4 - Models for midterm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook performs the following tasks:\n",
    "\n",
    "4.1 [Classify issued or not](#4.1)\n",
    "\n",
    "\n",
    "4.2 [Regressions](#4.2)\n",
    "\n",
    "Nick did tasks 4.1-4.2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data tools\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# ML tools\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load cleaned training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset has 159120 rows and 45 columns\n"
     ]
    }
   ],
   "source": [
    "data_train = pd.read_csv('data_train_clean1.csv')\n",
    "nrows_train, ncols_train = data_train.shape\n",
    "print(f\"Training dataset has {nrows_train} rows and {ncols_train} columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Permit Type</th>\n",
       "      <th>Street Number</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Number of Existing Stories</th>\n",
       "      <th>Number of Proposed Stories</th>\n",
       "      <th>Estimated Cost</th>\n",
       "      <th>Revised Cost</th>\n",
       "      <th>Existing Units</th>\n",
       "      <th>Proposed Units</th>\n",
       "      <th>Plansets</th>\n",
       "      <th>...</th>\n",
       "      <th>Proposed Construction Type</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Record ID</th>\n",
       "      <th>Filed Days</th>\n",
       "      <th>Issued Days</th>\n",
       "      <th>Issued or not</th>\n",
       "      <th>Days to issue</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159120.000000</td>\n",
       "      <td>159120.000000</td>\n",
       "      <td>23571.000000</td>\n",
       "      <td>124975.000000</td>\n",
       "      <td>124912.000000</td>\n",
       "      <td>1.287550e+05</td>\n",
       "      <td>1.543180e+05</td>\n",
       "      <td>117952.000000</td>\n",
       "      <td>118451.000000</td>\n",
       "      <td>159120.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>159120.000000</td>\n",
       "      <td>157760.000000</td>\n",
       "      <td>157760.000000</td>\n",
       "      <td>1.591200e+05</td>\n",
       "      <td>159120.000000</td>\n",
       "      <td>147226.000000</td>\n",
       "      <td>159120.000000</td>\n",
       "      <td>147226.000000</td>\n",
       "      <td>157772.000000</td>\n",
       "      <td>157772.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.523033</td>\n",
       "      <td>1121.984615</td>\n",
       "      <td>78.061941</td>\n",
       "      <td>5.704909</td>\n",
       "      <td>5.748119</td>\n",
       "      <td>1.776544e+05</td>\n",
       "      <td>1.396427e+05</td>\n",
       "      <td>15.552236</td>\n",
       "      <td>16.443947</td>\n",
       "      <td>1.740284</td>\n",
       "      <td>...</td>\n",
       "      <td>3.202545</td>\n",
       "      <td>5.541557</td>\n",
       "      <td>94115.507042</td>\n",
       "      <td>1.162524e+12</td>\n",
       "      <td>968.921437</td>\n",
       "      <td>973.138236</td>\n",
       "      <td>0.925251</td>\n",
       "      <td>26.202980</td>\n",
       "      <td>37.768730</td>\n",
       "      <td>-122.431815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.456435</td>\n",
       "      <td>1135.655030</td>\n",
       "      <td>323.361038</td>\n",
       "      <td>8.592910</td>\n",
       "      <td>8.598126</td>\n",
       "      <td>3.920941e+06</td>\n",
       "      <td>3.867726e+06</td>\n",
       "      <td>73.233051</td>\n",
       "      <td>74.035060</td>\n",
       "      <td>1.397578</td>\n",
       "      <td>...</td>\n",
       "      <td>2.188812</td>\n",
       "      <td>2.885742</td>\n",
       "      <td>9.284674</td>\n",
       "      <td>4.912923e+11</td>\n",
       "      <td>539.029506</td>\n",
       "      <td>536.279454</td>\n",
       "      <td>0.262986</td>\n",
       "      <td>91.586112</td>\n",
       "      <td>0.023721</td>\n",
       "      <td>0.028581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94102.000000</td>\n",
       "      <td>1.293532e+10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.708170</td>\n",
       "      <td>-122.510938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.311000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>94109.000000</td>\n",
       "      <td>1.308561e+12</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.752209</td>\n",
       "      <td>-122.447303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.100000e+04</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>94114.000000</td>\n",
       "      <td>1.371854e+12</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>986.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.773412</td>\n",
       "      <td>-122.427700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>1700.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.560000e+04</td>\n",
       "      <td>2.900000e+04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94122.000000</td>\n",
       "      <td>1.434867e+12</td>\n",
       "      <td>1434.000000</td>\n",
       "      <td>1431.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>37.788802</td>\n",
       "      <td>-122.409631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8400.000000</td>\n",
       "      <td>6004.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>5.379586e+08</td>\n",
       "      <td>7.805000e+08</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>1911.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>94158.000000</td>\n",
