feature_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ECS308: Data Science and Machine Learning Project | Akanksha Singh, 19022\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "nALnaq9T4k6m"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "n5i3qZen2cKG",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>...</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>FBI Code</th>\n",
       "      <th>X Coordinate</th>\n",
       "      <th>Y Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015825</td>\n",
       "      <td>2508182</td>\n",
       "      <td>HH781001</td>\n",
       "      <td>11/15/2002 12:30:00 PM</td>\n",
       "      <td>003XX N ASHLAND AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>06</td>\n",
       "      <td>1165667.0</td>\n",
       "      <td>1902638.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.888430</td>\n",
       "      <td>-87.667064</td>\n",
       "      <td>(41.888429628, -87.667063831)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2110300</td>\n",
       "      <td>2687361</td>\n",
       "      <td>HJ308830</td>\n",
       "      <td>04/18/2003 06:10:00 PM</td>\n",
       "      <td>016XX W 18TH ST</td>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1165650.0</td>\n",
       "      <td>1891466.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "      <td>41.857773</td>\n",
       "      <td>-87.667445</td>\n",
       "      <td>(41.857773091, -87.667444544)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>381828</td>\n",
       "      <td>10755691</td>\n",
       "      <td>HZ518320</td>\n",
       "      <td>11/16/2016 09:50:00 PM</td>\n",
       "      <td>009XX S CICERO AVE</td>\n",
       "      <td>1506</td>\n",
       "      <td>PROSTITUTION</td>\n",
       "      <td>SOLICIT ON PUBLIC WAY</td>\n",
       "      <td>STREET</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1144531.0</td>\n",
       "      <td>1895566.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "      <td>41.869447</td>\n",
       "      <td>-87.744861</td>\n",
       "      <td>(41.869447309, -87.744861466)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6048052</td>\n",
       "      <td>8740111</td>\n",
       "      <td>HV415603</td>\n",
       "      <td>08/04/2012 05:15:00 PM</td>\n",
       "      <td>072XX S INGLESIDE AVE</td>\n",
       "      <td>0460</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1183801.0</td>\n",
       "      <td>1857416.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>02/04/2016 06:33:39 AM</td>\n",
       "      <td>41.763932</td>\n",
       "      <td>-87.601884</td>\n",
       "      <td>(41.763931739, -87.601884055)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6873402</td>\n",
       "      <td>11924187</td>\n",
       "      <td>JC549979</td>\n",
       "      <td>12/16/2019 04:45:00 PM</td>\n",
       "      <td>017XX W THOME AVE</td>\n",
       "      <td>1310</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO PROPERTY</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1163394.0</td>\n",
       "      <td>1941667.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>12/23/2019 03:56:38 PM</td>\n",
       "      <td>41.995575</td>\n",
       "      <td>-87.674309</td>\n",
       "      <td>(41.995575459, -87.674309158)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>99995</th>\n",
       "      <td>3562445</td>\n",
       "      <td>4714376</td>\n",
       "      <td>HM318809</td>\n",
       "      <td>04/21/2006 12:01:00 AM</td>\n",
       "      <td>022XX S WENTWORTH AVE</td>\n",
       "      <td>1310</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO PROPERTY</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1175327.0</td>\n",
       "      <td>1889358.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "      <td>41.851777</td>\n",
       "      <td>-87.631988</td>\n",
       "      <td>(41.851777151, -87.631987613)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>4621702</td>\n",
       "      <td>6567536</td>\n",
       "      <td>HP637109</td>\n",
       "      <td>10/19/2008 10:05:00 PM</td>\n",
       "      <td>002XX W CERMAK RD</td>\n",
       "      <td>1360</td>\n",
       "      <td>CRIMINAL TRESPASS</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1175002.0</td>\n",
       "      <td>1889741.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.852835</td>\n",
       "      <td>-87.633169</td>\n",
       "      <td>(41.852835409, -87.633168974)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>5483697</td>\n",
