Predicting sale using Random Forest.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"
   ]
  },
  {
   "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\n"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "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": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Random Forest Classification to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63,  5],\n",
       "       [ 4, 28]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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('Random Forest 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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IN6TpmNBBW0tlS+IZE4ZzZpwTuxE/XiwAmJrQ29fLhl0bGNRBADIDGTbs2gBgQcCYUbI+AFMTund3H8n8swZ1kO7d3RGlyCTJIIOoatTJGJGqMsjgyDu6LACYmpAZyJS13Zgg9RzoIbMvE+sgoKpk9mXoOeB/uKk1AZmakG5Ie2b26YZ0KNez/gaT68YtN7KIRUweO5lUTMvNgwzSc6CHG7fc6PsYCwCmJnRM6BjWBwCQkhQdEzoCv5b1N5h8fQN9XLvp2qiTETgLAKYmZDPeoEvlXiX9Uv0NtR4A8u8X4tukUSmrxY3MAoCpGW0tbYH+ARcr6edn/lm13t/gdb8AvZNW0LZzcZRJC5zV4vyJZ2OWMVVQrKRfTFj9DdXidb8IdHcsjSZBIbJRY/5YADCJVapEn5JUwesw+huqqehIqvSWKqckfDZqzB8LACaxipXo0w1pZk6ceeT97Otabzooer+ZqZ7beyetYO1p0+mcl2LtadPpnbQizOQFqtR3a4ZYH4BJrFIji4Lub4gDr/tFoaN7WcG+vZNWsGHmEgYb9gOQad7MhplLAGqiv6Cao8ZqWWQ1ABE5XkRWicijIvKIiFwcVVpMMrW1tNVlSb+Y7P2igArpg9OYte4Gzwy9u2Ppkcw/a7Bhf830FyTtux2tKGsA/cBHVfVBEWkF/iQid6rqoxGmySRMPZb0S2lraWPdU+sBp+1/3awLPQNAsX6BWuovSNp3OxqRBQBV7QF63J/3icg64FjAAoAxIZp/wrwjP3duWk3nPK+GgGLPB2jB/vNX+597xsRLLPoARGQ6cApwr8d7S4AlAOmJ1oFjTJByg0Gu/HH04LShzzx6eDNK56bVoafRhCfyUUAi0gLcDHxEVZ/Nf19Vl6vqHFWd09TaVP0EGpNA1oaeDJHWAESkCSfzX6GqP4syLcaY4fLb0NdsWcNjzzwWYYpM0CILACIiwPeAdaoa7DpnxphAdW5aDYMNcKhl2HbpnR1RikwQoqwBvBq4EHhYRLrcbZ9U1duKHdCX6bM2x5gp1oZsateaLWvoHxgo2N74hcPMnRtBgkxoohwFtAaQco5p2fcK5qx+IKQUmXJ5jx4xlSpWyBk/dhyz20cucZcqJM0/YZ6/QtTBccy/d/fwbZb5151YjAIyxgyXP7Sy88