       "      <td>1.498342e+12</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1734.000000</td>\n",
       "      <td>37.825017</td>\n",
       "      <td>-122.365853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Permit Type  Street Number          Unit  Number of Existing Stories  \\\n",
       "count  159120.000000  159120.000000  23571.000000               124975.000000   \n",
       "mean        7.523033    1121.984615     78.061941                    5.704909   \n",
       "std         1.456435    1135.655030    323.361038                    8.592910   \n",
       "min         1.000000       0.000000      0.000000                    0.000000   \n",
       "25%         8.000000     235.000000      0.000000                    2.000000   \n",
       "50%         8.000000     711.000000      0.000000                    3.000000   \n",
       "75%         8.000000    1700.000000      1.000000                    4.000000   \n",
       "max         8.000000    8400.000000   6004.000000                   78.000000   \n",
       "\n",
       "       Number of Proposed Stories  Estimated Cost  Revised Cost  \\\n",
       "count               124912.000000    1.287550e+05  1.543180e+05   \n",
       "mean                     5.748119    1.776544e+05  1.396427e+05   \n",
       "std                      8.598126    3.920941e+06  3.867726e+06   \n",
       "min                      0.000000    1.000000e+00  0.000000e+00   \n",
       "25%                      2.000000    3.311000e+03  1.000000e+00   \n",
       "50%                      3.000000    1.100000e+04  7.000000e+03   \n",
       "75%                      4.000000    3.560000e+04  2.900000e+04   \n",
       "max                     78.000000    5.379586e+08  7.805000e+08   \n",
       "\n",
       "       Existing Units  Proposed Units       Plansets  ...  \\\n",
       "count   117952.000000   118451.000000  159120.000000  ...   \n",
       "mean        15.552236       16.443947       1.740284  ...   \n",
       "std         73.233051       74.035060       1.397578  ...   \n",
       "min          0.000000        0.000000       0.000000  ...   \n",
       "25%          1.000000        1.000000       0.000000  ...   \n",
       "50%          1.000000        2.000000       2.000000  ...   \n",
       "75%          4.000000        4.000000       2.000000  ...   \n",
       "max       1907.000000     1911.000000       4.000000  ...   \n",
       "\n",
       "       Proposed Construction Type  Supervisor District        Zipcode  \\\n",
       "count               159120.000000        157760.000000  157760.000000   \n",
       "mean                     3.202545             5.541557   94115.507042   \n",
       "std                      2.188812             2.885742       9.284674   \n",
       "min                      0.000000             1.000000   94102.000000   \n",
       "25%                      1.000000             3.000000   94109.000000   \n",
       "50%                      5.000000             6.000000   94114.000000   \n",
       "75%                      5.000000             8.000000   94122.000000   \n",
       "max                      5.000000            11.000000   94158.000000   \n",
       "\n",
       "          Record ID     Filed Days    Issued Days  Issued or not  \\\n",
       "count  1.591200e+05  159120.000000  147226.000000  159120.000000   \n",
       "mean   1.162524e+12     968.921437     973.138236       0.925251   \n",
       "std    4.912923e+11     539.029506     536.279454       0.262986   \n",
       "min    1.293532e+10       1.000000       1.000000       0.000000   \n",
       "25%    1.308561e+12     512.000000     520.000000       1.000000   \n",
       "50%    1.371854e+12     980.000000     986.000000       1.000000   \n",
       "75%    1.434867e+12    1434.000000    1431.000000       1.000000   \n",
       "max    1.498342e+12    1879.000000    1879.000000       1.000000   \n",
       "\n",
       "       Days to issue       Latitude      Longitude  \n",
       "count  147226.000000  157772.000000  157772.000000  \n",
       "mean       26.202980      37.768730    -122.431815  \n",
       "std        91.586112       0.023721       0.028581  \n",
       "min         0.000000      37.708170    -122.510938  \n",
       "25%         0.000000      37.752209    -122.447303  \n",
       "50%         0.000000      37.773412    -122.427700  \n",
       "75%         6.000000      37.788802    -122.409631  \n",
       "max      1734.000000      37.825017    -122.365853  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load cleaned testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing dataset has 39780 rows and 45 columns\n"
     ]
    }
   ],
   "source": [
    "data_test = pd.read_csv('data_test_clean1.csv')\n",
    "nrows_test, ncols_test = data_test.shape\n",
    "print(f\"Testing dataset has {nrows_test} rows and {ncols_test} columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Permit Type</th>\n",
       "      <th>Street Number</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Number of Existing Stories</th>\n",
       "      <th>Number of Proposed Stories</th>\n",
       "      <th>Estimated Cost</th>\n",
       "      <th>Revised Cost</th>\n",
       "      <th>Existing Units</th>\n",
       "      <th>Proposed Units</th>\n",
       "      <th>Plansets</th>\n",
       "      <th>...</th>\n",
       "      <th>Proposed Construction Type</th>\n",
