MLC Project - Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>NTA_name</th>\n",
       "      <th>Asthma Hospitalizations (Adults) by NTA</th>\n",
       "      <th>Children under 5 years old in Poverty</th>\n",
       "      <th>Children under 6 years with elevated blood lead levels by NTA</th>\n",
       "      <th>Foreign-Born</th>\n",
       "      <th>High School Graduation</th>\n",
       "      <th>Limited English</th>\n",
       "      <th>Owner-Occupied Homes</th>\n",
       "      <th>Percent Asian Alone</th>\n",
       "      <th>...</th>\n",
       "      <th>Number of persons 25+ yrs with master's degree</th>\n",
       "      <th>Number of persons 25+ yrs with professional school degree</th>\n",
       "      <th>Number of persons 25+ yrs with doctorate degree</th>\n",
       "      <th>Workers 16+ years in management, business, science,arts occupations</th>\n",
       "      <th>Workers 16 years and over in service occupations</th>\n",
       "      <th>Workers 16 years and over in sales and office occupations</th>\n",
       "      <th>Workers 16+ years in natural resources, construction, maint occupation</th>\n",
       "      <th>Workers 16 years and over in production, transport, material moving</th>\n",
       "      <th>Workers 16 years and over in armed forces</th>\n",
       "      <th>tree_dbh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Allerton-Pelham Gardens</td>\n",
       "      <td>24.1</td>\n",
       "      <td>13.706</td>\n",
       "      <td>20.7</td>\n",
       "      <td>35.260</td>\n",
       "      <td>78.422</td>\n",
       "      <td>15.358</td>\n",
       "      <td>56.914</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.653463</td>\n",
       "      <td>0.949253</td>\n",
       "      <td>1.237634</td>\n",
       "      <td>13.935395</td>\n",
       "      <td>10.360676</td>\n",
       "      <td>10.709136</td>\n",
       "      <td>3.460568</td>\n",
       "      <td>3.595746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Eltingville</td>\n",
       "      <td>9.1</td>\n",
       "      <td>12.394</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13.776</td>\n",
       "      <td>91.584</td>\n",
       "      <td>6.436</td>\n",
       "      <td>80.726</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.349466</td>\n",
       "      <td>1.722369</td>\n",
       "      <td>0.747052</td>\n",
       "      <td>21.799380</td>\n",
       "      <td>6.661210</td>\n",
       "      <td>10.655861</td>\n",
       "      <td>4.952675</td>\n",
       "      <td>2.611222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.632</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15.156</td>\n",
       "      <td>91.614</td>\n",
       "      <td>6.574</td>\n",
       "      <td>87.522</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.126636</td>\n",
       "      <td>0.754000</td>\n",
       "      <td>0.348294</td>\n",
       "      <td>20.702457</td>\n",
       "      <td>7.383073</td>\n",
       "      <td>11.309995</td>\n",
       "      <td>3.655176</td>\n",
       "      <td>3.888648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>13.8</td>\n",
       "      <td>15.506</td>\n",
       "      <td>25.4</td>\n",
       "      <td>42.560</td>\n",
       "      <td>84.144</td>\n",
       "      <td>26.334</td>\n",
       "      <td>15.276</td>\n",
       "      <td>15.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.254995</td>\n",
       "      <td>1.703561</td>\n",
       "      <td>1.381475</td>\n",
       "      <td>28.444418</td>\n",
       "      <td>11.113890</td>\n",
       "      <td>11.248415</td>\n",
       "      <td>3.161353</td>\n",
       "      <td>3.930995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Auburndale</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.374</td>\n",
       "      <td>5.4</td>\n",
       "      <td>47.530</td>\n",
       "      <td>85.116</td>\n",
       "      <td>36.186</td>\n",
       "      <td>68.084</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.944284</td>\n",
       "      <td>1.008646</td>\n",
       "      <td>1.176753</td>\n",
       "      <td>18.986551</td>\n",
       "      <td>8.659942</td>\n",
       "      <td>12.420749</td>\n",
       "      <td>4.111431</td>\n",
       "      <td>3.770413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>Windsor Terrace</td>\n",
       "      <td>12.2</td>\n",
       "      <td>7.222</td>\n",
       "      <td>24.3</td>\n",
       "      <td>22.130</td>\n",
       "      <td>90.972</td>\n",
       "      <td>12.928</td>\n",
       "      <td>42.460</td>\n",
       "      <td>11.193333</td>\n",
       "      <td>...</td>\n",
       "      <td>18.820617</td>\n",
       "      <td>3.938780</td>\n",
       "      <td>1.778078</td>\n",
       "      <td>36.394328</td>\n",
       "      <td>5.572811</td>\n",
       "      <td>8.854378</td>\n",
       "      <td>1.093856</td>\n",
       "      <td>2.363268</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>Woodhaven</td>\n",
       "      <td>13.1</td>\n",
       "      <td>20.202</td>\n",
       "      <td>25.5</td>\n",
       "      <td>47.442</td>\n",
       "      <td>76.268</td>\n",
       "      <td>28.784</td>\n",
       "      <td>51.268</td>\n",
       "      <td>23.710000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.918009</td>\n",
       "      <td>0.674005</td>\n",
       "      <td>0.273463</td>\n",
       "      <td>11.815203</td>\n",
       "      <td>13.898347</td>\n",
       "      <td>11.274712</td>\n",
       "      <td>4.463879</td>\n",
       "      <td>6.217258</td>\n",
       "      <td>0.020912</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>Woodlawn-Wakefield</td>\n",
       "      <td>28.6</td>\n",
       "      <td>21.162</td>\n",
       "      <td>25.4</td>\n",
       "      <td>42.000</td>\n",
       "      <td>82.792</td>\n",
       "      <td>7.640</td>\n",
       "      <td>46.608</td>\n",
       "      <td>3.670000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.181218</td>\n",
       "      <td>0.322266</td>\n",
       "      <td>0.313188</td>\n",
       "      <td>16.592152</td>\n",
       "      <td>12.645530</td>\n",