wqD4Bo3lsywFdj6GcY0znbFNHDWQAwps6M1CxXabNdNZphw5jO2aaILmQBwJgY8ip9+2n+qZbc9IVRGwhjUZ56XuhntCwAGBMzce9Yz3+SOAxhTOdsU0QXsl48Y0zshDGds00RXcgCgDEmdjomdAS+KE8Y56x11gRkjImdbJt8kCN2wjhnrbMAYIwJ3J4Dez37B8rp3whjOmebIno4CwDGmEDN//1hz+324GD8WAAwxtQ8e8BrdCwAGGNqmj3gNXpWJzPG1LRSD3iZ0iwAGGNqmj3gNXojNgGJyL8DN6jq7pH2NcnjNdLD76yVxgQh3ZD2zOyT/ICXX376ANqA+0XkQeA64A5VLbZitEkQrzlgwpy1svees+i++b1kdk0iPXEnHeddS9sZd4V2PVMbOiZ0eK5fnOQHvPwasQlIVT8FnIizete7gMdE5PMi8nchp82YI3rvOYsN13+MzK52IEVmVzsbrv8YvfecFXXSTMRs/eLR8zUKSFVVRHYAO4B+YALwUxG5U1UvCzOBxgB03/xeBg81D9s2eKiZ7pvf61kLsNpCstgDXqPjpw/gYuCdwNPAtcDHVfWwiKSAxwALAGaYYk+Bjkb2ydHMrkme73ttz9YWsgEjW1sALAgYk8NPDWAC8FZV3Zy7UVUHReSN4STL1KpiT4GORu6To+mJO93mn+HSE3cWbCu3tmBMUpUMACLSACxS1Su93lfVdWEkyph8HeddO6xUD0DTc2TmfbSwtlFGbcGYJCsZAFR1QEQ2iMhUVd1SrUSZ+hLEHDDZkntuu35m3kfh5B8V7jxuK+ydVrDZq7YQVza1gakGv01Aj4jIfcBz2Y2q+qbQUmXqQlcX9PUB84JZ5artjLs8mnAKz9v7jh8W1BZSYw7Scd61xdO6o4u+Q31lp2nu1LllHzMSm9rAVIufAHBF6KkwdWnPxdE8aO5VWxhpFNCeA3vh4Liyr9W5aXXgSzja2rWmWkYMAKoazqKfJhGiWt/Wu7ZQ2vx7y3vYvasrnCBnUxuYavEzDPQ04BvALGAM0AA8p6pHhZw2YwIT1uLlYbCpDUy1+GkC+iawCLgJmIPzTMCMMBNlTBi8pq6IozCnNrDOZZPLV/1VVR8HGlR1QFW/DywMN1nGJFdYUxtkO5eztYts53JvX2/FaTa1yU8NYL+IjAG6ROTLQA82jbSpQYEvSRhi90YYUxtY57LJ5ycAXIjT7v8h4BLgeOC8IC4uItcBbwR2qupJQZzTGC9RdUb7EXSzTPH+DgUp3Gqdy8nlZxRQdgqIA8BVAV//epw+hh8EfF5jakJYY/7z+zucEUseub9JtKIBQEQeBorO+6+qL6304qr6OxGZXul5jKlV1ixjolSqBhCLid5EZAmwBCCdnhpxaowJVhzG/Nvw0uQqGgDyZ/+MiqouB5YDtLbOsZXITF2p9pj/lKRs5SxzxIjDIkTkNBG5X0T6ROSQiAyIyLPVSJwxYevt62Xt1rV0PtHJ2q1rqz4ksmNCBykZ/mcYXqYstnKWGcYeBDOJFYdJ17LXqdbDWbZylsnld0nIx0WkQVUHgO+LyJ+Byyu9uIj8CJgPHC0i24DPqOr3Kj2vMX7EpQPWMmUTlUgfBFPV84M4jzGjEYcOWGOi5Ccjv9Dd70M46wEE9iCYqX29k1aw9rTpdM5Lsfa06fROWhF1knwr1tFqo2JMUvh+EExEBoBfAk+qau0srWRC0ztpBRtmLmGwYT8AmebNbJi5BIC2nYujTJovYU66ZkwtKFoDEJHviMhL3J/HAX/BeWL3zyJiTTeG7o6lRzL/rMGG/XR3LI0oReUJa9I1Y2pFqRrAmar6Affni4