       "      <th>Supervisor District</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Record ID</th>\n",
       "      <th>Filed Days</th>\n",
       "      <th>Issued Days</th>\n",
       "      <th>Issued or not</th>\n",
       "      <th>Days to issue</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39780.000000</td>\n",
       "      <td>39780.000000</td>\n",
       "      <td>5908.000000</td>\n",
       "      <td>31141.000000</td>\n",
       "      <td>31120.000000</td>\n",
       "      <td>3.207900e+04</td>\n",
       "      <td>3.851600e+04</td>\n",
       "      <td>29410.000000</td>\n",
       "      <td>29538.000000</td>\n",
       "      <td>39780.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>39780.000000</td>\n",
       "      <td>39423.000000</td>\n",
       "      <td>39424.000000</td>\n",
       "      <td>3.978000e+04</td>\n",
       "      <td>39780.000000</td>\n",
       "      <td>36734.000000</td>\n",
       "      <td>39780.000000</td>\n",
       "      <td>36734.000000</td>\n",
       "      <td>39428.000000</td>\n",
       "      <td>39428.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.519482</td>\n",
       "      <td>1120.706259</td>\n",
       "      <td>80.333446</td>\n",
       "      <td>5.709242</td>\n",
       "      <td>5.732696</td>\n",
       "      <td>1.340407e+05</td>\n",
       "      <td>1.056654e+05</td>\n",
       "      <td>16.123087</td>\n",
       "      <td>16.779640</td>\n",
       "      <td>1.741001</td>\n",
       "      <td>...</td>\n",
       "      <td>3.200251</td>\n",
       "      <td>5.525784</td>\n",
       "      <td>94115.474609</td>\n",
       "      <td>1.160142e+12</td>\n",
       "      <td>976.296254</td>\n",
       "      <td>980.376218</td>\n",
       "      <td>0.923429</td>\n",
       "      <td>25.460391</td>\n",
       "      <td>37.768836</td>\n",
       "      <td>-122.431798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.461523</td>\n",
       "      <td>1136.238212</td>\n",
       "      <td>341.065014</td>\n",
       "      <td>8.695553</td>\n",
       "      <td>8.673987</td>\n",
       "      <td>2.090947e+06</td>\n",
       "      <td>2.098990e+06</td>\n",
       "      <td>79.266563</td>\n",
       "      <td>79.798033</td>\n",
       "      <td>1.402760</td>\n",
       "      <td>...</td>\n",
       "      <td>2.191576</td>\n",
       "      <td>2.892233</td>\n",
       "      <td>9.211779</td>\n",
       "      <td>4.939345e+11</td>\n",
       "      <td>539.360158</td>\n",
       "      <td>536.428025</td>\n",
       "      <td>0.265913</td>\n",
       "      <td>88.927710</td>\n",
       "      <td>0.023767</td>\n",
       "      <td>0.028455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94102.000000</td>\n",
       "      <td>1.301155e+10</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.708174</td>\n",
       "      <td>-122.510729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.150000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>94109.000000</td>\n",
       "      <td>1.308592e+12</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.752256</td>\n",
       "      <td>-122.447067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>709.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.100000e+04</td>\n",
       "      <td>7.000000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>94114.000000</td>\n",
       "      <td>1.371799e+12</td>\n",
       "      <td>989.000000</td>\n",
       "      <td>996.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.773569</td>\n",
       "      <td>-122.427546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>1690.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000e+04</td>\n",
       "      <td>2.785800e+04</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>94122.000000</td>\n",
       "      <td>1.435691e+12</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>1443.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>37.788879</td>\n",
       "      <td>-122.409770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8400.000000</td>\n",
       "      <td>5903.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.750000e+08</td>\n",
       "      <td>2.076800e+08</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>1907.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>94158.000000</td>\n",
       "      <td>1.498331e+12</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1740.000000</td>\n",
       "      <td>37.823350</td>\n",
       "      <td>-122.365853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Permit Type  Street Number         Unit  Number of Existing Stories  \\\n",
       "count  39780.000000   39780.000000  5908.000000                31141.000000   \n",
       "mean       7.519482    1120.706259    80.333446                    5.709242   \n",
       "std        1.461523    1136.238212   341.065014                    8.695553   \n",
       "min        1.000000       1.000000     0.000000                    0.000000   \n",
       "25%        8.000000     235.000000     0.000000                    2.000000   \n",
       "50%        8.000000     709.500000     0.000000                    3.000000   \n",
       "75%        8.000000    1690.000000     1.000000                    4.000000   \n",
       "max        8.000000    8400.000000  5903.000000                   63.000000   \n",
       "\n",
       "       Number of Proposed Stories  Estimated Cost  Revised Cost  \\\n",
