Simi_ML_Decision Tree_fake data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffde29f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c624c6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('fake_decision tree_.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e23b98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf588e2a",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>isAdmitted</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adam Smith</td>\n",
       "      <td>28</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>1</td>\n",
       "      <td>University of California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica Brown</td>\n",
       "      <td>35</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>0</td>\n",
       "      <td>Harvard University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Johnson</td>\n",
       "      <td>42</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>1</td>\n",
       "      <td>University of California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rachel Lee</td>\n",
       "      <td>29</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>0</td>\n",
       "      <td>Harvard University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andrew Chen</td>\n",
       "      <td>36</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>1</td>\n",
       "      <td>University of Michigan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Age                 Job isAdmitted                University\n",
       "0     Adam Smith   28  Software Developer          1  University of California\n",
       "1  Jessica Brown   35  Software Developer          0        Harvard University\n",
       "2   Ryan Johnson   42  Software Developer          1  University of California\n",
       "3     Rachel Lee   29  Software Developer          0        Harvard University\n",
       "4    Andrew Chen   36  Software Developer          1    University of Michigan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d25ace2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Age', 'Job', 'isAdmitted', 'University'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b671b851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='None'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('isAdmitted').size().plot(kind='pie', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69651336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAN2ElEQVR4nO3df4wc913G8efhzlUtr+VQ7K5cJ+JShCqFGNp6FZBC0V2EKpNEpUIINSpVEaATEqkCuEJGlaD9A9FfRvkHAaZECTTtFtFaqmwKtcDXtGqbcpc4ubhuSRpOIk5kKwLcXBSBrv3wx87Fm8vO7dzOze6H6/slrW53Zr7e577++rnZud3EESEAQF4/NOkAAIDNUdQAkBxFDQDJUdQAkBxFDQDJTTfxh+7fvz9mZmaa+KNre/HFF7Vnz55JxyhFvnrIVw/56qmTb2lp6fmIODBwZ0Rs++3IkSOR1blz5yYdYVPkq4d89ZCvnjr5JC1GSady6QMAkqOoASA5ihoAkqOoASA5ihoAkqOoASC5Su+jtr0i6QVJ35O0FhGdJkMBAK7Zygde5iLi+caSAAAG4tIHACTnqPA/DrD975L+S1JI+suIODngmHlJ85LUbrePdLvdbY66PVZXV9VqtV61ffnS1Qmk6Tl8aN/L98vyZUG+eshXz07ONzc3t1R2WblqUb8hIp61/XpJZyW9LyIeKju+0+nE4uLiSGGbtrCwoNnZ2Vdtnzl+ZvxhCisfvuPl+2X5siBfPeSrZyfns11a1JUufUTEs8XXK5JOSbplpCQAgC0bWtS299jeu35f0tslPdF0MABAT5V3fbQlnbK9fvynIuIfG00FAHjZ0KKOiKcl/dQYsgAABuDteQCQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlR1ACQHEUNAMlVLmrbU7YftX26yUAAgFfayhn1PZIuNhUEADBYpaK2fb2kOyR9otk4AICNHBHDD7L/XtKfSNor6f0RceeAY+YlzUtSu90+0u12Rwq0fOnqSOOqau+WLr/U6FPUMs58hw/t2/KY1dVVtVqtBtJsD/LVQ7566uSbm5tbiojOoH3TwwbbvlPSlYhYsj1bdlxEnJR0UpI6nU7MzpYeuqlfO35mpHFVHTu8phPLQ7/tiRlnvpV3z255zMLCgkb9ux0H8tVDvnqaylfl0setkt5he0VSV9Jttj+57UkAAAMNLeqI+IOIuD4iZiS9S9K/RMSvNp4MACCJ91EDQHpbuhgaEQuSFhpJAgAYiDNqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5ChqAEiOogaA5IYWte3X2v6G7cdsX7D9oXEEAwD0TFc45n8k3RYRq7Z3SfqK7S9ExNcbzgYAUIWijoiQtFo83FXcoslQAIBrKl2jtj1l+7ykK5LORsTDjaYCALzMvRPmigfb10k6Jel9EfHEhn3zkuYlqd1uH+l2uyMFWr50daRxVbV3S5dfavQpavlByXf40L76f8gAq6urarVapfubXl9l1r/fYfkmbTvzNTHXVddfU+trmDrzNzc3txQRnUH7tlTUkmT7jyS9GBEfLzum0+nE4uLi1lIWZo6fGWlcVccOr+nEcpVL85Pxg5Jv5cN3bEOaV1tYWNDs7Gzp/qbXV5n173dYvknbznxNzHXV9dfU+hqmzvzZLi3qKu/6OFCcScv2bkk/L+lbIyUBAGxZlVOjg5IesD2lXrH/XUScbjYWAGBdlXd9PC7pLWPIAgAYgE8mAkByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByFDUAJEdRA0ByQ4va9g22z9m+aPuC7XvGEQwA0DNd4Zg1Scci4hHbeyUt2T4bEd9sOBsAQBXOqCPiuYh4pLj/gqSLkg41HQwA0OOIqH6wPSPpIUk3R8R3N+yblzQvSe12+0i32x0p0PKlqyONq6q9W7r8UqNPUQv56iFfPeSr58Z9U2q1WiONnZubW4qIzqB9lYvadkvSlyT9cUR8brNjO51OLC4ubjmoJM0cPzPSuKqOHV7TieUqV3wmg3z1kK8e8tVz/9E9mp2dHWms7dKirvSuD9u7JH1W0oPDShoAsL2qvOvDkv5a0sWI+NPmIwEA+lU5o75V0nsk3Wb7fHG7veFcAIDC0Is9EfEVSR5DFgDAAHwyEQCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCSo6gBILmhRW37PttXbD8xjkAAgFeqckZ9v6SjDecAAJQYWtQR8ZCk/xxDFgDAAI6I4QfZM5JOR8TNmxwzL2lektrt9pFutztSoOVLV0caV1V7t3T5pUafohby1UO+eshXz437ptRqtUYaOzc3txQRnUH7tq2o+3U6nVhcXNxSyHUzx8+MNK6qY4fXdGJ5utHnqIN89ZCvHvLVc//RPZqdnR1prO3SouZdHwCQHEUNAMlVeXvepyV9TdKbbD9j+zeajwUAWDf0Yk9E3DWOIACAwbj0AQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJUdQAkBxFDQDJVSpq20dtf9v2U7aPNx0KAHDN0KK2PSXpzyT9gqSbJN1l+6amgwEAeqqcUd8i6amIeDoi/ldSV9IvNhsLALDOEbH5AfYvSzoaEb9ZPH6PpJ+OiLs3HDcvab54+CZJ397+uNtiv6TnJx1iE+Srh3z1kK+eOvl+NCIODNoxXWGwB2x7VbtHxElJJ7cYbOxsL0ZEZ9I5ypCvHvLVQ756mspX5dLHM5Ju6Ht8vaRntzsIAGCwKkX9r5J+3PaNtl8j6V2SPt9sLADAuqGXPiJizfbdkv5J0pSk+yLiQuPJmpP98gz56iFfPeSrp5F8Q3+ZCACYLD6ZCADJUdQAkNyOKGrbN9g+Z/ui7Qu27ym2f8b2+eK2Yvt8yfgV28vFcYsN5Hut7W/YfqzI96Fi++tsn7X9ZPH1h0vGN/oR/k3yfcz2t2w/bvuU7etKxjc6f0MyftD2pb6/59tLxk9qDlOsweI5pmw/avt08TjF+tskX5r1V5JvfGsvIv7f3yQdlPTW4v5eSf8m6aYNx5yQ9Icl41ck7W8wnyW1ivu7JD0s6WckfVTS8WL7cUkfGTB2StJ3JL1R0mskPbbxe2sw39slTRfbPzIo3zjmb0jGD0p6/5CxE5vDLGuweI7fk/QpSaeLxynW3yb50qy/knxjW3s74ow6Ip6LiEeK+y9Iuijp0Pp+25b0K5I+PaF8ERGrxcNdxS3U+yj+A8X2ByS9c8Dwxj/CX5YvIr4YEWvF9q+r9x76idhkDquY2Byu75/0GrR9vaQ7JH2ib3OK9VeWL9P6K5m/KrZl/nZEUfezPSPpLeqd0ax7m6TLEfFkybCQ9EXbS+59FL6JXFPFy94rks5GxMOS2hHxnNT7YSPp9QOGHpL0H32Pn1HfD6GG8/X7dUlfKBne+PwNyXh38fL4vpKX7xnmcNJr8F5Jvy/p+33b0qy/knz9Jr3+7tXgfGNZezuqqG23JH1W0u9ExHf7dt2lzc9kbo2It6r3Xwj8bds/t93ZIuJ7EfFm9c4KbrF9c8WhlT7CX9dm+Wx/QNKapAdLhjc+f5tk/HNJPybpzZKeU+/ywkYTn0NNcA3avlPSlYhYGmX4gG3bOnfD8k16/W2Sb2xrb8cUte1d6pX0gxHxub7t05J+SdJnysZGxLPF1yuSTqn3cqUREfHfkhYkHZV02fbBIudB9c7ENhrrR/g35JPt90q6U9K7o7joNmDM2OZvY8aIuFwU5Pcl/VXJc096Die9Bm+V9A7bK+q99L7N9ieVZ/2V5cuy/gbmG+vaa/Li+7hu6v3U+htJ9w7Yd1TSlzYZu0fS3r77X1WvALYz3wFJ1xX3d0v6snqL72N65S9zPjpg7LSkpyXdqGu/jPiJMeU7Kumbkg5Mcv6GZDzYd8zvSupmmsMsa7DvuWZ17ZdhKdbfJvnSrL+SfGNbe418M+O+SfpZ9V5OPC7pfHG7vdh3v6Tf2nD8GyT9Q3H/jcXkPSbpgqQPNJDvJyU9WuR7QsVv/iX9iKR/lvRk8fV1G/MVj29X750s3xlzvqfUu762Pqd/MYn5G5LxbyUtF9s/v/6PJ8scZlmDfc/bXzQp1t8m+dKsv5J8Y1t7fIQcAJLbMdeoAWCnoqgBIDmKGgCSo6gBIDmKGgCSo6gBIDmKGgCS+z8db630X8TeQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5471483",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.iloc[1:11,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea01e291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35    1\n",
      "42    1\n",
      "29    1\n",
      "36    1\n",
      "31    1\n",
      "45    1\n",
      "33    1\n",
      "26    1\n",
      "39    1\n",
      "27    1\n",
      "Name: Age, dtype: int64\n",
      "Software Developer            4\n",
      "Human Resources Specialist    4\n",
      "Software Engineer             1\n",
      "Product Manager               1\n",
      "Name: Job, dtype: int64\n",
      "Harvard University                       3\n",
      "University of California                 3\n",
      "University of Michigan                   2\n",
      "Massachusetts Institute of Technology    1\n",
      "University of Southern California        1\n",
      "Name: University, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "col_names = ['Age','Job','University']\n",
    "for col in col_names:\n",
    "    print(df[col].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0b39bc1",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>isAdmitted</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica Brown</td>\n",
       "      <td>35</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>0</td>\n",
       "      <td>Harvard University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Johnson</td>\n",
       "      <td>42</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>1</td>\n",
       "      <td>University of California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rachel Lee</td>\n",
       "      <td>29</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>0</td>\n",
       "      <td>Harvard University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andrew Chen</td>\n",
       "      <td>36</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>1</td>\n",
       "      <td>University of Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Emily Davis</td>\n",
       "      <td>31</td>\n",
       "      <td>Human Resources Specialist</td>\n",
       "      <td>0</td>\n",
       "      <td>University of California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jacob Garcia</td>\n",
       "      <td>45</td>\n",
       "      <td>Human Resources Specialist</td>\n",
       "      <td>1</td>\n",
       "      <td>Harvard University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Olivia Wilson</td>\n",
       "      <td>33</td>\n",
       "      <td>Human Resources Specialist</td>\n",
       "      <td>0</td>\n",
       "      <td>University of Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michael Nguyen</td>\n",
       "      <td>26</td>\n",
       "      <td>Human Resources Specialist</td>\n",
       "      <td>1</td>\n",
       "      <td>University of California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sophia Kim</td>\n",
       "      <td>39</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>0</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daniel Hernandez</td>\n",
