Vinay_Crime_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import folium\n",
    "import json\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime = pd.read_csv(\"../withzip1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instances of Crime by Type of Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESIDENCE/HOUSE                               141026\n",
       "APARTMENT                                     125740\n",
       "APARTMENT PARKING LOT                         122372\n",
       "ROAD/STREET/SIDEWALK                           88333\n",
       "DRIVEWAY                                       59311\n",
       "OTHER PARKING LOT                              48647\n",
       "DEPARTMENT/DISCOUNT STORE                      42158\n",
       "RESTAURANT/CAFETERIA PARKING LOT               34513\n",
       "OTHER/UNKNOWN                                  26737\n",
       "COMMERCIAL PARKING LOT/GARAGE                  26287\n",
       "GROCERY/SUPERMARKET                            22880\n",
       "STRIP BUSINESS CENTER PARKING LOT              19990\n",
       "SERVICE/GAS STATION                            17981\n",
       "RESTAURANT/CAFETERIA                           16607\n",
       "MISC. BUSINESS (NON-SPECIFIC)                  16264\n",
       "HOTEL/MOTEL PARKING LOT                        14639\n",
       "CONVENIENCE STORE                              14413\n",
       "GROCERY/SUPERMARKET PARKING LOT                12877\n",
       "BAR/NIGHT CLUB PARKING LOT                     11067\n",
       "BAR/NIGHT CLUB                                  9841\n",
       "HOTEL/MOTEL/ETC.                                9368\n",
       "CONVENIENCE STORE PARKING LOT                   9348\n",
       "COMMERCIAL BUILDING                             8431\n",
       "SPECIALTY STORE (NON-SPECIFIC)                  8355\n",
       "MALL PARKING LOT                                8183\n",
       "GARAGE/CARPORT                                  7373\n",
       "PARKS & RECREATION, ZOO, SWIM POOL              6643\n",
       "OFFICE BUILDING                                 6003\n",
       "DRUG STORE/MEDICAL SUPPLY                       5286\n",
       "CLOTHING STORE                                  5235\n",
       "                                               ...  \n",
       "LIGHT RAIL PLATFORM                              205\n",
       "FIRE STATION                                     205\n",
       "HIGH RISE                                        197\n",
       "VIDEO RENTAL & SALES                             171\n",
       "LIGHT RAIL VEHICLE                               170\n",
       "BOOK, RECORD, STATIONARY, OFFICE SUP.            169\n",
       "SAVINGS AND LOAN INSTITUTIONS                    163\n",
       "VACANT RESTAURANT                                159\n",
       "JAIL/PRISON                                      138\n",
       "LAKE/WATERWAY/BAYOU                              122\n",
       "VACANT INDUSTRIAL/MANUFACTURING/INDUSTRIAL       109\n",
       "RAILROAD TRACK/RIGHT OF WAY                       95\n",
       "CREDIT UNION                                      94\n",
       "VACANT CHURCH/SYNAGOGUE/TEMPLE                    89\n",
       "VACANT HOTEL/MOTEL/ETC.                           84\n",
       "TRAIN TERMINAL                                    82\n",
       "TOLLWAY                                           70\n",
       "VACANT SCHOOL/COLLEGE                             67\n",
       "VACANT GOVERNMENT/PUBLIC BUILDING                 49\n",
       "VACANT HOSPITAL                                   32\n",
       "PACKAGE FACILITY (FEDEX,UPS,DHL)                  25\n",
       "MARINE VEH. SALES, BOATS, SAILBOATS               19\n",
       "ADULT NOVELTY STORE                               15\n",
       "ILLICIT MASSAGE PARLOR OR SPA                     12\n",
       "CONTRA-FLOW/HOV                                    6\n",
       "VACANT BANK                                        5\n",
       "ADULT THEATER/ARCADE                               5\n",
       "CANTINA                                            4\n",
       "MODELING STUDIO                                    2\n",
       "VACANT JAIL/PRISON                                 1\n",
       "Name: Location_Desc, Length: 132, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"Location_Desc\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instances of Crime by Police Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Westside Division              145699\n",
       "North Division                 128402\n",
       "Central Division               125581\n",
       "Northeast Division             107227\n",
       "Southeast Division              89481\n",
       "Midwest Division                80993\n",
       "Northwest Division              78772\n",
       "Southwest Division              77761\n",
       "Clear Lake Division             59088\n",
       "South Central Division          56811\n",
       "South Gessner Division          54144\n",
       "Eastside Division               27581\n",
       "Kingwood Division               11752\n",
       "Airport-IAH Division             4884\n",