       "      <td>10.142296</td>\n",
       "      <td>3.456415</td>\n",
       "      <td>4.577537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>Woodside</td>\n",
       "      <td>10.4</td>\n",
       "      <td>20.604</td>\n",
       "      <td>26.6</td>\n",
       "      <td>58.000</td>\n",
       "      <td>78.168</td>\n",
       "      <td>40.624</td>\n",
       "      <td>32.634</td>\n",
       "      <td>38.970000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.991805</td>\n",
       "      <td>1.120330</td>\n",
       "      <td>0.414130</td>\n",
       "      <td>15.462300</td>\n",
       "      <td>11.545503</td>\n",
       "      <td>10.209390</td>\n",
       "      <td>3.177901</td>\n",
       "      <td>9.110856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>Yorkville</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1.118</td>\n",
       "      <td>9.1</td>\n",
       "      <td>20.488</td>\n",
       "      <td>96.502</td>\n",
       "      <td>5.792</td>\n",
       "      <td>26.380</td>\n",
       "      <td>8.630000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.741701</td>\n",
       "      <td>9.949343</td>\n",
       "      <td>3.850883</td>\n",
       "      <td>44.858112</td>\n",
       "      <td>4.206216</td>\n",
       "      <td>13.944081</td>\n",
       "      <td>0.695913</td>\n",
       "      <td>0.631978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                    NTA_name  \\\n",
       "0             0                     Allerton-Pelham Gardens   \n",
       "1             1  Annadale-Huguenot-Prince's Bay-Eltingville   \n",
       "2             2                               Arden Heights   \n",
       "3             3                                     Astoria   \n",
       "4             4                                  Auburndale   \n",
       "..          ...                                         ...   \n",
       "174         174                             Windsor Terrace   \n",
       "175         175                                   Woodhaven   \n",
       "176         176                          Woodlawn-Wakefield   \n",
       "177         177                                    Woodside   \n",
       "178         178                                   Yorkville   \n",
       "\n",
       "     Asthma Hospitalizations (Adults) by NTA  \\\n",
       "0                                       24.1   \n",
       "1                                        9.1   \n",
       "2                                       15.5   \n",
       "3                                       13.8   \n",
       "4                                        5.3   \n",
       "..                                       ...   \n",
       "174                                     12.2   \n",
       "175                                     13.1   \n",
       "176                                     28.6   \n",
       "177                                     10.4   \n",
       "178                                      6.4   \n",
       "\n",
       "     Children under 5 years old in Poverty  \\\n",
       "0                                   13.706   \n",
       "1                                   12.394   \n",
       "2                                    3.632   \n",
       "3                                   15.506   \n",
       "4                                    3.374   \n",
       "..                                     ...   \n",
       "174                                  7.222   \n",
       "175                                 20.202   \n",
       "176                                 21.162   \n",
       "177                                 20.604   \n",
       "178                                  1.118   \n",
       "\n",
       "     Children under 6 years with elevated blood lead levels by NTA  \\\n",
       "0                                                 20.7               \n",
       "1                                                  6.8               \n",
       "2                                                  6.3               \n",
       "3                                                 25.4               \n",
       "4                                                  5.4               \n",
       "..                                                 ...               \n",
       "174                                               24.3               \n",
       "175                                               25.5               \n",
       "176                                               25.4               \n",
       "177                                               26.6               \n",
       "178                                                9.1               \n",
       "\n",
       "     Foreign-Born  High School Graduation  Limited English  \\\n",
       "0          35.260                  78.422           15.358   \n",
       "1          13.776                  91.584            6.436   \n",
       "2          15.156                  91.614            6.574   \n",
       "3          42.560                  84.144           26.334   \n",
       "4          47.530                  85.116           36.186   \n",
       "..            ...                     ...              ...   \n",
       "174        22.130                  90.972           12.928   \n",
       "175        47.442                  76.268           28.784   \n",
       "176        42.000                  82.792            7.640   \n",
       "177        58.000                  78.168           40.624   \n",
       "178        20.488                  96.502            5.792   \n",
       "\n",
       "     Owner-Occupied Homes  Percent Asian Alone  ...  \\\n",
       "0                  56.914             8.400000  ...   \n",
       "1                  80.726             3.190000  ...   \n",
       "2                  87.522             6.290000  ...   \n",
       "3                  15.276            15.350000  ...   \n",
       "4                  68.084            44.750000  ...   \n",
       "..                    ...                  ...  ...   \n",
       "174                42.460            11.193333  ...   \n",
       "175                51.268            23.710000  ...   \n",