       "      <td>7815219</td>\n",
       "      <td>HS625058</td>\n",
       "      <td>11/20/2010 04:50:00 PM</td>\n",
       "      <td>0000X N STATE ST</td>\n",
       "      <td>0860</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>RETAIL THEFT</td>\n",
       "      <td>DEPARTMENT STORE</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>06</td>\n",
       "      <td>1176362.0</td>\n",
       "      <td>1900577.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "      <td>41.882540</td>\n",
       "      <td>-87.627851</td>\n",
       "      <td>(41.882539542, -87.627850543)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>3616848</td>\n",
       "      <td>4921843</td>\n",
       "      <td>HM536277</td>\n",
       "      <td>08/12/2006 05:29:00 PM</td>\n",
       "      <td>032XX W ROOSEVELT RD</td>\n",
       "      <td>0530</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>AGGRAVATED: OTHER DANG WEAPON</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>04A</td>\n",
       "      <td>1154766.0</td>\n",
       "      <td>1894523.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.866387</td>\n",
       "      <td>-87.707314</td>\n",
       "      <td>(41.866386519, -87.707313676)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>4558204</td>\n",
       "      <td>6401168</td>\n",
       "      <td>HP484827</td>\n",
       "      <td>07/30/2008 10:00:00 PM</td>\n",
       "      <td>026XX W POTOMAC AVE</td>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1158631.0</td>\n",
       "      <td>1908547.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.904791</td>\n",
       "      <td>-87.692741</td>\n",
       "      <td>(41.904791439, -87.692740532)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        ID Case Number                    Date  \\\n",
       "0         2015825   2508182    HH781001  11/15/2002 12:30:00 PM   \n",
       "1         2110300   2687361    HJ308830  04/18/2003 06:10:00 PM   \n",
       "2          381828  10755691    HZ518320  11/16/2016 09:50:00 PM   \n",
       "3         6048052   8740111    HV415603  08/04/2012 05:15:00 PM   \n",
       "4         6873402  11924187    JC549979  12/16/2019 04:45:00 PM   \n",
       "...           ...       ...         ...                     ...   \n",
       "99995     3562445   4714376    HM318809  04/21/2006 12:01:00 AM   \n",
       "99996     4621702   6567536    HP637109  10/19/2008 10:05:00 PM   \n",
       "99997     5483697   7815219    HS625058  11/20/2010 04:50:00 PM   \n",
       "99998     3616848   4921843    HM536277  08/12/2006 05:29:00 PM   \n",
       "99999     4558204   6401168    HP484827  07/30/2008 10:00:00 PM   \n",
       "\n",
       "                       Block  IUCR       Primary Type  \\\n",
       "0        003XX N ASHLAND AVE  0810              THEFT   \n",
       "1            016XX W 18TH ST  0486            BATTERY   \n",
       "2         009XX S CICERO AVE  1506       PROSTITUTION   \n",
       "3      072XX S INGLESIDE AVE  0460            BATTERY   \n",
       "4          017XX W THOME AVE  1310    CRIMINAL DAMAGE   \n",
       "...                      ...   ...                ...   \n",
       "99995  022XX S WENTWORTH AVE  1310    CRIMINAL DAMAGE   \n",
       "99996      002XX W CERMAK RD  1360  CRIMINAL TRESPASS   \n",
       "99997       0000X N STATE ST  0860              THEFT   \n",
       "99998   032XX W ROOSEVELT RD  0530            ASSAULT   \n",
       "99999    026XX W POTOMAC AVE  0486            BATTERY   \n",
       "\n",
       "                         Description Location Description  Arrest  ...  Ward  \\\n",
       "0                          OVER $500               STREET   False  ...  27.0   \n",
       "1            DOMESTIC BATTERY SIMPLE             SIDEWALK   False  ...  25.0   \n",
       "2              SOLICIT ON PUBLIC WAY               STREET    True  ...  24.0   \n",
       "3                             SIMPLE                OTHER   False  ...   5.0   \n",
       "4                        TO PROPERTY            APARTMENT   False  ...  40.0   \n",
       "...                              ...                  ...     ...  ...   ...   \n",
       "99995                    TO PROPERTY           RESTAURANT   False  ...  25.0   \n",
       "99996                     TO VEHICLE                ALLEY    True  ...  25.0   \n",
       "99997                   RETAIL THEFT     DEPARTMENT STORE    True  ...  42.0   \n",
       "99998  AGGRAVATED: OTHER DANG WEAPON                ALLEY    True  ...  24.0   \n",
