+hUNVHgL/EnY+wqep2VX3KfbwXWA8cF2+uwqGOLvfpCPenolE9FgCS6SPAfXFnwgxwHLCl4PlWhklhkgYiMhU4HXg83pyER0TqRGQtsBN4QFUrOrf6aLJlvIjIg8BEj5eWquqd7j5LcaqtK6qZtyD8nJcxcRKRBuB24FOq+krc+QmLqvYA090+wztE5FRV9d2PYwGgilT1neVeF5GLgXOAs7SGbtAY7LyGkReBEwqeH+9uMwkmIiNwCv8VqvrzuPMTBVXdLSIP4/Tj+A4A1gSUECIyD/gscK6q7os7P8bTb4GTReREERkJLADuijlPpgwREeD7wHpVvTbu/IRJRI7JjxYUkdHA2cCzlaRhASA5bgAagQdEZK2IfCfuDIVBRC4Qka3ATOAeEbk/7jwNldtJ/0ngfpzOxJ+q6jPx5iocInILsAZoEZGtIvLRuPMUkrcBHwTmuv9Xa0Xk3XFnKiSTgIdF5Gmci5MHVPXuShKwqSCMMSalrAZgjDEpZQHAGGNSygKAMcaklAUAY4xJKQsAxhiTUhYAjPFJRM4XERWR18adF2PCYAHAGP8WAqvd38bUPAsAxvjgziUzC/gozh3AiEhGRP7DXcfhARG5V0Qucl97s4isEpHficj9IjIpxuwb48kCgDH+nAf8UlU3AJ0i8macdQ6m4qwN8EGcu53zc8/8G3CRqr4ZuAlYFkemjSnHJoMzxp+FwPXu41vd5/XAz1S1F9jhTsYF0AKcijOtB0AdsL262TVmcBYAjBmEiLwGmAu8QUQUp0BX4I5SbwGeUdWZVcqiMUNiTUDGDO4i4MeqOkVVp6rqCcBGnBW0LnT7ApqAOe7+bcAxInK4SUhEXh9Hxo0pxwKAMYNbyMCr/dtxFsHZCvwJ+AnwFLDHXS7yIuAbIvIHYC1wZvWya4w/NhuoMQGISIOqdonIeOAJ4G2quiPufBnjh/UBGBPM3e6iHCOBr1jhb2qJ1QCMMSalrA/AGGNSygKAMcaklAUAY4xJKQsAxhiTUhYAjDEmpf4/qppKuByRGksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Visualising the Test set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\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],\n",
    "                c = ListedColormap(('blue', 'green'))(i), label = j)\n",
    "plt.title('Decision Tree Classification (Test 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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}