       "176                46.608             3.670000  ...   \n",
       "177                32.634            38.970000  ...   \n",
       "178                26.380             8.630000  ...   \n",
       "\n",
       "     Number of persons 25+ yrs with master's degree  \\\n",
       "0                                          5.653463   \n",
       "1                                          7.349466   \n",
       "2                                          7.126636   \n",
       "3                                         10.254995   \n",
       "4                                          7.944284   \n",
       "..                                              ...   \n",
       "174                                       18.820617   \n",
       "175                                        2.918009   \n",
       "176                                        5.181218   \n",
       "177                                        5.991805   \n",
       "178                                       18.741701   \n",
       "\n",
       "     Number of persons 25+ yrs with professional school degree  \\\n",
       "0                                             0.949253           \n",
       "1                                             1.722369           \n",
       "2                                             0.754000           \n",
       "3                                             1.703561           \n",
       "4                                             1.008646           \n",
       "..                                                 ...           \n",
       "174                                           3.938780           \n",
       "175                                           0.674005           \n",
       "176                                           0.322266           \n",
       "177                                           1.120330           \n",
       "178                                           9.949343           \n",
       "\n",
       "     Number of persons 25+ yrs with doctorate degree  \\\n",
       "0                                           1.237634   \n",
       "1                                           0.747052   \n",
       "2                                           0.348294   \n",
       "3                                           1.381475   \n",
       "4                                           1.176753   \n",
       "..                                               ...   \n",
       "174                                         1.778078   \n",
       "175                                         0.273463   \n",
       "176                                         0.313188   \n",
       "177                                         0.414130   \n",
       "178                                         3.850883   \n",
       "\n",
       "     Workers 16+ years in management, business, science,arts occupations  \\\n",
       "0                                            13.935395                     \n",
       "1                                            21.799380                     \n",
       "2                                            20.702457                     \n",
       "3                                            28.444418                     \n",
       "4                                            18.986551                     \n",
       "..                                                 ...                     \n",
       "174                                          36.394328                     \n",
       "175                                          11.815203                     \n",
       "176                                          16.592152                     \n",
       "177                                          15.462300                     \n",
       "178                                          44.858112                     \n",
       "\n",
       "     Workers 16 years and over in service occupations  \\\n",
       "0                                           10.360676   \n",
       "1                                            6.661210   \n",
       "2                                            7.383073   \n",
       "3                                           11.113890   \n",
       "4                                            8.659942   \n",
       "..                                                ...   \n",
       "174                                          5.572811   \n",
       "175                                         13.898347   \n",
       "176                                         12.645530   \n",
       "177                                         11.545503   \n",
       "178                                          4.206216   \n",
       "\n",
       "     Workers 16 years and over in sales and office occupations  \\\n",
       "0                                            10.709136           \n",
       "1                                            10.655861           \n",
       "2                                            11.309995           \n",
       "3                                            11.248415           \n",
       "4                                            12.420749           \n",
       "..                                                 ...           \n",
       "174                                           8.854378           \n",
       "175                                          11.274712           \n",
       "176                                          10.142296           \n",
       "177                                          10.209390           \n",
       "178                                          13.944081           \n",
       "\n",
       "     Workers 16+ years in natural resources, construction, maint occupation  \\\n",
       "0                                             3.460568                        \n",
       "1                                             4.952675                        \n",
       "2                                             3.655176                        \n",
       "3                                             3.161353                        \n",
       "4                                             4.111431                        \n",