       "count                31120.000000    3.207900e+04  3.851600e+04   \n",
       "mean                     5.732696    1.340407e+05  1.056654e+05   \n",
       "std                      8.673987    2.090947e+06  2.098990e+06   \n",
       "min                      0.000000    1.000000e+00  0.000000e+00   \n",
       "25%                      2.000000    3.150000e+03  1.000000e+00   \n",
       "50%                      3.000000    1.100000e+04  7.000000e+03   \n",
       "75%                      4.000000    3.500000e+04  2.785800e+04   \n",
       "max                     63.000000    1.750000e+08  2.076800e+08   \n",
       "\n",
       "       Existing Units  Proposed Units      Plansets  ...  \\\n",
       "count    29410.000000    29538.000000  39780.000000  ...   \n",
       "mean        16.123087       16.779640      1.741001  ...   \n",
       "std         79.266563       79.798033      1.402760  ...   \n",
       "min          0.000000        0.000000      0.000000  ...   \n",
       "25%          1.000000        1.000000      0.000000  ...   \n",
       "50%          1.000000        2.000000      2.000000  ...   \n",
       "75%          4.000000        4.000000      2.000000  ...   \n",
       "max       1907.000000     1907.000000      4.000000  ...   \n",
       "\n",
       "       Proposed Construction Type  Supervisor District       Zipcode  \\\n",
       "count                39780.000000         39423.000000  39424.000000   \n",
       "mean                     3.200251             5.525784  94115.474609   \n",
       "std                      2.191576             2.892233      9.211779   \n",
       "min                      0.000000             1.000000  94102.000000   \n",
       "25%                      1.000000             3.000000  94109.000000   \n",
       "50%                      5.000000             6.000000  94114.000000   \n",
       "75%                      5.000000             8.000000  94122.000000   \n",
       "max                      5.000000            11.000000  94158.000000   \n",
       "\n",
       "          Record ID    Filed Days   Issued Days  Issued or not  Days to issue  \\\n",
       "count  3.978000e+04  39780.000000  36734.000000   39780.000000   36734.000000   \n",
       "mean   1.160142e+12    976.296254    980.376218       0.923429      25.460391   \n",
       "std    4.939345e+11    539.360158    536.428025       0.265913      88.927710   \n",
       "min    1.301155e+10      1.000000      1.000000       0.000000       0.000000   \n",
       "25%    1.308592e+12    521.000000    527.000000       1.000000       0.000000   \n",
       "50%    1.371799e+12    989.000000    996.000000       1.000000       0.000000   \n",
       "75%    1.435691e+12   1443.000000   1443.000000       1.000000       6.000000   \n",
       "max    1.498331e+12   1879.000000   1879.000000       1.000000    1740.000000   \n",
       "\n",
       "           Latitude     Longitude  \n",
       "count  39428.000000  39428.000000  \n",
       "mean      37.768836   -122.431798  \n",
       "std        0.023767      0.028455  \n",
       "min       37.708174   -122.510729  \n",
       "25%       37.752256   -122.447067  \n",
       "50%       37.773569   -122.427546  \n",
       "75%       37.788879   -122.409770  \n",
       "max       37.823350   -122.365853  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 Classify issued or not\n",
    "<a id='4.1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict whether a permit will be issued or not (binary clasification) using Permit Type as feature (one-hot encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NA values in y_train: 0\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Issued or not\"]\n",
    "print(\"Number of NA values in y_train: \", end='')\n",
    "print(y_train.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check which columns have NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Permit Number                          False\n",
       "Permit Type                            False\n",
       "Permit Creation Date                   False\n",
       "Block                                  False\n",
       "Lot                                    False\n",
       "Street Number                          False\n",
       "Street Number Suffix                    True\n",
       "Street Name                            False\n",
       "Street Suffix                           True\n",
       "Unit                                    True\n",
       "Unit Suffix                             True\n",
       "Description                             True\n",
       "Current Status                         False\n",
       "Current Status Date                    False\n",
       "Filed Date                             False\n",
       "Issued Date                             True\n",
       "Completed Date                          True\n",
       "First Construction Document Date        True\n",
       "Structural Notification                False\n",
       "Number of Existing Stories              True\n",
       "Number of Proposed Stories              True\n",
       "Voluntary Soft-Story Retrofit          False\n",
       "Fire Only Permit                       False\n",
       "Permit Expiration Date                  True\n",
       "Estimated Cost                          True\n",
       "Revised Cost                            True\n",
       "Existing Use                            True\n",
       "Existing Units                          True\n",