       "Special Operations Division      3376\n",
       "Airport-Hobby Division            207\n",
       "Name: Police_Division, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"Police_Division\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of Perpetrators Who Committed n Offenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1080606\n",
       "2       11413\n",
       "3        1758\n",
       "4         254\n",
       "5          81\n",
       "6          47\n",
       "7          16\n",
       "8          10\n",
       "9           5\n",
       "13          4\n",
       "10          3\n",
       "16          3\n",
       "12          2\n",
       "17          2\n",
       "29          1\n",
       "11          1\n",
       "33          1\n",
       "Name: Num_Offenses, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"Num_Offenses\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instances of Crime by Street Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WESTHEIMER                          29602\n",
       "GULF                                15444\n",
       "GESSNER                             15433\n",
       "NORTH                               13584\n",
       "RICHMOND                            13524\n",
       "KATY                                10958\n",
       "SOUTHWEST                           10816\n",
       "MAIN                                10043\n",
       "BISSONNET                           10036\n",
       "NORTHWEST                            9730\n",
       "BELLAIRE                             9415\n",
       "BELLFORT                             8424\n",
       "FM 1960                              8111\n",
       "BEECHNUT                             7807\n",
       "GREENS                               7723\n",
       "FONDREN                              7672\n",
       "POST OAK                             7301\n",
       "TIDWELL                              7001\n",
       "LITTLE YORK                          6884\n",
       "BROADWAY                             6754\n",
       "WEST SAM HOUSTON                     6364\n",
       "EAST                                 6291\n",
       "SHEPHERD                             6065\n",
       "AIRLINE                              5622\n",
       "KIRBY                                5280\n",
       "SAM HOUSTON                          5269\n",
       "HILLCROFT                            4911\n",
       "FANNIN                               4858\n",
       "LOOP                                 4677\n",
       "WILCREST                             4644\n",
       "                                    ...  \n",
       "AUSTIN TRLR A                           1\n",
       "GATLINBURG DR                           1\n",
       "PINEMOOR                                1\n",
       "ROLLING MILL DRIVE                      1\n",
       "WHITE DIAMOND LANE                      1\n",
       "WOODWAY SUITE J                         1\n",
       "STONEWALL COURT                         1\n",
       "CHILTON RD                              1\n",
       "THRUSH DRIVE                            1\n",
       "LAKEVILLE DRIVE                         1\n",
       "N IH 45                                 1\n",
       "RUSH HOLLOW                             1\n",
       "SAVOY DR                                1\n",
       "SABO RD APT 1811                        1\n",
       "E. SABINE                               1\n",
       "MCGOWAN                                 1\n",
       "GROVE WEST BLVD 307                     1\n",
       "GLADE ESTATES,KINGWOOD                  1\n",
       "WILLOWVIEW LN                           1\n",
       "ALICE ST APT                            1\n",
       "ARBOR VILLA                             1\n",
       "SORELLA PVT CT / CITY CENTRE PVT        1\n",
       "ASH CREEK DR                            1\n",
       "WOODS BRIDGE WAY                        1\n",
       "HERMANN DRIVE APT                       1\n",
       "STEEL ST                                1\n",
       "P.O. 924234                             1\n",
       "COLONIAL MAPLE                          1\n",
       "GARDEN ESTATES RD                       1\n",
       "WATER IRIS                              1\n",
       "Name: Street_Name, Length: 24816, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"Street_Name\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency of Crime by Hour Committed (0-23 hrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a94932d68>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crime[\"Hour\"].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instances of Crime by Police Beat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19G10    23473\n",
       "12D10    23128\n",
       "1A20     22591\n",
       "6B60     22027\n",
       "13D20    20751\n",
       "3B10     20083\n",
       "18F20    19140\n",
       "17E10    19100\n",
       "5F30     18068\n",
       "18F30    17933\n",
       "18F40    17841\n",
       "15E40    17813\n",
       "1A30     17800\n",
       "14D20    17373\n",
       "1A10     16926\n",
       "20G30    16911\n",
       "20G50    16891\n",