       "..                                                 ...                        \n",
       "174                                           1.093856                        \n",
       "175                                           4.463879                        \n",
       "176                                           3.456415                        \n",
       "177                                           3.177901                        \n",
       "178                                           0.695913                        \n",
       "\n",
       "     Workers 16 years and over in production, transport, material moving  \\\n",
       "0                                             3.595746                     \n",
       "1                                             2.611222                     \n",
       "2                                             3.888648                     \n",
       "3                                             3.930995                     \n",
       "4                                             3.770413                     \n",
       "..                                                 ...                     \n",
       "174                                           2.363268                     \n",
       "175                                           6.217258                     \n",
       "176                                           4.577537                     \n",
       "177                                           9.110856                     \n",
       "178                                           0.631978                     \n",
       "\n",
       "     Workers 16 years and over in armed forces  tree_dbh  \n",
       "0                                     0.000000        11  \n",
       "1                                     0.000000        44  \n",
       "2                                     0.000000        26  \n",
       "3                                     0.000000         5  \n",
       "4                                     0.000000        25  \n",
       "..                                         ...       ...  \n",
       "174                                   0.000000        10  \n",
       "175                                   0.020912         6  \n",
       "176                                   0.000000         9  \n",
       "177                                   0.000000         5  \n",
       "178                                   0.000000         2  \n",
       "\n",
       "[179 rows x 64 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data=pd.read_csv(\"DataInput.csv\")\n",
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data.drop(columns=['Unnamed: 0'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179, 63)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clmns = list(Data.columns)\n",
    "clmns.remove('NTA_name') \n",
    "Data[clmns] = Data[clmns].apply(pd.to_numeric, errors='coerce')\n",
    "Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>NTA_name</th>\n",
       "      <th>Asthma Hospitalizations (Adults) by NTA</th>\n",
       "      <th>Children under 5 years old in Poverty</th>\n",
       "      <th>Children under 6 years with elevated blood lead levels by NTA</th>\n",
       "      <th>Foreign-Born</th>\n",
       "      <th>High School Graduation</th>\n",
       "      <th>Limited English</th>\n",
       "      <th>Owner-Occupied Homes</th>\n",
       "      <th>Percent Asian Alone</th>\n",
       "      <th>Percent Black Alone</th>\n",
       "      <th>...</th>\n",
       "      <th>Number of persons 25+ yrs with master's degree</th>\n",
       "      <th>Number of persons 25+ yrs with professional school degree</th>\n",
       "      <th>Number of persons 25+ yrs with doctorate degree</th>\n",
       "      <th>Workers 16+ years in management, business, science,arts occupations</th>\n",
       "      <th>Workers 16 years and over in service occupations</th>\n",
       "      <th>Workers 16 years and over in sales and office occupations</th>\n",
       "      <th>Workers 16+ years in natural resources, construction, maint occupation</th>\n",
       "      <th>Workers 16 years and over in production, transport, material moving</th>\n",
       "      <th>Workers 16 years and over in armed forces</th>\n",
       "      <th>tree_dbh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Allerton-Pelham Gardens</td>\n",
       "      <td>24.1</td>\n",
       "      <td>13.706</td>\n",
       "      <td>20.7</td>\n",
       "      <td>35.260</td>\n",
       "      <td>78.422</td>\n",
       "      <td>15.358</td>\n",
       "      <td>56.914</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>32.406667</td>\n",
       "      <td>...</td>\n",
       "      <td>5.653463</td>\n",
       "      <td>0.949253</td>\n",
       "      <td>1.237634</td>\n",
       "      <td>13.935395</td>\n",
       "      <td>10.360676</td>\n",
       "      <td>10.709136</td>\n",
       "      <td>3.460568</td>\n",
       "      <td>3.595746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Eltingville</td>\n",
       "      <td>9.1</td>\n",
       "      <td>12.394</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13.776</td>\n",
       "      <td>91.584</td>\n",
       "      <td>6.436</td>\n",
       "      <td>80.726</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.349466</td>\n",
       "      <td>1.722369</td>\n",
       "      <td>0.747052</td>\n",
       "      <td>21.799380</td>\n",
       "      <td>6.661210</td>\n",
       "      <td>10.655861</td>\n",
       "      <td>4.952675</td>\n",
       "      <td>2.611222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3.632</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15.156</td>\n",
       "      <td>91.614</td>\n",
       "      <td>6.574</td>\n",
       "      <td>87.522</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>1.603333</td>\n",
       "      <td>...</td>\n",
       "      <td>7.126636</td>\n",
       "      <td>0.754000</td>\n",
       "      <td>0.348294</td>\n",
       "      <td>20.702457</td>\n",
       "      <td>7.383073</td>\n",
       "      <td>11.309995</td>\n",
       "      <td>3.655176</td>\n",