       "99999        DOMESTIC BATTERY SIMPLE            APARTMENT   False  ...  26.0   \n",
       "\n",
       "       Community Area  FBI Code  X Coordinate  Y Coordinate  Year  \\\n",
       "0                28.0        06     1165667.0     1902638.0  2002   \n",
       "1                31.0       08B     1165650.0     1891466.0  2003   \n",
       "2                25.0        16     1144531.0     1895566.0  2016   \n",
       "3                69.0       08B     1183801.0     1857416.0  2012   \n",
       "4                77.0        14     1163394.0     1941667.0  2019   \n",
       "...               ...       ...           ...           ...   ...   \n",
       "99995            34.0        14     1175327.0     1889358.0  2006   \n",
       "99996            34.0        26     1175002.0     1889741.0  2008   \n",
       "99997            32.0        06     1176362.0     1900577.0  2010   \n",
       "99998            29.0       04A     1154766.0     1894523.0  2006   \n",
       "99999            24.0       08B     1158631.0     1908547.0  2008   \n",
       "\n",
       "                   Updated On   Latitude  Longitude  \\\n",
       "0      02/28/2018 03:56:25 PM  41.888430 -87.667064   \n",
       "1      02/10/2018 03:50:01 PM  41.857773 -87.667445   \n",
       "2      02/10/2018 03:50:01 PM  41.869447 -87.744861   \n",
       "3      02/04/2016 06:33:39 AM  41.763932 -87.601884   \n",
       "4      12/23/2019 03:56:38 PM  41.995575 -87.674309   \n",
       "...                       ...        ...        ...   \n",
       "99995  02/10/2018 03:50:01 PM  41.851777 -87.631988   \n",
       "99996  02/28/2018 03:56:25 PM  41.852835 -87.633169   \n",
       "99997  02/10/2018 03:50:01 PM  41.882540 -87.627851   \n",
       "99998  02/28/2018 03:56:25 PM  41.866387 -87.707314   \n",
       "99999  02/28/2018 03:56:25 PM  41.904791 -87.692741   \n",
       "\n",
       "                            Location  \n",
       "0      (41.888429628, -87.667063831)  \n",
       "1      (41.857773091, -87.667444544)  \n",
       "2      (41.869447309, -87.744861466)  \n",
       "3      (41.763931739, -87.601884055)  \n",
       "4      (41.995575459, -87.674309158)  \n",
       "...                              ...  \n",
       "99995  (41.851777151, -87.631987613)  \n",
       "99996  (41.852835409, -87.633168974)  \n",
       "99997  (41.882539542, -87.627850543)  \n",
       "99998  (41.866386519, -87.707313676)  \n",
       "99999  (41.904791439, -87.692740532)  \n",
       "\n",
       "[100000 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data\n",
    "df = pd.read_csv('dataset.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Latitude', 'Longitude'], axis = 1) #redundant feature, already used in location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 100000 entries, 2002-11-15 12:30:00 to 2008-07-30 22:00:00\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   Unnamed: 0            100000 non-null  object        \n",
      " 1   ID                    100000 non-null  object        \n",
      " 2   Case Number           100000 non-null  object        \n",
      " 3   Date                  100000 non-null  datetime64[ns]\n",
      " 4   Block                 100000 non-null  object        \n",
      " 5   IUCR                  100000 non-null  object        \n",
      " 6   Primary Type          100000 non-null  object        \n",
      " 7   Description           100000 non-null  object        \n",
      " 8   Location Description  100000 non-null  object        \n",
      " 9   Arrest                100000 non-null  bool          \n",
      " 10  Domestic              100000 non-null  bool          \n",
      " 11  Beat                  100000 non-null  object        \n",
      " 12  District              100000 non-null  object        \n",
      " 13  Ward                  100000 non-null  object        \n",
      " 14  Community Area        100000 non-null  object        \n",
      " 15  FBI Code              100000 non-null  object        \n",
      " 16  X Coordinate          100000 non-null  object        \n",
      " 17  Y Coordinate          100000 non-null  object        \n",
      " 18  Year                  100000 non-null  object        \n",
      " 19  Updated On            100000 non-null  object        \n",
      " 20  Location              100000 non-null  object        \n",
      "dtypes: bool(2), datetime64[ns](1), object(18)\n",
      "memory usage: 15.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df = df.astype({\"Unnamed: 0\": object})\n",
    "df = df.astype({\"ID\": object})\n",