       "17E40    16791\n",
       "2A50     16345\n",
       "1A50     16129\n",
       "6B10     15784\n",
       "18F50    15678\n",
       "5F40     15174\n",
       "7C20     15045\n",
       "9C40     14663\n",
       "6B30     14157\n",
       "3B50     13894\n",
       "2A30     13887\n",
       "20G10    13758\n",
       "19G40    13356\n",
       "         ...  \n",
       "11H50     2951\n",
       "24C10     2809\n",
       "24C50     2681\n",
       "24C30     2637\n",
       "24C20     2611\n",
       "1A40      2532\n",
       "9C10      2483\n",
       "12D50     2398\n",
       "11H40     2274\n",
       "18F10     2251\n",
       "21I50     2106\n",
       "21I10     1272\n",
       "13D30     1131\n",
       "24C40     1130\n",
       "23J50     1011\n",
       "24C60      746\n",
       "21I30      587\n",
       "21I60      430\n",
       "21I20      316\n",
       "21I70      171\n",
       "21I40      108\n",
       "HCSO        68\n",
       "OOJ         41\n",
       "23J40       28\n",
       "23J30       13\n",
       "UH3P         6\n",
       "23J20        3\n",
       "23J10        2\n",
       "3B20         1\n",
       "7C60         1\n",
       "Name: Police_Beat, Length: 126, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"Police_Beat\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instances of Crime by Repeat Criminals and Police Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Num_Offenses  Police_Division            \n",
       "1             Airport-Hobby Division            196\n",
       "              Airport-IAH Division             4841\n",
       "              Central Division               124603\n",
       "              Clear Lake Division             58447\n",
       "              Eastside Division               27137\n",
       "              Kingwood Division               11646\n",
       "              Midwest Division                80354\n",
       "              North Division                 126538\n",
       "              Northeast Division             105247\n",
       "              Northwest Division              77996\n",
       "              South Central Division          56217\n",
       "              South Gessner Division          53364\n",
       "              Southeast Division              87854\n",
       "              Southwest Division              76966\n",
       "              Special Operations Division      3325\n",
       "              Westside Division              144067\n",
       "2             Airport-Hobby Division              9\n",
       "              Airport-IAH Division               30\n",
       "              Central Division                  852\n",
       "              Clear Lake Division               532\n",
       "              Eastside Division                 367\n",
       "              Kingwood Division                  85\n",
       "              Midwest Division                  552\n",
       "              North Division                   1557\n",
       "              Northeast Division               1666\n",
       "              Northwest Division                668\n",
       "              South Central Division            509\n",
       "              South Gessner Division            649\n",
       "              Southeast Division               1323\n",
       "              Southwest Division                664\n",
       "                                              ...  \n",
       "7             South Gessner Division              1\n",
       "              Southeast Division                  3\n",
       "              Southwest Division                  2\n",
       "              Westside Division                   1\n",
       "8             North Division                      1\n",
       "              Northeast Division                  2\n",
       "              Northwest Division                  1\n",
       "              South Central Division              1\n",
       "              Southeast Division                  2\n",
       "              Southwest Division                  2\n",
       "9             Airport-IAH Division                1\n",
       "              Eastside Division                   1\n",
       "              Northwest Division                  1\n",
       "              Southeast Division                  2\n",
       "10            Central Division                    1\n",
       "              Westside Division                   1\n",
       "11            Clear Lake Division                 1\n",
       "12            Airport-IAH Division                1\n",
       "              Northeast Division                  1\n",
       "13            Northeast Division                  1\n",
       "              Northwest Division                  1\n",
       "              Southwest Division                  1\n",
       "              Westside Division                   1\n",
       "16            Airport-IAH Division                1\n",
       "              Clear Lake Division                 1\n",
       "              Southwest Division                  1\n",
       "17            Northwest Division                  1\n",