       "      <td>27</td>\n",
       "      <td>Product Manager</td>\n",
       "      <td>1</td>\n",
       "      <td>University of Southern California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Age                         Job isAdmitted  \\\n",
       "1      Jessica Brown   35          Software Developer          0   \n",
       "2       Ryan Johnson   42          Software Developer          1   \n",
       "3         Rachel Lee   29          Software Developer          0   \n",
       "4        Andrew Chen   36          Software Developer          1   \n",
       "5        Emily Davis   31  Human Resources Specialist          0   \n",
       "6       Jacob Garcia   45  Human Resources Specialist          1   \n",
       "7      Olivia Wilson   33  Human Resources Specialist          0   \n",
       "8     Michael Nguyen   26  Human Resources Specialist          1   \n",
       "9         Sophia Kim   39           Software Engineer          0   \n",
       "10  Daniel Hernandez   27             Product Manager          1   \n",
       "\n",
       "                               University  \n",
       "1                      Harvard University  \n",
       "2                University of California  \n",
       "3                      Harvard University  \n",
       "4                  University of Michigan  \n",
       "5                University of California  \n",
       "6                      Harvard University  \n",
       "7                  University of Michigan  \n",
       "8                University of California  \n",
       "9   Massachusetts Institute of Technology  \n",
       "10      University of Southern California  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c367672",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Job']=df['Job'].map({'Software Developer':1,'Human Resources Specialist':2,'Product Manager':3}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47392016",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['isAdmitted']=df['isAdmitted'].map({'False':0,'True':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5459375",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['University']=df['University'].map({'Harvard University':1,'University of California':2,'University of Michigan':3,\n",
    "                                      'Massachusetts Institute of Technology':4,'University of Southern California':5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c7b6d6b7",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>isAdmitted</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica Brown</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Johnson</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rachel Lee</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andrew Chen</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Emily Davis</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jacob Garcia</td>\n",
       "      <td>45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Olivia Wilson</td>\n",
       "      <td>33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michael Nguyen</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sophia Kim</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daniel Hernandez</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Age  Job isAdmitted  University\n",
       "1      Jessica Brown   35  1.0          0           1\n",
       "2       Ryan Johnson   42  1.0          1           2\n",
       "3         Rachel Lee   29  1.0          0           1\n",
       "4        Andrew Chen   36  1.0          1           3\n",
       "5        Emily Davis   31  2.0          0           2\n",
       "6       Jacob Garcia   45  2.0          1           1\n",
       "7      Olivia Wilson   33  2.0          0           3\n",
       "8     Michael Nguyen   26  2.0          1           2\n",
       "9         Sophia Kim   39  NaN          0           4\n",
       "10  Daniel Hernandez   27  3.0          1           5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5eb6a7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5948e2fd",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>isAdmitted</th>\n",
       "      <th>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica Brown</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Johnson</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rachel Lee</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andrew Chen</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Emily Davis</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jacob Garcia</td>\n",
       "      <td>45</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Olivia Wilson</td>\n",