    "df = df.astype({\"Beat\": object})\n",
    "df = df.astype({\"District\": object})\n",
    "df = df.astype({\"Ward\": object})\n",
    "df = df.astype({\"Community Area\": object})\n",
    "df = df.astype({\"Year\": object})\n",
    "df = df.astype({\"X Coordinate\": object})\n",
    "df = df.astype({\"Y Coordinate\": object})\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1. Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.1 Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropy(y):\n",
    "    probs = [] # Probabilities of each class label\n",
    "    for c in set(y): # Set gets a unique set of values. We're iterating over each value\n",
    "        num_same_class = sum(y == c)  # Remember that true == 1, so we can sum.\n",
    "        p = num_same_class / len(y) # Probability of this class label\n",
    "        probs.append(p)\n",
    "    return sum(-p * np.log2(p) for p in probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This code takes too long to execute. So, the result has been appended as a column \"Entropy\" in df_ent\\n\\n#Code:\\nfor column in columns:\\n    print(entropy(df[column]))\\n    df_ent.append(entropy(df[column]))\\n\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Unnamed: 0', 'ID', 'Case Number', 'Date', 'Block', 'IUCR',\n",
    "       'Primary Type', 'Description', 'Location Description', 'Arrest',\n",
    "       'Domestic', 'Beat', 'District', 'Ward', 'Community Area', 'FBI Code',\n",
    "       'X Coordinate', 'Y Coordinate', 'Year', 'Updated On', 'Location']\n",
    "\n",
    "\"\"\"This code takes too long to execute. So, the result has been appended as a column \"Entropy\" in df_ent\n",
    "\n",
    "#Code:\n",
    "for column in columns:\n",
    "    print(entropy(df[column]))\n",
    "    df_ent.append(entropy(df[column]))\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Features</th>\n",
       "      <th>Entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>16.609640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID</td>\n",
       "      <td>16.609640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Case Number</td>\n",
       "      <td>16.609640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Date</td>\n",
       "      <td>16.503254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Block</td>\n",
       "      <td>13.842338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IUCR</td>\n",
       "      <td>5.649406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Primary Type</td>\n",
       "      <td>3.482726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Description</td>\n",
       "      <td>5.518047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location Description</td>\n",
       "      <td>4.055616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arrest</td>\n",
       "      <td>0.839130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Domestic</td>\n",
       "      <td>0.577362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Beat</td>\n",
       "      <td>8.091504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>District</td>\n",
       "      <td>4.403739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ward</td>\n",
       "      <td>5.475454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Community Area</td>\n",
       "      <td>5.826020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FBI Code</td>\n",
       "      <td>3.557429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>X Coordinate</td>\n",
       "      <td>14.760132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Y Coordinate</td>\n",
       "      <td>15.117476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Year</td>\n",
       "      <td>4.239197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Updated On</td>\n",
       "      <td>2.855392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Location</td>\n",
       "      <td>15.741332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Features    Entropy\n",
       "0             Unnamed: 0  16.609640\n",
       "1                     ID  16.609640\n",
       "2            Case Number  16.609640\n",
       "3                   Date  16.503254\n",
       "4                  Block  13.842338\n",
       "5                   IUCR   5.649406\n",
       "6           Primary Type   3.482726\n",
       "7            Description   5.518047\n",
       "8   Location Description   4.055616\n",
       "9                 Arrest   0.839130\n",
       "10              Domestic   0.577362\n",
       "11                  Beat   8.091504\n",