       "      <td>3.888648</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>13.8</td>\n",
       "      <td>15.506</td>\n",
       "      <td>25.4</td>\n",
       "      <td>42.560</td>\n",
       "      <td>84.144</td>\n",
       "      <td>26.334</td>\n",
       "      <td>15.276</td>\n",
       "      <td>15.350000</td>\n",
       "      <td>4.183333</td>\n",
       "      <td>...</td>\n",
       "      <td>10.254995</td>\n",
       "      <td>1.703561</td>\n",
       "      <td>1.381475</td>\n",
       "      <td>28.444418</td>\n",
       "      <td>11.113890</td>\n",
       "      <td>11.248415</td>\n",
       "      <td>3.161353</td>\n",
       "      <td>3.930995</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Auburndale</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.374</td>\n",
       "      <td>5.4</td>\n",
       "      <td>47.530</td>\n",
       "      <td>85.116</td>\n",
       "      <td>36.186</td>\n",
       "      <td>68.084</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.944284</td>\n",
       "      <td>1.008646</td>\n",
       "      <td>1.176753</td>\n",
       "      <td>18.986551</td>\n",
       "      <td>8.659942</td>\n",
       "      <td>12.420749</td>\n",
       "      <td>4.111431</td>\n",
       "      <td>3.770413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>Windsor Terrace</td>\n",
       "      <td>12.2</td>\n",
       "      <td>7.222</td>\n",
       "      <td>24.3</td>\n",
       "      <td>22.130</td>\n",
       "      <td>90.972</td>\n",
       "      <td>12.928</td>\n",
       "      <td>42.460</td>\n",
       "      <td>11.193333</td>\n",
       "      <td>5.036667</td>\n",
       "      <td>...</td>\n",
       "      <td>18.820617</td>\n",
       "      <td>3.938780</td>\n",
       "      <td>1.778078</td>\n",
       "      <td>36.394328</td>\n",
       "      <td>5.572811</td>\n",
       "      <td>8.854378</td>\n",
       "      <td>1.093856</td>\n",
       "      <td>2.363268</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>Woodhaven</td>\n",
       "      <td>13.1</td>\n",
       "      <td>20.202</td>\n",
       "      <td>25.5</td>\n",
       "      <td>47.442</td>\n",
       "      <td>76.268</td>\n",
       "      <td>28.784</td>\n",
       "      <td>51.268</td>\n",
       "      <td>23.710000</td>\n",
       "      <td>4.173333</td>\n",
       "      <td>...</td>\n",
       "      <td>2.918009</td>\n",
       "      <td>0.674005</td>\n",
       "      <td>0.273463</td>\n",
       "      <td>11.815203</td>\n",
       "      <td>13.898347</td>\n",
       "      <td>11.274712</td>\n",
       "      <td>4.463879</td>\n",
       "      <td>6.217258</td>\n",
       "      <td>0.020912</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>Woodlawn-Wakefield</td>\n",
       "      <td>28.6</td>\n",
       "      <td>21.162</td>\n",
       "      <td>25.4</td>\n",
       "      <td>42.000</td>\n",
       "      <td>82.792</td>\n",
       "      <td>7.640</td>\n",
       "      <td>46.608</td>\n",
       "      <td>3.670000</td>\n",
       "      <td>59.723333</td>\n",
       "      <td>...</td>\n",
       "      <td>5.181218</td>\n",
       "      <td>0.322266</td>\n",
       "      <td>0.313188</td>\n",
       "      <td>16.592152</td>\n",
       "      <td>12.645530</td>\n",
       "      <td>10.142296</td>\n",
       "      <td>3.456415</td>\n",
       "      <td>4.577537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>Woodside</td>\n",
       "      <td>10.4</td>\n",
       "      <td>20.604</td>\n",
       "      <td>26.6</td>\n",
       "      <td>58.000</td>\n",
       "      <td>78.168</td>\n",
       "      <td>40.624</td>\n",
       "      <td>32.634</td>\n",
       "      <td>38.970000</td>\n",
       "      <td>1.176667</td>\n",
       "      <td>...</td>\n",
       "      <td>5.991805</td>\n",
       "      <td>1.120330</td>\n",
       "      <td>0.414130</td>\n",
       "      <td>15.462300</td>\n",
       "      <td>11.545503</td>\n",
       "      <td>10.209390</td>\n",
       "      <td>3.177901</td>\n",
       "      <td>9.110856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>Yorkville</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1.118</td>\n",
       "      <td>9.1</td>\n",
       "      <td>20.488</td>\n",
       "      <td>96.502</td>\n",
       "      <td>5.792</td>\n",
       "      <td>26.380</td>\n",
       "      <td>8.630000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.741701</td>\n",
       "      <td>9.949343</td>\n",
       "      <td>3.850883</td>\n",
       "      <td>44.858112</td>\n",
       "      <td>4.206216</td>\n",
       "      <td>13.944081</td>\n",
       "      <td>0.695913</td>\n",
       "      <td>0.631978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       NTA_name  \\\n",
       "0                       Allerton-Pelham Gardens   \n",
       "1    Annadale-Huguenot-Prince's Bay-Eltingville   \n",
       "2                                 Arden Heights   \n",
       "3                                       Astoria   \n",
       "4                                    Auburndale   \n",
       "..                                          ...   \n",
       "174                             Windsor Terrace   \n",
       "175                                   Woodhaven   \n",
       "176                          Woodlawn-Wakefield   \n",
       "177                                    Woodside   \n",
       "178                                   Yorkville   \n",
       "\n",
       "     Asthma Hospitalizations (Adults) by NTA  \\\n",
       "0                                       24.1   \n",
       "1                                        9.1   \n",
       "2                                       15.5   \n",
       "3                                       13.8   \n",
       "4                                        5.3   \n",
       "..                                       ...   \n",
       "174                                     12.2   \n",
       "175                                     13.1   \n",
       "176                                     28.6   \n",
       "177                                     10.4   \n",