       "Proposed Use                            True\n",
       "Proposed Units                          True\n",
       "Plansets                               False\n",
       "Existing Construction Type             False\n",
       "Proposed Construction Type             False\n",
       "Site Permit                            False\n",
       "Supervisor District                     True\n",
       "Neighborhoods - Analysis Boundaries     True\n",
       "Zipcode                                 True\n",
       "Location                                True\n",
       "Record ID                              False\n",
       "Filed Days                             False\n",
       "Issued Days                             True\n",
       "Issued or not                          False\n",
       "Days to issue                           True\n",
       "Latitude                                True\n",
       "Longitude                               True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fill in missing values in feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset has 159120 rows and 45 columns\n",
      "Testing dataset has 39780 rows and 45 columns\n"
     ]
    }
   ],
   "source": [
    "data_train = data_train.fillna(method='ffill')\n",
    "data_test = data_test.fillna(method='ffill')\n",
    "\n",
    "nrows_train, ncols_train = data_train.shape\n",
    "print(f\"Training dataset has {nrows_train} rows and {ncols_train} columns\")\n",
    "\n",
    "nrows_test, ncols_test = data_test.shape\n",
    "print(f\"Testing dataset has {nrows_test} rows and {ncols_test} columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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deILBGfMPDNuOMfjjhsGH4N+A88B/Aa+ad80zHOsXgP8BHhn+nJx3zbMc75q+X2aBr3Lp+P4G+DvgLPAN4NC8a57xePcBX2VwBcwjwG/Pu+YrGOungO8DP2OwN34H8B7gPSPv7fHha/GNaX+W/eq/JDViUQ65SJImMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI/4fL5F6gHMxbaoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.12414734434278363\n",
      "Mean Squared Error: 0.06253102033436125\n",
      "Root Mean Squared Error: 0.25006203297254315\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Issued or not\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Issued or not\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clearly this should use a binary classification instead... trying SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9253\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.07657114127702364\n",
      "Mean Squared Error: 0.07657114127702364\n",
      "Root Mean Squared Error: 0.27671490974832497\n"
     ]
    }
   ],
   "source": [
    "SVM = svm.LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print(round(SVM.score(X_train, y_train), 4))\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets try to binary classify using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9253\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.07657114127702364\n",
      "Mean Squared Error: 0.07657114127702364\n",
      "Root Mean Squared Error: 0.27671490974832497\n"
     ]
    }
   ],
   "source": [
    "RF = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)\n",
    "RF.fit(X_train, y_train)\n",
    "y_pred = RF.predict(X_test)\n",
    "print(round(RF.score(X_train, y_train), 4))\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not seeing much binary separation. Trying to add another feature (Plansets One-hot encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.1204895830322235\n",
      "Mean Squared Error: 0.060827935011085264\n",
      "Root Mean Squared Error: 0.24663319932864933\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Issued or not\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Issued or not\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets try SVM again using the two features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9253\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.07657114127702364\n",
      "Mean Squared Error: 0.07657114127702364\n",
      "Root Mean Squared Error: 0.27671490974832497\n"
     ]
    }
   ],
   "source": [
    "SVM = svm.LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print(round(SVM.score(X_train, y_train), 4))\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not seeing much binary separation. Trying to add another feature (Proposed Construction Types One-hot Encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.11922111944555337\n",
      "Mean Squared Error: 0.05907380866901399\n",
      "Root Mean Squared Error: 0.2430510412835419\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Issued or not\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Issued or not\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets try SVM using three features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9247\n"
     ]
    },
    {
     "data": {
      "image/png": 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xs2ee5X5XrX2ALfwul45f3wB/D5wBvgsc3uyZZ7zffcA3GL4D5tvAH2z2zJex188CTwC/YPho/DbgHcA7xr62x0e/F9/t+3vZj/5LUiO2ylMukqQpDLokNcKgS1IjDLokNcKgS1IjDLokNcKgS1Ij/hfC9hmtWIiRJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.07677224736048266\n",
      "Mean Squared Error: 0.07677224736048266\n",
      "Root Mean Squared Error: 0.277078052830755\n"
     ]
    }
   ],
   "source": [
    "SVM = svm.LinearSVC()\n",
    "SVM.fit(X_train, y_train)\n",
    "y_pred = SVM.predict(X_test)\n",