       "12              District   4.403739\n",
       "13                  Ward   5.475454\n",
       "14        Community Area   5.826020\n",
       "15              FBI Code   3.557429\n",
       "16          X Coordinate  14.760132\n",
       "17          Y Coordinate  15.117476\n",
       "18                  Year   4.239197\n",
       "19            Updated On   2.855392\n",
       "20              Location  15.741332"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ent = pd.DataFrame()\n",
    "df_ent[\"Features\"] = columns\n",
    "df_ent[\"Entropy\"] = [16.60964047441802, 16.60964047441802, 16.60964047441802, 16.50325437354276, \n",
    "                     13.842338152132987, 5.6494058088293935, 3.482725829513007, 5.518046958190912,4.0556159613837135,\n",
    "                     0.839130421235637,0.5773624295265078,8.0915039193288,4.403738721836829,5.475453895970417,\n",
    "                     5.826020205215316,3.5574293236120447,14.760131667723728,15.11747552923421,4.239196753831807,\n",
    "                     2.8553923756999566,15.74133241017893]\n",
    "\n",
    "df_ent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "944QMlQcv9Ti",
    "scrolled": true
   },
   "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>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Beat</th>\n",
       "      <th>District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>FBI Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1333</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>06</td>\n",
       "      <td>2002</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1222</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>2003</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1506</td>\n",
       "      <td>PROSTITUTION</td>\n",
       "      <td>SOLICIT ON PUBLIC WAY</td>\n",
       "      <td>STREET</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1131</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2016</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0460</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>324</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>2012</td>\n",
       "      <td>02/04/2016 06:33:39 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1310</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO PROPERTY</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2433</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2019</td>\n",
       "      <td>12/23/2019 03:56:38 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>99995</th>\n",
       "      <td>1310</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO PROPERTY</td>\n",
       "      <td>RESTAURANT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2111</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2006</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>1360</td>\n",
       "      <td>CRIMINAL TRESPASS</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2111</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>26</td>\n",
       "      <td>2008</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>0860</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>RETAIL THEFT</td>\n",
       "      <td>DEPARTMENT STORE</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>06</td>\n",
       "      <td>2010</td>\n",
       "      <td>02/10/2018 03:50:01 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>0530</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>AGGRAVATED: OTHER DANG WEAPON</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1022</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>04A</td>\n",
       "      <td>2006</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1423</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>2008</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       IUCR       Primary Type                    Description  \\\n",
       "0      0810              THEFT                      OVER $500   \n",
       "1      0486            BATTERY        DOMESTIC BATTERY SIMPLE   \n",
       "2      1506       PROSTITUTION          SOLICIT ON PUBLIC WAY   \n",
       "3      0460            BATTERY                         SIMPLE   \n",
       "4      1310    CRIMINAL DAMAGE                    TO PROPERTY   \n",
       "...     ...                ...                            ...   \n",
       "99995  1310    CRIMINAL DAMAGE                    TO PROPERTY   \n",
       "99996  1360  CRIMINAL TRESPASS                     TO VEHICLE   \n",
       "99997  0860              THEFT                   RETAIL THEFT   \n",