       "      <td>33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michael Nguyen</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Daniel Hernandez</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Age  Job isAdmitted  University\n",
       "1      Jessica Brown   35  1.0          0           1\n",
       "2       Ryan Johnson   42  1.0          1           2\n",
       "3         Rachel Lee   29  1.0          0           1\n",
       "4        Andrew Chen   36  1.0          1           3\n",
       "5        Emily Davis   31  2.0          0           2\n",
       "6       Jacob Garcia   45  2.0          1           1\n",
       "7      Olivia Wilson   33  2.0          0           3\n",
       "8     Michael Nguyen   26  2.0          1           2\n",
       "10  Daniel Hernandez   27  3.0          1           5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e573ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name          0\n",
       "Age           0\n",
       "Job           0\n",
       "isAdmitted    0\n",
       "University    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d5c5328a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['isAdmitted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39cc5212",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[['Job','Age','University']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bb731fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "499e33d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "decisiontree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5af91122",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=decisiontree.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64664774",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4c1ea41a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rules:str = tree.export_text(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "811f3736",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('fake.txt','w')  as f:\n",
    "    f.write(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14816ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rules = rules.replace('feature_1 ','Age')\n",
    "rules = rules.replace('class: 0', 'rejected')\n",
    "rules = rules.replace('class: 1', 'approved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a916ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "419f451e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'|--- Age <= 35.50\\n|   |--- Age <= 28.00\\n|   |   |--- class: 1\\n|   |--- Age >  28.00\\n|   |   |--- class: 0\\n|--- Age >  35.50\\n|   |--- class: 1\\n'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_rules = export_text(decisiontree, feature_names=list(x.columns))\n",
    "tree_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "093433f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lineage(tree, feature_names):\n",
    "     left      = tree.tree_.children_left\n",
    "     right     = tree.tree_.children_right\n",
    "     threshold = tree.tree_.threshold\n",
    "     features  = [feature_names[i] for i in tree.tree_.feature]\n",
    "\n",
    "     # get ids of child nodes\n",
    "     idx = np.argwhere(left == -1)[:,0]     \n",
    "\n",
    "     def recurse(left, right, child, lineage=None):          \n",
    "          if lineage is None:\n",
    "               lineage = [child]\n",
    "          if child in left:\n",
    "               parent = np.where(left == child)[0].item()\n",
    "               split = 'l'\n",
    "          else:\n",
    "               parent = np.where(right == child)[0].item()\n",
    "               split = 'r'\n",
    "\n",
    "          lineage.append((parent, split, threshold[parent], features[parent]))\n",
    "\n",
    "          if parent == 0:\n",
    "               lineage.reverse()\n",
    "               return lineage\n",
    "          else:\n",
    "               return recurse(left, right, parent, lineage)\n",
    "\n",
    "     for child in idx:\n",
    "          for node in recurse(left, right, child):\n",
    "               print(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "69e730df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'l', 35.5, 'Age')\n",
      "(1, 'l', 28.0, 'Age')\n",
      "2\n",
      "(0, 'l', 35.5, 'Age')\n",
      "(1, 'r', 28.0, 'Age')\n",
      "3\n",
      "(0, 'r', 35.5, 'Age')\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "get_lineage(decisiontree, df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "248a43ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as pltimage\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "tree.plot_tree(decisiontree, feature_names=['Job','Age','University'],class_names=['yes','no'],filled=True)\n",
    "fig.savefig('tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f810d59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0818bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}