       "178                                      6.4   \n",
       "\n",
       "     Children under 5 years old in Poverty  \\\n",
       "0                                   13.706   \n",
       "1                                   12.394   \n",
       "2                                    3.632   \n",
       "3                                   15.506   \n",
       "4                                    3.374   \n",
       "..                                     ...   \n",
       "174                                  7.222   \n",
       "175                                 20.202   \n",
       "176                                 21.162   \n",
       "177                                 20.604   \n",
       "178                                  1.118   \n",
       "\n",
       "     Children under 6 years with elevated blood lead levels by NTA  \\\n",
       "0                                                 20.7               \n",
       "1                                                  6.8               \n",
       "2                                                  6.3               \n",
       "3                                                 25.4               \n",
       "4                                                  5.4               \n",
       "..                                                 ...               \n",
       "174                                               24.3               \n",
       "175                                               25.5               \n",
       "176                                               25.4               \n",
       "177                                               26.6               \n",
       "178                                                9.1               \n",
       "\n",
       "     Foreign-Born  High School Graduation  Limited English  \\\n",
       "0          35.260                  78.422           15.358   \n",
       "1          13.776                  91.584            6.436   \n",
       "2          15.156                  91.614            6.574   \n",
       "3          42.560                  84.144           26.334   \n",
       "4          47.530                  85.116           36.186   \n",
       "..            ...                     ...              ...   \n",
       "174        22.130                  90.972           12.928   \n",
       "175        47.442                  76.268           28.784   \n",
       "176        42.000                  82.792            7.640   \n",
       "177        58.000                  78.168           40.624   \n",
       "178        20.488                  96.502            5.792   \n",
       "\n",
       "     Owner-Occupied Homes  Percent Asian Alone  Percent Black Alone  ...  \\\n",
       "0                  56.914             8.400000            32.406667  ...   \n",
       "1                  80.726             3.190000             0.560000  ...   \n",
       "2                  87.522             6.290000             1.603333  ...   \n",
       "3                  15.276            15.350000             4.183333  ...   \n",
       "4                  68.084            44.750000             1.150000  ...   \n",
       "..                    ...                  ...                  ...  ...   \n",
       "174                42.460            11.193333             5.036667  ...   \n",
       "175                51.268            23.710000             4.173333  ...   \n",
       "176                46.608             3.670000            59.723333  ...   \n",
       "177                32.634            38.970000             1.176667  ...   \n",
       "178                26.380             8.630000             2.900000  ...   \n",
       "\n",
       "     Number of persons 25+ yrs with master's degree  \\\n",
       "0                                          5.653463   \n",
       "1                                          7.349466   \n",
       "2                                          7.126636   \n",
       "3                                         10.254995   \n",
       "4                                          7.944284   \n",
       "..                                              ...   \n",
       "174                                       18.820617   \n",
       "175                                        2.918009   \n",
       "176                                        5.181218   \n",
       "177                                        5.991805   \n",
       "178                                       18.741701   \n",
       "\n",
       "     Number of persons 25+ yrs with professional school degree  \\\n",
       "0                                             0.949253           \n",
       "1                                             1.722369           \n",
       "2                                             0.754000           \n",
       "3                                             1.703561           \n",
       "4                                             1.008646           \n",
       "..                                                 ...           \n",
       "174                                           3.938780           \n",
       "175                                           0.674005           \n",
       "176                                           0.322266           \n",
       "177                                           1.120330           \n",
       "178                                           9.949343           \n",
       "\n",
       "     Number of persons 25+ yrs with doctorate degree  \\\n",
       "0                                           1.237634   \n",
       "1                                           0.747052   \n",
       "2                                           0.348294   \n",
       "3                                           1.381475   \n",
       "4                                           1.176753   \n",
       "..                                               ...   \n",
       "174                                         1.778078   \n",
       "175                                         0.273463   \n",
       "176                                         0.313188   \n",
       "177                                         0.414130   \n",