       "99998  0530            ASSAULT  AGGRAVATED: OTHER DANG WEAPON   \n",
       "99999  0486            BATTERY        DOMESTIC BATTERY SIMPLE   \n",
       "\n",
       "      Location Description  Arrest  Domestic  Beat District  Ward  \\\n",
       "0                   STREET   False     False  1333     12.0  27.0   \n",
       "1                 SIDEWALK   False      True  1222     12.0  25.0   \n",
       "2                   STREET    True     False  1131     11.0  24.0   \n",
       "3                    OTHER   False     False   324      3.0   5.0   \n",
       "4                APARTMENT   False     False  2433     24.0  40.0   \n",
       "...                    ...     ...       ...   ...      ...   ...   \n",
       "99995           RESTAURANT   False     False  2111      9.0  25.0   \n",
       "99996                ALLEY    True     False  2111      9.0  25.0   \n",
       "99997     DEPARTMENT STORE    True     False   122      1.0  42.0   \n",
       "99998                ALLEY    True     False  1022     10.0  24.0   \n",
       "99999            APARTMENT   False     False  1423     14.0  26.0   \n",
       "\n",
       "      Community Area FBI Code  Year              Updated On  \n",
       "0               28.0       06  2002  02/28/2018 03:56:25 PM  \n",
       "1               31.0      08B  2003  02/10/2018 03:50:01 PM  \n",
       "2               25.0       16  2016  02/10/2018 03:50:01 PM  \n",
       "3               69.0      08B  2012  02/04/2016 06:33:39 AM  \n",
       "4               77.0       14  2019  12/23/2019 03:56:38 PM  \n",
       "...              ...      ...   ...                     ...  \n",
       "99995           34.0       14  2006  02/10/2018 03:50:01 PM  \n",
       "99996           34.0       26  2008  02/28/2018 03:56:25 PM  \n",
       "99997           32.0       06  2010  02/10/2018 03:50:01 PM  \n",
       "99998           29.0      04A  2006  02/28/2018 03:56:25 PM  \n",
       "99999           24.0      08B  2008  02/28/2018 03:56:25 PM  \n",
       "\n",
       "[100000 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping high entropy features(Entropy > 10)\n",
    "\n",
    "df_drop = df.drop(columns =['Unnamed: 0', 'ID', 'Case Number', 'Date', \n",
    "                        'Block', 'X Coordinate', 'Y Coordinate', 'Location'])\n",
    "\n",
    "df_drop.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - 100,000 Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - 25,000 Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are considering 25k data points due to technical limitations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#These data points have unique primary crime type, later we will need to drop it for splitting\n",
    "\n",
    "df_drop.drop([df_drop.index[64744], df_drop.index[94033]], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1. Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting data into 25k\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_drop[['IUCR','Description','Location Description','Arrest','Domestic','Beat','District',\n",
    "          'Ward','Community Area','FBI Code','Year','Updated On']]\n",
    "Y = df_drop['Primary Type']\n",
    "\n",
    "data25k, data75k, test25k, test75k= train_test_split(X, Y, test_size = 0.75, random_state = 50)\n",
    "\n",
    "data25 = pd.concat([data25k, test25k], axis=1)\n",
    "data25 = data25.reset_index(drop= True)\n",
    "\n",
    "data25.to_csv(\"data25.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2. One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['IUCR',\n",
       " 'Primary Type',\n",
       " 'Description',\n",
       " 'Location Description',\n",
       " 'Arrest',\n",
       " 'Domestic',\n",
       " 'Beat',\n",
       " 'District',\n",
       " 'Ward',\n",
       " 'Community Area',\n",
       " 'FBI Code',\n",
       " 'Year',\n",
       " 'Updated On']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = []\n",
    "for row in df_drop:\n",
    "  column_names.append(row)\n",
    "\n",
    "column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akank\\AppData\\Local\\Temp\\ipykernel_15648\\1443840504.py:3: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  df_dum = pd.get_dummies(data25, columns = column_names)\n"
     ]
    }
   ],
   "source": [
    "# generate binary values using get_dummies\n",
    "\n",
    "df_dum = pd.get_dummies(data25, columns = column_names)\n",
    "df_dum = df_dum.reset_index(drop = True)"
   ]
  }
 ],
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