    "print(round(SVM.score(X_train, y_train), 4))\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 Regressions\n",
    "<a id='4.2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict \"Days to Issue\" instead of binary classification using Permit Type feature (One-hot encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 30.670178652265193\n",
      "Mean Squared Error: 5742.223838284252\n",
      "Root Mean Squared Error: 75.77746260125271\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type'])\n",
    "))\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Plansets as a feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.116499542650594\n",
      "Mean Squared Error: 5626.642076141689\n",
      "Root Mean Squared Error: 75.01094637545702\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Proposed Construction Type feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.373819631017472\n",
      "Mean Squared Error: 5604.552325119704\n",
      "Root Mean Squared Error: 74.86355805810797\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding zipcode (One-hot encoded) feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XkZsBzAL4+wC2AZxR1U+LyB4AXwMwAuAigPep6s+kUfft0wDuAPALAB9Q1cfDNNqtqIaTz9CPCHjt000qWKdjgvRTqVRabirtWMvrudkPwFGMohyxuV03N1t7Kf4RJe3nm5mZif2aOuE37JGVlrKPZyikiNwA4AZVfVxE3ghgGcARAB8AcEVV7xeRewFcr6ofEZE7APxbNMT9XQA+rarvcjtHmKGQQKPwBgA88MADLbnVd+/e3ZGxsZfJxTgnJsMK+Ys7dHAQ4DUl/cItFNLT566ql82Rt6r+HMDTAG4EcBjAQ8ZuD6Eh+DDaZ7XBYwDeZNwgQsPJXwu8PlpqF3YAeO211/DAAw803/cyuRj3xGRYuVGYYyV8eE1JEgnkcxeREQAHAHwfwFtU9TLQuAGIyJuN3W4E8FPLYWtG2+W2vo4DOA404rPDwvQZOlVDsrb3Eg7Wj1CysNwPrDQUPrymJGn4jpYRkd0AvgHgpKq+6rarTVuH70dVz6hqVVWr+/bt82sGAPsYaZMgX7BeJhcZSkYISTK+xF1E8mgI+5dU1QzbeNF0txi/XzLa1wDcbDn8JgDPh2Nug7DCuHrph6FkhJAk4ynuRvTL5wE8raqfsmx6GMBdxuu7AJyztE9Lg1sBrJvum7DwE9vcXsfUrr2XGGkWfyCEJBk/I/d3A/g9AP9URJ40fu4AcD+A3xaRVQC/bbwHgG8BeBbAMwA+B+DfhG20nwksu0LV7dEyvUyEcRKNEJJkUpkVkhBCSI+hkIQQQtIHxZ0QQjIIxZ0QQjIIxZ0QQjJIarNCLiwsNKvftJeaM2EZMULIoJJKcV9YWIA1usZaas5aS9SpjBjwejY8M09NvV5vec2bASEkzaRS3J3CJpeWlpri7rd2pzWVgfU1a0oSQtJMZn3ubrU7veqRmkRZoYgQQqIklYuYvPK5J4mdO3fi6tWrXR+/e/dubGxsYGNjo2dbRARveMMbWjJj5nI5bG1tNQugmO6oRx99tKWij11fIyMjuHLlSod7q1QqYXR0FKurqx032Xw+j7GxseY287zmb2s/VgqFAnK5nG3/di40u/kW4HV3nLXgi7WiVhzzNH7mi5w+Q9Dc/ZxzSi5h/H3cFjFR3AkxqFarWFlZsX2ya692VavVOqpjeZVdBDrni0zK5TKmp6db+u+lAAgLiCSbsP4+buKeSp87IVHgNsBYWlpy3Q40njbm5uZw6dKllol96+js1Vfts2VfuHABtVqtpfydWyFzrxGf0/Fnz57F3Nyc43Gzs7O4cOFC8337TYeEQxz1IDLrcyekXywtLaFWq9lW63J7UrbO77jVC/BTAczpePP8dse1CzvQuOnMzs462ky6I456EBR3QiJgcXHRdnTmhvWL7VQXQERcR/Rex7sd1y7sXu2ke+KoB0FxJyQC1tfXA4/CrF9sp3oBTiP/9nPZHe/nODdqtRpmZmZw+vRpzMzMRFYveBCIox4Efe6EWKhWq56+dT+YQh1EPK1fbKvv3epbN987nc/peGt0kNtxTrgtCkzKBG2aooOc/r5h2ktxJ8TC1NQUhoeH8c1vfrPr8FPrCKw9IsKJarXa8cV2KrptF2VhN+KzHu8UnWE9rlwu27pgyuVyXwrCByENN592oi6qTnEnqaVUKqFer4eyBsDsD+gUxbNnz7pOhObzeVx33XWOIzCn0bbJsWPHfH/Jux3x+TluenraMVrGKfw4KQXhk37z6QcUd5JIisVix0Imk1KphJMnTwJwX/Pg1kc7TqPfxcVFV2EHGiJi2tOOeaOYmZlxdKcEFZ9uR3x+jnMKeyyVSr7cQb3Qi1sljuiTtMEJVZJYGrXZOxkdHW2+dos6KBQKvs81NjZmKyR+xMGPwKW9oHrU9tuFjdqFeDoRR/RJ2qC4k0RSr9exa9cu222rq6vN126iE2TUZu3Tih9xsN5snEh7QfWo7fdatOVF2m+eUUC3DHHEaYItDpzcAEDraNrNl+zl63bq08rExITnpKjTjaGdqCfQoiZK+3t1q8QRfZI2KO7EllKphCtXroTaZz6fx44dOzz94OaIK0jYn92X2I8wO/Vp7RtwnxQdZL9uWITh00/7zTNs6JYZYIrFInK5XEf70NBQYLeGF4VCAYcOHfIUduvjfq+P2u2uBCcfPgDXPiuVCk6ePEm/boTQrRI+niN3EfkCgIMAXlLV3zDaPg7gXwH4O2O3j6rqt4xt9wH4EIAtAP9OVb8dgd2kR/L5PCYnJwGgJbuhmdkQgOPCl2KxiEKh4Cj+hUIBm5ubtilt3Ubj7REn3Txq20VcmP3axXoD9jHmdtg9CVCAwoFulfDx45b5IoDPAGjPHvSnqvon1gYRuQXAnQD2A/hVAP9bRN6mqlsh2JpogoTdBaFarWJ4eLj5T18oFLqO6zYffdu/OO1fIFME7YTdvCmYuc/txO7gwYOOX8qgAhnkUdtrIUuvAkIBiha6VcLFU9xV9VERGfHZ32EAX1XVqwAuiMgzAN4J4HtdW5gS7rnnHgBwjGcOipMAz8zMdC3uTrHY7TglvBKRlgiJbsQuSoH0s5ClVwGhAJG00MuE6odFZBrAEoC7VfVnAG4E8JhlnzWjrQMROQ7gOAAMDw/3YIY3+Xw+UHa+oLQnfPI7iWdijsb9CF23Nw6zwpEf3NLF+l0i70ZUAskJT0Jep1tx/yyAPwKgxu8HAHwQgN2Mle3yPlU9A+AM0KjE1KUdvohS2HO5HEZHR5sj9lKpZFtGzo1isYj77rvP1/mcogpEBLt27XJ0De3fv99X/2ZfTjZbC0okjThWURKSFrqKllHVF1V1S1W3AXwODdcL0Bip32zZ9SYAz/dmYrxUq1WcOnXK9/5DQ0NYWVlpWVm3srKCiYkJnDp1CkePHvVMvRo0c6Bdf6qKa9euOY7Q/cZim305EWTVYNww4oKQ1+lK3EXkBsvbowB+ZLx+GMCdIrJTRMoARgH8oDcT42V5eRm1Ws33aG9zc9N1ZZ0ZjucWhhc0ltepv83NTceRu58biJmv240gqwbjJu2rQAkJEz+hkF8B8FsA9orIGoBTAH5LRN6OhsvlIoA/AABVfUpEvg7grwBcA3AibZEyqopHHnkEY2NjPeX1bl9FOTc357hv0JGlV392eN1AnMIE7UiyD5sTnoQ08BMt836b5s+77P8JAJ/oxah+s7m5idXVVc/CDW4rLtvF1MkfXCwWuxIjt/6uXbsWOBY7SEk4+rAJST5coerA+vo6pqamcOzYsaaYFYvFpk/bfOSfnJz05ed18gebC4aC4tZfN64Jv6Nx+rAJSQfMLeOAXeEGN7zitsOO7/bqL2i/bk8C5mrUIDanqeQZIVkkleLeyypNPwQdnfq9AYTtDw6zP6eVo+Zq1CCkseQZIVkjlW6ZgwcPYmgoHNNLpRKq1erAR1iEGWnSa25uQkjvpHLk7icNqxd2iaoGnbCeBLhSlJD+k0pxBzqFaGFhAcvLy56rQU04KRgdXClKSP9Jrbi3MzU11Uwra+KUxKvb8MN+kMaJSabGJaT/pNLn7pewww/jpteiwf2CK0UJ6T+ZGbnbkfb8235S2CYVrhQlpL9kWtyBdIsMJyYJId2SabdM2mHNTkJIt1DcEwxT2BJCuiXzbpk0k/Y5A0JI/6C4J5w0zxkQQvpHqsXdKwZ8dnYWFy5caL4vl8uYnp527M+6EEpEMD4+3hE7TwghaUD8ruiMkmq1qkELYzz44IN4+eWXW9pyuRwKhYJjNSIA2Lt3L06cONHRvrCwYJu73Wl/QgjpNyKyrKpVu22pnFCdnZ3tEHYA2NrachV2AHj55ZdtFwEtLy877r+wsNCdoYQQ0idSKe5WV0s32GUndHuCcRJ+QghJKqn2uXfL+vo6arVai39eRBwF3st11W3+lzTmjSGEpIOBFHcAmJ+fx/nz51Gv11EqlTAyMuL4RCAijv10W5iCBS0IIVGSSrdMuVzuuY/t7e2mf359fR1ra2vYvXu37b7j4+OO/XRbmIIFLQghUZJKcZ+enu4Q+HK57FjM2g+bm5vI5XKoVqvNkbqIoFqtuoZDdpv/hXljCCFRklq3jFO8up1Lwymvezvr6+u2eeHd6LYwBQtaEEKixFPcReQLAA4CeElVf8No2wPgawBGAFwE8D5V/Zk0hryfBnAHgF8A+ICqPh6F4V6TkdbtxWIRQ0ND2N7edu2zG2HttjAFC1oQQqLEj1vmiwBub2u7F8Ciqo4CWDTeA8AkgFHj5ziAz4ZjZiteRSzat9frdYhI001TLBaRy+Va+uxWWLstTMGCFoSQKPEcuavqoyIy0tZ8GMBvGa8fAvB/AHzEaJ/VRuzgYyLyJhG5QVUvh2Uw4F3Ewm771tYWCoUC7rnnHgDhhiF2m/+FeWMIIVHRrc/9LaZgq+plEXmz0X4jgJ9a9lsz2jrEXUSOozG6x/DwcKCTe01G+pmspLASQrJM2NEydgHhtiuAVPWMqlZVtbpv375AJ/EqYsEiF4SQQadbcX9RRG4AAOP3S0b7GoCbLfvdBOD57s2zx6uIBYtcEEIGnW7F/WEAdxmv7wJwztI+LQ1uBbAetr8d8J6M5GQlIWTQ8Uz5KyJfQWPydC+AFwGcAjAP4OsAhgFcAvC7qnrFCIX8DBrRNb8A8Puq6pnLt5uUv4QQMui4pfz1Ey3zfodNHT4OI0omluTnTLpFCCHOpHKFKpNuEUKIO6nMLcOkW4QQ4k4qxZ1JtwghxJ1Uijvj2AkhxJ1Uijvj2AkhxJ1UTqiak6aMliGEEHtSKe4Ac8MQQogbqXTLEEIIcYfiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGYTiTgghGSS1icNOnz7tur1YLGL//v1YXV3F+vo6isUiAKBer0NEYBYGLxaLmJycbElCtrCwgOXlZXgVDwfQ0peV9kyVtVoN58+fR71edzyvuZ+Z7dLsO8qsl1msRZvFz0RIUMSPgEVNtVrVpaUl3/t7CXuaqFarmJqaAtBZG9ZKLpdDoVBAvV4PTbDszpfP53Ho0KHUimEWPxMhTojIsqpW7balduSeFcyb2tTUlG1tWJOtra3mqD+sguButWj7LYTdjr6T/JkIiROKewJYWlrCU0891RRvP4QhWG61aGdmZlqEFYivOEr76DvIzYz1dQlp0NOEqohcFJGaiDwpIktG2x4R+a6IrBq/rw/H1GwTRNhNehUst5qzZt/r6+uYn5/HuXPnWtrm5uawsLDQ0/mdcBt9e8H6uoQ0CCNa5p+o6tstfp97ASyq6iiAReM9iYBeBKtWq2FjY8PXvtvb29ja2upoX1paQq1W69oGJ3oZfbO+LiENogiFPAzgIeP1QwCORHAOAnQtWKbbo/1pwYwoCoKf0XRQehl9VyoVHDp0qLlvqVTiZCoZSHr1uSuA74iIAvjvqnoGwFtU9TIAqOplEXmz3YEichzAcQAYHh7u0YzBpFvBcpq4LRQKKBQKgdw9UfiyJyYmbCNe/N7MWF+XkN5H7u9W1XcAmARwQkRu83ugqp5R1aqqVvft29ejGfFz7Nixjsf/OBGRro91c3vYuTWGhpz/TaLwZXP0TUjv9DRyV9Xnjd8vichZAO8E8KKI3GCM2m8A8FIIdiaKUqnUFBrrwqQozrNnzx5cuHChY9v4+HhP/doJvPVztUfGXLp0Ce1rEaL0ZXP0TUhvdL2ISUTeAGBIVX9uvP4ugP8EYALAK6p6v4jcC2CPqt7j1lfaFjGZK0dFBOPj4xgeHu5YVdorpVIJJ0+eBNC6YlZEMDIygitXrnQdltjtQh+u/CQkWUS1iOktAM4a7oEdAL6sqv9LRH4I4Osi8iEAlwD8bg/nSCSmeKsqlpaW8Morr9j6ie0ol8tYW1tz3a99RDw1NeW4irWbBU1Oo3Ov4zmaJiQ9dC3uqvosgDGb9lfQGL0PDBcuXMALL7zgKewAsLa2hrGxsWbOm1KphNHR0Zb3bkIb1gpMCjUh2YYrVEPCr999c3MTq6urTZdLULgCkxDiB6b87QO9CDFXYBJC/MCRu09yuRxUFdvb2z33ZQ01bJ8sHR8fb/rX7eg1BpwQMhgw5W8GGRoaat6E2m8YtSge+7kAAAtxSURBVFoNZ8+ebYno2b17N3K5HNbX15HL5VpSDZTLZRw4cKA5+WrNix90vsDET9SNdZ/2c/YSpRNHxA+jikhcuEXLUNwHBFPkg1znbhgaGsKRI0dsxWxhYcH2/O1hmG557e32t+ImrF4hoL2IsvXYIPYS0gsUdxIrppgFjf03BdVJJNv3bZ+U9hJvM41xO+aNb2VlxfaG4lQ1y+28fuwlpFfcxJ0TqiR0Njc3MT8/3xRSvwMIM2bfz4Sz3T5eqYKd+jXXKziJc71ex7lz5xwzYLoVWXGzl5AoobiTSOh24nlzc9NX3hy7fbzCRHuJKNra2nLMgOlHuBnNROKG4k4Sh6p6JmVzKkpuh9lulxQtCE4i7iXcjGYi/YDiThLJ2NiYq2jabXMS742NDdRqtWa2yW4zajrZ43bTYEZL0i8Y504SibmK12mS1G4k7JSps16vt+Tfccrkmc/nsWPHDtttuVzOcfTdba4eQqKE4k5CxU9kjBkf7xSdArzuAgkqnJVKBYuLix0Cbc2/45QqYnNzEx/96EdRq9VabgBe0TLmeSnmJElQ3DNMWOmHg6CqjvniTdbX17GysoKxsbHm6tx2rC6QoMLpZ2LVKZ99N+cjJInQ555h+rGGwRxZe01cmgnUjh49GnpB624mVjnpSbIGxT1D9FJ6Lwzy+TxGR0d9xX0DjZF0FCX1vMSbZfzIIEC3TEZoXxUaN8ViEfv373f1o7cTlRvEj5+erheSdSjuGcAqXnNzc677FovFnmq+5vP5jmIj5rlnZmZ8C3vUbhCKNxl0KO4pxi4hldtkZrFYxD333OOa5MoNr6gRv/35iT4hhPQGxT2lOAnkxMQEzp0715K2F2j44ycnJwG0jmqdkmn5OVc7TjcWM2qH8d+ExAfFPQJM10dUoYjVatWxoIfdQh43cXYr7B1UjJ0KiXCykpD4obiHTLlcxnPPPQcg/FBEP5WagGD+5jBXV0a1UjNotSpCCPO5h461ClIUiAh27dqFer0eaoWipOJU4MPt6YWQQYH53EMkn8+jUCjYbhORSIUdaDwNmO6Wer3efG3mQnfKOZ5WlpeXA7UTQhpEJu4icruI/FhEnhGRe6M6T9xsbm5iY2PDdlu/n4KshSmygtM17fe1JiTpRCLuIpID8CCASQC3AHi/iNwSxblIK1mr+OO06rbfq3EJSTpRjdzfCeAZVX1WVTcAfBXA4YjORSxkreLP+Ph4oHZCSIOoomVuBPBTy/s1AO+y7iAixwEcB4Dh4eGIzAiXXC7XET+eJBu6WfVpXdCUxElZc9KU0TKEBCMqcbd7Zm5xkqrqGQBngEa0TER29Ez7ApxLly7ZRm8MDQ1hx44djv74MKhWqxgeHm6Kca/RMu2FMMxJWQCJE3iKOSHBiErc1wDcbHl/E4DnIzpXJAwNDeHIkSMdIme+bxf4nTt3Yv/+/XjiiScCj+6r1WpHrpZLly45jlbDEl677I3WohaEkPQSlbj/EMCoiJQBPAfgTgD/IqJzhU6hUMDBgwcdBW5qagrDw8Mto956vY6lpaVmqKQZh37t2jXHZFrlchnT09O22yqVSssofXV1tVkHNCy8iloQQtJLJOKuqtdE5MMAvg0gB+ALqvpUFOcKi1wuh8OHD/sWT6ec5WZbtVp1TX+bz+dx4MABx/7jcJl4VSQihKSXyOLcVfVbqvo2Vf11Vf1EVOcJi62trUAx4m6j283NTSwvL7umv/WKSXdzmYQFKxIRkl2YW8ZCEHeEV51QP4tsvOqMBj0mKFHlgiGE9B+Ku4Ug7gi3bIqAv+LUbueLy2XCohaEZBPmljEI6o4w63Ca4YjtfY2Pj7sWifY6H10mhJBeGOiRe69FJMxRr9NCoF5i0ukyIYT0QmZT/rKUGyEk67il/M3kyJ25vgkhg07mfO4UdkIIyaC4U9gJISSD4k4IISSl4u4U681l84QQ0iCV4j46OhqonRBCBo1Uivvq6mqgdkIIGTRSKe5MVUsIIe6kUtzpcyeEEHdSKe7Mu0IIIe6kcoUq864QQog7qRR3gKlqCSHEjVS6ZQghhLhDcSeEkAxCcSeEkAxCcSeEkAxCcSeEkAySiEpMIvJ3AP62y8P3Ang5RHOihvZGS5rsTZOtAO2Nmm7s/TVV3We3IRHi3gsisuRUZiqJ0N5oSZO9abIVoL1RE7a9dMsQQkgGobgTQkgGyYK4n+m3AQGhvdGSJnvTZCtAe6MmVHtT73MnhBDSSRZG7oQQQtqguBNCSAZJtbiLyO0i8mMReUZE7k2APTeLyF+IyNMi8pSI/KHR/nEReU5EnjR+7rAcc59h/49F5L19sPmiiNQMu5aMtj0i8l0RWTV+X2+0i4j8mWHvX4rIO2K29R9YruGTIvKqiJxM0vUVkS+IyEsi8iNLW+DrKSJ3GfuvishdMdv7X0Xkrw2bzorIm4z2ERGpW67zn1uOGTf+j54xPpPEZGvgv31cuuFg79cstl4UkSeN9vCvraqm8gdADsBPALwVQAHACoBb+mzTDQDeYbx+I4C/AXALgI8D+A82+99i2L0TQNn4PLmYbb4IYG9b238BcK/x+l4Af2y8vgPAeQAC4FYA3+/z3/8FAL+WpOsL4DYA7wDwo26vJ4A9AJ41fl9vvL4+RnvfA2CH8fqPLfaOWPdr6+cHAP6R8VnOA5iMydZAf/s4dcPO3rbtDwD4j1Fd2zSP3N8J4BlVfVZVNwB8FcDhfhqkqpdV9XHj9c8BPA3gRpdDDgP4qqpeVdULAJ5B43P1m8MAHjJePwTgiKV9Vhs8BuBNInJDPwwEMAHgJ6rqtrI59uurqo8CuGJjR5Dr+V4A31XVK6r6MwDfBXB7XPaq6ndU9Zrx9jEAN7n1Ydj8K6r6PW2o0Sxe/4yR2uqC098+Nt1ws9cYfb8PwFfc+ujl2qZZ3G8E8FPL+zW4C2msiMgIgAMAvm80fdh4zP2C+ViOZHwGBfAdEVkWkeNG21tU9TLQuGEBeLPRngR7Te5E6xcjqdcXCH49k2I3AHwQjdGiSVlEnhCR/ysiv2m03YiGjSZx2xvkb5+Ua/ubAF5U1VVLW6jXNs3ibud3SkRcp4jsBvANACdV9VUAnwXw6wDeDuAyGo9jQDI+w7tV9R0AJgGcEJHbXPZNgr0QkQKA3wHwP42mJF9fN5zsS4TdIvIxANcAfMlougxgWFUPAPj3AL4sIr+C/tob9G+fiGsL4P1oHZyEfm3TLO5rAG62vL8JwPN9sqWJiOTREPYvqeocAKjqi6q6parbAD6H110Dff8Mqvq88fslAGcN21403S3G75eM3ftur8EkgMdV9UUg2dfXIOj17LvdxiTuQQD/0nAHwHBxvGK8XkbDd/02w16r6yY2e7v42yfh2u4AcAzA18y2KK5tmsX9hwBGRaRsjOTuBPBwPw0y/GifB/C0qn7K0m71Sx8FYM6ePwzgThHZKSJlAKNoTJ7EZe8bROSN5ms0JtJ+ZNhlRmjcBeCcxd5pI8rjVgDrprshZlpGPUm9vhaCXs9vA3iPiFxvuBneY7TFgojcDuAjAH5HVX9had8nIjnj9VvRuJ7PGjb/XERuNb4D05bPGLWtQf/2SdCNfwbgr1W16W6J5NpGMUsc1w8a0QZ/g8Zd7mMJsOcfo/HI9JcAnjR+7gDwPwDUjPaHAdxgOeZjhv0/RgQRBh72vhWNaIEVAE+Z1xDA3wOwCGDV+L3HaBcADxr21gBU+3CNrwPwCoCSpS0x1xeNm85lAJtojLo+1M31RMPX/Yzx8/sx2/sMGn5p83/4z419/7nxf7IC4HEAhyz9VNEQ1p8A+AyM1e8x2Br4bx+XbtjZa7R/EcC/bts39GvL9AOEEJJB0uyWIYQQ4gDFnRBCMgjFnRBCMgjFnRBCMgjFnRBCMgjFnRBCMgjFnRBCMsj/B3zshifTT/IOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.76528330132133\n",