       "178                                         3.850883   \n",
       "\n",
       "     Workers 16+ years in management, business, science,arts occupations  \\\n",
       "0                                            13.935395                     \n",
       "1                                            21.799380                     \n",
       "2                                            20.702457                     \n",
       "3                                            28.444418                     \n",
       "4                                            18.986551                     \n",
       "..                                                 ...                     \n",
       "174                                          36.394328                     \n",
       "175                                          11.815203                     \n",
       "176                                          16.592152                     \n",
       "177                                          15.462300                     \n",
       "178                                          44.858112                     \n",
       "\n",
       "     Workers 16 years and over in service occupations  \\\n",
       "0                                           10.360676   \n",
       "1                                            6.661210   \n",
       "2                                            7.383073   \n",
       "3                                           11.113890   \n",
       "4                                            8.659942   \n",
       "..                                                ...   \n",
       "174                                          5.572811   \n",
       "175                                         13.898347   \n",
       "176                                         12.645530   \n",
       "177                                         11.545503   \n",
       "178                                          4.206216   \n",
       "\n",
       "     Workers 16 years and over in sales and office occupations  \\\n",
       "0                                            10.709136           \n",
       "1                                            10.655861           \n",
       "2                                            11.309995           \n",
       "3                                            11.248415           \n",
       "4                                            12.420749           \n",
       "..                                                 ...           \n",
       "174                                           8.854378           \n",
       "175                                          11.274712           \n",
       "176                                          10.142296           \n",
       "177                                          10.209390           \n",
       "178                                          13.944081           \n",
       "\n",
       "     Workers 16+ years in natural resources, construction, maint occupation  \\\n",
       "0                                             3.460568                        \n",
       "1                                             4.952675                        \n",
       "2                                             3.655176                        \n",
       "3                                             3.161353                        \n",
       "4                                             4.111431                        \n",
       "..                                                 ...                        \n",
       "174                                           1.093856                        \n",
       "175                                           4.463879                        \n",
       "176                                           3.456415                        \n",
       "177                                           3.177901                        \n",
       "178                                           0.695913                        \n",
       "\n",
       "     Workers 16 years and over in production, transport, material moving  \\\n",
       "0                                             3.595746                     \n",
       "1                                             2.611222                     \n",
       "2                                             3.888648                     \n",
       "3                                             3.930995                     \n",
       "4                                             3.770413                     \n",
       "..                                                 ...                     \n",
       "174                                           2.363268                     \n",
       "175                                           6.217258                     \n",
       "176                                           4.577537                     \n",
       "177                                           9.110856                     \n",
       "178                                           0.631978                     \n",
       "\n",
       "     Workers 16 years and over in armed forces  tree_dbh  \n",
       "0                                     0.000000        11  \n",
       "1                                     0.000000        44  \n",
       "2                                     0.000000        26  \n",
       "3                                     0.000000         5  \n",
       "4                                     0.000000        25  \n",
       "..                                         ...       ...  \n",
       "174                                   0.000000        10  \n",
       "175                                   0.020912         6  \n",
       "176                                   0.000000         9  \n",
       "177                                   0.000000         5  \n",
       "178                                   0.000000         2  \n",
       "\n",
       "[179 rows x 63 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Data.iloc[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = Data.iloc[:, [1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 61)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data=Data.dropna()\n",