CNqvoWEWkHbgc8FmM1SZJJey8TXWx7HFkHrEmyUn0Ah3J+Phv4OYCq7gg1RaZmpDPeT2YX226MiZdSAWCPiLxRRE4BXg38GkBEGoGx1UicibeO7mWkBp43bFtq4Hl0dC+LKEXGmHKUagJ6P/B1oB34SE7J/yzgV2EnzMRftqO3u2MpmfQW0pmpdHQvq4kO4HpXsPZBidmww1olrHfSimG/GyXmljQRKTUX0EY8Vv5S1TuAO8JMlKme3l7o7oZMBtJp6OiAtnL+9h9eDL9YDBkgDXQA1qQeqXLWPgjraWivEWKocz3rc4kPX08Cm/iqJAPv7YUNG2DQHQWZyTivwd85enth/XpQHTp+/frhx1e6GLuUmMJeQyxQ5l83/1pxXmCmHGE8Dd35qgnQvKdw8Rkh8mmubU3k4SwA1LBKM/Du7qFjswYHne1+jn/sscKMURU2bnTOQefgiEGpVABbswYGLnhN0es33rCKuXNHTme5vK6be63Oealhga2Wg0EoT0OP6fNceazi81YoDnM/xY0FgBrhlVFWmoFnivwtFtuer7/fe/vAgPMve65iQWmkADZ3LvDEqmH7D/sMTvSXznLlX9fZOPRjdrUtZ5Wt2l4eu9R01JWUlgVBPdr8o3zKOi5zP8VJqRXBLi11oKpeHXxyDBRmdBMnwo4dhRllfuaf5TcDT6e9900H/DdaLCiVE8Aqre0Yb8Wehp44dmJFpeXmxmYyA5lYPWVtcz8VKlUDaHX/nwmcijMNBMA5wH1hJirJvDK67dsL9yuW+YP/DLyjA9at897uR0PDUEl/JF6BppwaSKW1nTBV2s8RBr/NUsWmoy63tJxfG0o3tjB9/PRYtbdXe/GdWlBqFNBVACLyO+DlqrrPfX0lNgw0NF4ZXSmp1PD9Uyn/GTg4nZ257filOl3ztbV5Bycv6XRhzaZYAPEKYJU2V4Vh9mxgdRlfVpUUDAEdgdfT0Oue9igZMHJpOT/wxKlppWNCB+t3Po6mDh/ZJoNNdBzdkdjOYT99AG0Mfyr4EDbQLzTlZGi5fQGjGQXU3e3dieu3VL1rl7/rpFJOM1Z+zcYr2BQLYNVqrqoXxWolfmoGa7ascYbse3w/NV1afuif0fuOg/lXwrgtsHcq2nkle+f9ih0v+FkiO4f9BIAfAPeJyC3u67cA/xtekpLDq2O3WEaXL5tRtrWNvgmk0lJ1qf2y91Gqw1oVGhudmsBIAayjo7Dfo9zaTlLM96iVZJtoOjetRgTmTR8hEBweS6pBhk/2p5Dpz8SyycuP7pvfC7vaoetdw7ZvX3AFJLRz2M900MtE5HbgTHfTRar653CTVf+KdWq2tw/v8AUno2tvd0rcfkr6fp8NqLRUXer4008fvs2rrwGckUR+hnJm01/RQ2sJNns2rFnm/FL1L02xZssa5k4t8cEPpJn5+Dfr6invzK5J3m+0Pum9fwI6h/0OA30e8Kyqfl9EjhGRE1R1U5gJq3fFOjV37YKZM6vzcFe5peqRRieVOj6IJpxKajvlqvgJ6XKulTdlQlgZ7ZHnGA6Og+f3jbh/287FNZ3h50tP3ElmV3vhG/uOhaO2Fe5fy81dPo3YWyQinwH+g6E1gJuAG8JMVBKUan5pa3NK0PPnO/+Xk/GUGi2Tr63NCTbZTDiddl57XS8bWLLpzmSczL+93d/xEyd6p3fsWFi7Fjo7nf97e33dZqh6e2Fd4woyH5wOn0mR+eB01jWuCCVtvZNWsH7GEmeqBFEyzZtZP2NJTa2sVis6zruW1JiDw7alxhxkysBppGR4Vhj1kNVq8VMDOBc4BXgQQFW3i0hr6UPMSMLq1Cy3Xd9vqbpUjSW/ucdLsQ7jPXuGpzEOY/s3Nq+A1y+BMW779/jNcM4SNt4ObQRbIn5s2lK0cfiiOtq4n8emLR116dvfKKCGss+7Zo339jCexg5D2xl3AU5fQGbXJNITd9Jx3rW0nfI04/pm2iigIg6pqoqIAojI80NOUyKE1akZl8Ay2v3CHNvvt1lnYN4nhzL/rDH7GZi3FLoKM+VKmov6n+e9eE6x7X4FPT3F6umvQecV6fyN4VDYYtrOuOtIIBi2PaELA/kJAD8Rkf8BxovI+4B3A9eGm6z6V6pTs5IMJa6Bxe/oJghnbH9ZTxKP2+p9knGFmXLFTyjvnerUMLy2x4zX6KFaHRFkHH5GAf2XiJwNPIvzVPCnVfXO0FOWAF7NL5VmKGGNlqk0sHgdX0wYY/vLepL42WNhXGGnoFemXOy82QnxRvoOGlYvYyC3uQng0PNoWL0Mxvm7t2pRtQy/3owYAETkS6r6H8CdHttMwIKY8iCM0TKVBhav48sZRVSpspqwfvsFOOf9BZkydy2DvM7sYuf1OyHejIOLWbcSOGvpkYeTuGsZM/oXVxQARsqoGxsaSg8DzTPviVWsKTb0o0b6AEwhP01AZ+OMAsr1eo9tJgBxnPIgq9LA4nX8uHHxG9uf3ngBmZVSkCmnNy6GvA5vv01bxYJ4WxvQu5ju/7c4sM/A60GwXJ2vmuBrGGi+WunsNf6Vmg30g8C/Ah0i8lDOW63AH8JOWFIlbcqDao7t96ujAzY8spjBh4c6fFMp6JhZZF+fTVuVjsSKs9mzoZOhmsf4seOY3T470jSZkZWqAfwfcDvwBeATOdv3qeozoaYqwWzKg3CUE1jLae7y2ndgwHuthEqCuLPK1l7P90Yq8VdL47JBBi54DTrN+glqRanZQPcCe4HzAURkEtAMtIhIi6pWNk7NeLIpD8JRbmAtp1Sev29+R/5I1/I16mtM+U021dTV5UwxIeLMIWel/9rgpxP4HOBqYAqwE5gGrANeUunFRWQh8DWcp1KuVdUvVnrOelAPTQJxU83AWs61/I76mv/7w4UHx9CIk8yZWPHTCfw54DTgt6p6ioi8Brig0guLSAPwLZxO5m3A/SLyS1V9tNJz14pqzjdj/AfW1dNfg7Z1DdsmvbOZl79MZADX8jvqq6sL9rx/QsHxjf+92zpnzaj5CQCHVXWXiKREJKWqq0TkqwFc+5XA46raDSAiNwJvBhIRAGyJw/jyasPWaavhieCv5XfU155/b4JU4eo5/UtTgT2JG8QYf+sEri1+AsAeEWkBfvCCT84AABEmSURBVAesEJGdwHMBXPtYIPeRy23Aq/J3EpElwBKAdDp+T0eOVpyXOEy6anaq+u2crlYT0GjvPXd1tM4zmwJMkQmTn1mj3gwcAC4Bfg38DWdd4KpQ1eWqOkdV5zQ1HVOty4YuzuP9TfV0dDgdxLls1JepFj9TQTwHICJHASsDvPaTwPE5r49ztyVC0sb7G2826stEyc96AO8XkR3AQ8ADwJ/c/yt1P3CiiJwgImOARcAvAzhvTbCSnzEman76AD4GnKSqTwd5YVXtF5EPAXfgDAO9TlUfCfIacRZmyc9rdFFY1zKVscEAJkp+AsDfgP0j7jUKqnobcFsY564FYYz398pQ1q1zpvJVHdpmmUxp1Rqia4MBTJT8BIDLgXtE5F7gSKu1qn44tFSZUfPKUGAo88+yTKa4apbKbTCAiZKfAPA/wN3Aw0A8Jh0xRZWTcVgm4y2IUrnfZjgbDBCM3r7eRC7pWCk/AaBJVS8NPSUmEOWsvGWZjLdKS+XlNMO1t1dvTYR61dvXy4ZdGxhU50PMDGTYsMupslkQKM1PALjdfRhrJcObgGxG0BgqNj1xbuYDQ5lMpW3dGzfC9u1Dr6dMgRkzKruHqFVaKi+nGW7XLpg5Mz4d9NkF5WXzvLKmvujqgj0X+3msKHjdu7uPZP5ZgzpI9+5uCwAj8BMAznf/vzxnmwJWRomhYqOLim2rpK07P/OHoddxDAJ+g12lU3KX2wxnk/9VJjPg/YEX226G+HkQ7IRqJMQEp1iGkr9t7drK2rrzM//c7XELAOV07FY6RLeWm+FqcSqIdEPaM7NPN8Tsw42hUiuCLVDVu0XkrV7vq+rPwkuWqYYwR6CsXRuPJo2sag63LLcZLmjZZpxSWsZ4Lzh85NiD45h/7+4gkxWajgkdw/oAAFKSomOCNVKMpFQNYB7O6B+veX8UsABQ4xoahhYuz99eqWwQicszB+UEu0qHgZbTDBfWZzL/hPLn5c8es2bLGjwWNIutbDu/jQIqX6kVwT7j/vhZVd2U+56IWLNQHRApb3u+KVOKNwPlisMzB+V07AZRW/DbDGeC0dbSZhn+KPjptr/ZY9tPg06IqT6vdWtLbc83Y4YTBPyI+pmDcuZesoezTFKU6gN4Ec6yj+Py+gGOwlkb2FQo6hXBgngIacaM4R2+2bb/Ss4ZhjjMuhn1921MvlJ9ADOBNwLjGd4PsA94X5iJSoI4TAJW6XDHap0zKFEOt+zthfXrhz8Itn79ULpqXZTPAZjRK9UH8AvgFyJyuqqurWKaEiEOk4CFUSqOQ0m7GL8l8GI1o8bG0Y9ueuyxwgfBVJ3tcfhsgjKazmcTHT8Pgp0rIo/grAr2a+ClwCWqekOoKatzcWlnDqNUXM45q9UsUk6Ny6sWI+L0jWT7R8qtsVXa31KMnyGf5WoZ08Kegb3lndvy/ZrkJwC8TlUvE5FzcZbFfivO+sAWACpQ7UnA4tj+XM1msHJqXF61mP7+wiGzcRjdBMGXum0x9+TwNRmc+/8/Ajep6l7xO07QFFXNtvI49Dd4qWYzWLk1rvxaTGdnecfnB9z8h8CygnjmwpjR8hMAVorIepwmoA+KyDHAwXCTVf+q2VYeh/4GL9VsBqv0obdyamxeAbdYmSluU2YU07Wjiz0H9vre3/oCaoOfuYA+ISJfBvaq6oCI7AfeHH7S6l+1RqXEpb8hXzWbwSp96K2cGptXwFV1OpEbGuLVDOfXngN7aVw2yNy5I+/beWYTXTu6rCmpBhTt5RGRy3JenqWqAwCq+hxgq4HVkGIZatRj88t5OKtSlXbCtrU50zZnP7N02nntlYEXC6z9/XD66TB/vvN/rWT+pn6VqgEsAr7s/nw5cFPOewuBT4aVKBOsuI7ND7MZLL8NvlgTUDlB0G+NLc6rfPXecxbdN7+XzK5JpCfupOO8a2k7466ok1UVnZtWe24fP3ZcYmsrpQKAFPnZ67WJsTiPzQ+jGcxvG3xYQTBOAXdYpvfw+aRu/RiDh5wH+TO72tlw/ccAaDvjrqIZZNkOtbAntTe48wUovxkrO3V1UpeULBUAtMjPXq9NzCVp0ZGo2+DjEHCzGRoo6cw0UquWcWD9q49k/lmDh5rpvvm9R2oBJdcD8NH+DxSdRrqry9/xYZrtcQ+Z/kxil5QsFQBeJiLP4pT2x7o/4762uYBMbJVqg/fTiRmESKedyF0jVyDTvBkWXgD7fwgPTy/YP7Nr0pGfR/Ng2fzVgwVTQXgFkjhMFbHGoyP7YP9BNK9Mm5QlJUtNBWEjlE1NinMbfDV4rZGLAGdfDg9fULB/euJOYHRDNzs3rXaaUeYNHDlH56bVTiAZLMxCohwe2rlpNf1LU3Tmpis1UJD5ZyVhSUk/zwEYU1Pi1AYfhaIZV+s2UmMODmsGSo05SMd51476WvNPmEfXDqdtJ9uRmrstV7U7WvPb9Wcd8yJ6+noK9jtw+EBil5S0AGDqThza4KNUdI3cxjQd7/qvwEcBeWXsUY+qGdYMxlC7/syJMwuadfL3heQsKWkBwNSlJHV65yu1Rm7b8XclYtinVzNYsXb9JC8pGUkAEJG3A1cCs4BXquoDUaTDmHoUVIZWy0MjizWDFdue1CUlo6oB/BVnVtH/iej6ZhTiOKOo8VZphlasCSV77rgr2gyWgHb9ckQSAFR1HYDNKlo7qjmjqAWa6JXThFKOatUqSjWDmSHWB2AKeGXA1ZpRNK5TV9czr0y53CYUv9epVq0iye365QgtAIjIb4F2j7eWustN+j3PEmAJQDo9NaDUmWKKZcD5mX9W0DOKxnXq6jirpFRdLFNukAYGtHDypEqaUMKqVRST1Hb9coQWAFT1tQGdZzmwHKC1dY5NQRGyYhlwMUE/XBXXqavjSysqVRfLlBtTjaRIBdqEEkatwlQm+mezTayUymjzu2xEgn+4Kq5TV8dZsVK1H8Uy3/7BfmZOnHmkxJ9uSHuOoS9HsdqDdcxGJ6phoOcC3wCOAX4lIl2q+g9RpMUMV2wahcbGwrnzvZY4rFTSn+INit9SdanRMkE3oVjHbPxENQroFuCWKK6dVH5H1hTLgItl9kG3zSf9Kd6g+C1Vl8qUgx6xYx2z8WOjgBKgnJE1xTLgdeu8zx1G23ySn+IdjZSMvq2+WKYMhDJixzpm48UCQAwFPQ6+3JE1XhlwNj35rG0+asLMiTMrKlV7Zcprt66t6ogdEw0LADETxjj4IEbW1FrbfJIeJgujVG0jdpLBRgHFTKnS+mgFMbKmnEXRo5YNotkAlw2ivb3RpquW2IidZLAaQMyEMQ4+qNJ7rbTN28NklbMRO8lgASBmwljNKmkja+xhssrZiJ1ksAAQM2G1tddK6T0IpZ5lWLu2/oJgWBOs2Yid+mcBIGaSVloPg1cQFXEeZMs+zFY/k8x5TwXxxJ4nSDcOrzZGvUqXiR8LADEURmk9rFExcRxt4xVE+/thIG9us3rpF/Aarnng8AEOZA4P2965aXWki7Kb+LEAkABhTbEc56mb84NoZ6f3fnXdL5AqnM3TmFwWABIgrFEx1R5tE8fahqkta7asKdg2d+rcCFISDxYAEiCsUTHVHG0T59pG1PKngkBh1robaNu5+Mimri7Yc3GyH/vp3LQaBhvgUMvQxua9dO3oSmz/SLJ/IxIirCmWqzl1c6UPyNXvNNNSMG0zMCzzN0Mav3CY+ffuPvKPwYaokxQpqwEkQFhDS8M6r1dTT6W1jVqbyqIc+cM1OzetjjA1ppZYAEiAsIaWhnHeYk09XusRgP8SvA2vNaaQBYCECOtBsKDPW6ypR8QpsVdSgk/Sw3DG+GF9ACZWijXpDAzUzmR0xtQKqwGYWCk1F5KV4I0JltUATKx0dDhNO7nqpbPWmLixGoCJFeusNaZ6LACY2LGmHmOqw5qAjDEmoSwAGGNMQlkAMMaYhLIAYIwxCWUBwBhjEsoCgDHGJJQFAGOMSahIAoCIfEVE1ovIQyJyi4iMjyIdxhiTZFHVAO4ETlLVlwIbgcsjSocxxiRWJAFAVX+jqtnZ3f8IHBdFOowxJsni0AfwbuD2Ym+KyBIReUBEHjh8+KkqJssYY+pbaHMBichvgXaPt5aq6i/cfZYC/cCKYudR1eXAcoDW1jkaQlKNMSaRQgsAqvraUu+LyLuANwJnqapl7MYYU2WRzAYqIguBy4B5qro/ijQYY0zSRdUH8E2gFbhTRLpE5DsRpcMYYxIrkhqAqr4wiusaY4wZEodRQMYYYyJgAcAYYxLKAoAxxiSUBQBjjEkoCwDGGJNQFgCMMSahLAAYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY4xJKAsAxhiTUBYAjDEmoSwAGGNMQlkAMMYk16GWqFMQKaml5XhF5Clgc9TpCMHRwNNRJyIE9XpfUL/3Vq/3BfV7b37ua5qqHpO/saYCQL0SkQdUdU7U6Qhavd4X1O+91et9Qf3eWyX3ZU1AxhiTUBYAjDEmoSwAxMPyqBMQknq9L6jfe6vX+4L6vbdR35f1ARhjTEJZDcAYYxLKAoAxxiSUBYCYEJGviMh6EXlIRG4RkfFRpykIIvJ2EXlERAZFpOaH4InIQhHZICKPi8gnok5PUETkOhHZKSJ/jTotQRKR40VklYg86v4eXhx1moIiIs0icp+I/MW9t6vKPYcFgPi4EzhJVV8KbAQujzg9Qfkr8Fbgd1EnpFIi0gB8C3g98GLgfBF5cbSpCsz1wMKoExGCfuCjqvpi4DTg3+roO8sAC1T1ZcBsYKGInFbOCSwAxISq/kZV+92XfwSOizI9QVHVdaq6Iep0BOSVwOOq2q2qh4AbgTdHnKZAqOrvgGeiTkfQVLVHVR90f94HrAOOjTZVwVBHn/uyyf1X1qgeCwDx9G7g9qgTYQocC2zNeb2NOslMkkBEpgOnAPdGm5LgiEiDiHQBO4E7VbWse2sMJ1nGi4j8Fmj3eGupqv7C3WcpTrV1RTXTVgk/92VMlESkBbgZ+IiqPht1eoKiqgPAbLfP8BYROUlVfffjWACoIlV9ban3ReRdwBuBs7SGHtAY6b7qyJPA8Tmvj3O3mRgTkSaczH+Fqv4s6vSEQVX3iMgqnH4c3wHAmoBiQkQWApcBb1LV/VGnx3i6HzhRRE4QkTHAIuCXEafJlCAiAnwPWKeqV0edniCJyDHZ0YIiMhY4G1hfzjksAMTHN4FW4E4R6RKR70SdoCCIyLkisg04HfiViNwRdZpGy+2k/xBwB05n4k9U9ZFoUxUMEfkRsBaYKSLbROQ9UacpIK8GLgQWuH9XXSLyhqgTFZDJwCoReQincHKnqt5azglsKghjjEkoqwEYY0xCWQAwxpiEsgBgjDEJZQHAGGMSygKAMcYklAUAY3wSkbeIiIrIi6JOizFBsABgjH/nA2vc/42peRYAjPHBnUtmLvAenCeAEZGUiHzbXcfhThG5TUTe5r73ChFZLSJ/EpE7RGRyhMk3xpMFAGP8eTPwa1XdCOwSkVfgrHMwHWdtgAtxnnbOzj3zDeBtqvoK4DpgWRSJNqYUmwzOGH/OB77m/nyj+7oRuElVB4Ed7mRcADOBk3Cm9QBoAHqqm1xjRmYBwJgRiMgLgAXAySKiOBm6ArcUOwR4RFVPr1ISjRkVawIyZmRvA36oqtNUdbqqHg9swllB6zy3L6ANmO/uvwE4RkSONAmJyEuiSLgxpVgAMGZk51NY2r8ZZxGcbcCjwA3Ag8Bed7nItwFfEpG/AF3AGdVLrjH+2GygxlRARFpUtU9EJgL3Aa9W1R1Rp8sYP6wPwJjK3OouyjEG+E/L/E0tsRqAMcYklPUBGGNMQlkAMMaYhLIAYIwxCWUBwBhjEsoCgDHGJNT/B2MsCBSDEbAAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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('Random Forest 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
}