      "Mean Squared Error: 5598.743466852393\n",
      "Root Mean Squared Error: 74.82475169923649\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding supervisor district feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.814007632800717\n",
      "Mean Squared Error: 5597.525444929608\n",
      "Root Mean Squared Error: 74.81661209203213\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_train['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_test['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Existing Construction Type feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.79750053517\n",
      "Mean Squared Error: 5596.454261246669\n",
      "Root Mean Squared Error: 74.8094530206355\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_train['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_train['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_test['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_test['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Neighborhoods - Analysis Boundaries feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.895160096227492\n",
      "Mean Squared Error: 5594.647651243515\n",
      "Root Mean Squared Error: 74.79737730190487\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_train['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_train['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_train['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_test['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_test['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_test['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Proposed Use feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (39780,198) and (207,) not aligned: 198 (dim 1) != 207 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-daa101fdff12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \"\"\"\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[0;32m--> 206\u001b[0;31m                                dense_output=True) + self.intercept_\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (39780,198) and (207,) not aligned: 198 (dim 1) != 207 (dim 0)"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_train['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_train['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_train['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood']),\n",
    "    pd.get_dummies(data_train['Proposed Use'].to_numpy(dtype ='str'),dummy_na=True,prefix=['location'])\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_test['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_test['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_test['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood']),\n",
    "    pd.get_dummies(data_test['Proposed Use'].to_numpy(dtype ='str'),dummy_na=True,prefix=['location'])\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add longitude and latitude features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.91595621622046\n",
      "Mean Squared Error: 5594.944706664702\n",
      "Root Mean Squared Error: 74.79936300975231\n"
     ]
    }
   ],
   "source": [
    "y_train = data_train[\"Days to issue\"]\n",
    "nrows_train = y_train.shape\n",
    "\n",
    "X_train = np.column_stack((\n",
    "    np.ones(nrows_train), #Intercept\n",
    "    pd.get_dummies(data_train['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_train['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_train['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_train['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_train['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_train['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_train['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood']),\n",
    "    data_train['Latitude'],\n",
    "    data_train['Longitude']\n",
    "))\n",
    "X_train\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "y_test = data_test[\"Days to issue\"]\n",
    "nrows_test = y_test.shape\n",
    "\n",
    "X_test = np.column_stack((\n",
    "    np.ones(nrows_test), #Intercept\n",
    "    pd.get_dummies(data_test['Permit Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['permit_type']),\n",
    "    pd.get_dummies(data_test['Plansets'].to_numpy(dtype ='str'),dummy_na=True,prefix=['planset']),\n",
    "    pd.get_dummies(data_test['Proposed Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['proposed_construction_type']),\n",
    "    pd.get_dummies(data_test['Zipcode'].to_numpy(dtype ='str'),dummy_na=True,prefix=['zipcodes']),\n",
    "    pd.get_dummies(data_test['Supervisor District'].to_numpy(dtype ='str'),dummy_na=True,prefix=['supervisor_district']),\n",
    "    pd.get_dummies(data_test['Existing Construction Type'].to_numpy(dtype ='str'),dummy_na=True,prefix=['existing_construction_type']),\n",
    "    pd.get_dummies(data_test['Neighborhoods - Analysis Boundaries'].to_numpy(dtype ='str'),dummy_na=True,prefix=['neighborhood']),\n",
    "    data_test['Latitude'],\n",
    "    data_test['Longitude']\n",
    "))\n",
    "X_test\n",
    "\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_pred,  color='gray')\n",
    "plt.show()\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
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 "nbformat_minor": 2
}