    "# Split data into 70% train, 30% test\n",
    "X_train,X_test,y_train,y_test=train_test_split(x, y, test_size=0.3, random_state=999)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 61)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr = DecisionTreeRegressor(max_depth=3)\n",
    "dr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>variables</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Number of persons 25+ yrs in 11th grade</td>\n",
       "      <td>0.694879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Families with income &lt; $10,000</td>\n",
       "      <td>0.199821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Families with income between $125,000-$149,999</td>\n",
       "      <td>0.0451447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Percent Black Alone</td>\n",
       "      <td>0.0439314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Percent Hispanic Alone</td>\n",
       "      <td>0.00843204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         variables  importance\n",
       "43         Number of persons 25+ yrs in 11th grade    0.694879\n",
       "24                  Families with income < $10,000    0.199821\n",
       "37  Families with income between $125,000-$149,999   0.0451447\n",
       "7                              Percent Black Alone   0.0439314\n",
       "8                           Percent Hispanic Alone  0.00843204"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_importance=pd.DataFrame([list(X_train.columns),list(dr.feature_importances_)]).T\n",
    "Feature_importance.columns=[\"variables\",\"importance\"]\n",
    "# list the top 5 most important features in order\n",
    "Feature_importance.sort_values(by=\"importance\",ascending=False).iloc[:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_import = ['Number of persons 25+ yrs in 11th grade','Percent Black Alone','tree_dbh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Number of persons 25+ yrs in 11th grade</th>\n",
       "      <th>Percent Black Alone</th>\n",
       "      <th>tree_dbh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.120004</td>\n",
       "      <td>4.076667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>1.984973</td>\n",
       "      <td>3.216667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1.409507</td>\n",
       "      <td>9.126667</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Number of persons 25+ yrs in 11th grade  Percent Black Alone  tree_dbh\n",
       "89                                  0.120004             4.076667         2\n",
       "155                                 1.984973             3.216667         5\n",
       "139                                 1.409507             9.126667        10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_only_import_var = X_train[only_import]\n",
    "X_test_only_import_var = X_test[only_import]\n",
    "X_train_only_import_var.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In sample accuracy: 0.8169742871272855\n",
      "Out of sample accuracy: 0.8547772063765305\n"
     ]
    }
   ],
   "source": [
    "dr.fit(X_train_only_import_var, y_train)\n",
    "print('In sample accuracy:',dr.score(X_train_only_import_var,y_train))\n",
    "print('Out of sample accuracy:',dr.score(X_test_only_import_var,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;\n",
      "edge [fontname=helvetica] ;\n",
      "0 [label=\"Number of persons 25+ yrs in 11th grade <= 1.511, samples = 125, value = 23.663\", fillcolor=\"#f8ddc9\"] ;\n",
      "1 [label=\"Percent Black Alone <= 7.975, samples = 77, value = 13.251\", fillcolor=\"#fcf1e9\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"Number of persons 25+ yrs in 11th grade <= 0.397, samples = 52, value = 10.321\", fillcolor=\"#fdf7f2\"] ;\n",
      "1 -> 2 ;\n",
      "3 [label=\"samples = 11, value = 6.3\", fillcolor=\"#ffffff\"] ;\n",
      "2 -> 3 ;\n",
      "4 [label=\"samples = 41, value = 11.4\", fillcolor=\"#fdf5ef\"] ;\n",
      "2 -> 4 ;\n",
      "5 [label=\"tree_dbh <= 5.5, samples = 25, value = 19.344\", fillcolor=\"#fae5d6\"] ;\n",
      "1 -> 5 ;\n",
      "6 [label=\"samples = 6, value = 23.333\", fillcolor=\"#f8ddca\"] ;\n",
      "5 -> 6 ;\n",
      "7 [label=\"samples = 19, value = 18.084\", fillcolor=\"#fae8da\"] ;\n",
      "5 -> 7 ;\n",
      "8 [label=\"Number of persons 25+ yrs in 11th grade <= 2.645, samples = 48, value = 40.367\", fillcolor=\"#f1bc95\"] ;\n",
      "0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "9 [label=\"Percent Black Alone <= 7.88, samples = 29, value = 33.062\", fillcolor=\"#f4caac\"] ;\n",
      "8 -> 9 ;\n",
      "10 [label=\"samples = 4, value = 20.55\", fillcolor=\"#f9e3d3\"] ;\n",
      "9 -> 10 ;\n",
      "11 [label=\"samples = 25, value = 35.064\", fillcolor=\"#f3c6a6\"] ;\n",
      "9 -> 11 ;\n",
      "12 [label=\"Number of persons 25+ yrs in 11th grade <= 3.909, samples = 19, value = 51.516\", fillcolor=\"#eda572\"] ;\n",
      "8 -> 12 ;\n",
      "13 [label=\"samples = 17, value = 49.347\", fillcolor=\"#edaa79\"] ;\n",
      "12 -> 13 ;\n",
      "14 [label=\"samples = 2, value = 69.95\", fillcolor=\"#e58139\"] ;\n",
      "12 -> 14 ;\n",
      "}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import pydotplus\n",
    "from IPython.display import Image  \n",
    "print(tree.export_graphviz(dr,out_file=None,\n",
    "                         feature_names=X_train_only_import_var.columns.values,  \n",
    "                         class_names=['no buy','buy'],  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True,impurity=False).replace(\"<br/>\",\", \").replace(\"&le;\",\"<=\").replace(\"=<\",\"=\\\"\").replace(\">,\",\"\\\",\"))\n",
    "\n",
    "thestring = tree.export_graphviz(dr, out_file=None,  \n",
    "                         feature_names=X_train_only_import_var.columns.values, \n",
    "                         class_names=['no buy','buy'],  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True,impurity=False)\n",
    "graph = pydotplus.graph_from_dot_data(thestring)  \n",
    "graph.write_png('DecisionTree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}