       "              Westside Division                   1\n",
       "29            Airport-IAH Division                1\n",
       "33            North Division                      1\n",
       "Length: 123, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime.groupby(['Num_Offenses', 'Police_Division']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency of Crime by Police Division (Bar Chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba4a7fe5f8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "division_counts = crime.groupby(\"Police_Division\").count()\n",
    "division_counts.plot.bar(y=\"RecordID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Offense Type of Crime by Police Division (100% Stacked Bar Chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d70f882be0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "division_counts = crime.groupby([\"Police_Division\",\"Offense_Type\"])[\"RecordID\"].count().unstack(\"Offense_Type\")\n",
    "percentage_division_counts = division_counts.div(division_counts.sum(axis=1), axis=0)\n",
    "percentage_division_counts.plot(kind='bar',stacked=True)\n",
    "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Frequency of Crime by Offense Type and Police Division (Stacked Bar Chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x271a3844e48>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "division_counts = crime.groupby([\"Police_Division\",\"Offense_Type\"])[\"RecordID\"].count().unstack(\"Offense_Type\")\n",
    "division_counts.plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instance of Crime by Zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77036.0    38471\n",
       "77099.0    24891\n",
       "77072.0    23760\n",
       "77092.0    23002\n",
       "77007.0    22862\n",
       "77004.0    22578\n",
       "77063.0    22125\n",
       "77002.0    20875\n",
       "77060.0    20721\n",
       "77074.0    20719\n",
       "77057.0    20067\n",
       "77034.0    19931\n",
       "77082.0    19601\n",
       "77056.0    19301\n",
       "77006.0    19074\n",
       "77009.0    18479\n",
       "77021.0    18371\n",
       "77022.0    17967\n",
       "77042.0    17732\n",
       "77055.0    17007\n",
       "77088.0    15804\n",
       "77077.0    15735\n",
       "77087.0    15534\n",
       "77008.0    15495\n",
       "77026.0    15487\n",
       "77016.0    15008\n",
       "77061.0    14632\n",
       "77096.0    14420\n",
       "77076.0    14399\n",
       "77035.0    14286\n",
       "           ...  \n",
       "77504.0      581\n",
       "77094.0      553\n",
       "77050.0      534\n",
       "77587.0      337\n",
       "77090.0      333\n",
       "77066.0      300\n",
       "77365.0      250\n",
       "77039.0      231\n",
       "77086.0      211\n",
       "77401.0      208\n",
       "77498.0      186\n",
       "77069.0      164\n",
       "77073.0      120\n",
       "77449.0      101\n",
       "77095.0       73\n",
       "77373.0       70\n",
       "77545.0       63\n",
       "77532.0       54\n",
       "77068.0       50\n",
       "77065.0       47\n",
       "77506.0       18\n",
       "77450.0       12\n",
       "77357.0       11\n",
       "77429.0        6\n",
       "77494.0        5\n",
       "77375.0        3\n",
       "77433.0        2\n",
       "77530.0        2\n",
       "77536.0        1\n",
       "77547.0        1\n",
       "Name: zip_codes, Length: 125, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime[\"zip_codes\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choropleth Map of Crime by Zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_crime_data = crime.groupby(\"zip_codes\")\n",
    "crime_by_zipcode = pd.DataFrame(grouped_crime_data.size().reset_index(name = \"crime_frequency\"))\n",
    "crime_by_zipcode[\"zip_codes\"] = crime_by_zipcode[\"zip_codes\"].astype(int).astype(str)\n",
    "houston_map = folium.Map(location=[29.749907,-95.358421],zoom_start=9)\n",
    "houston_map.choropleth(\n",
    "    geo_data=\"../houston_zipcodes.geojson\",\n",
    "    name=\"choropleth\",\n",
    "    data=crime_by_zipcode,\n",
    "    columns=[\"zip_codes\",\"crime_frequency\"],\n",
    "    key_on=\"feature.properties.ZCTA5CE10\",\n",
    "    fill_color=\"YlGn\",\n",
    "    fill_opacity=\"0.7\",\n",
    "    line_opacity=\"0.2\",\n",
    "    legend_name=\"Crime Frequency\",\n",
    "    nan_fill_color=\"White\"\n",
    ")\n",
    "\n",
    "coordinates = {\"name\":[\"Airport-Hobby\",\"Airport-IAH\",\"Central\",\"Clear Lake\",\"Eastside\",\n",
    "                      \"Kingwood\",\"Midwest\",\"North\",\"Northeast\",\"Northwest\",\"South Central\",\n",
    "                      \"South Gessner\",\"Southeast\",\"Southwest\",\"Westside\"],\n",
    "               \"lat\":[29.644100,29.987380,29.765950,29.579730,29.734550,30.054690,29.716660,29.879310,\n",
    "                     29.832180,29.855720,29.742190,29.649460,29.656890,29.636221,29.727830], \n",
    "               \"long\":[-95.514530,-95.342670,-95.371940,-95.106990,-95.290710,-95.188290,-95.511530,-95.446890,\n",
    "                      -95.272690,-95.540280,-95.363080,-95.528549,-95.315560,-95.457939,-95.604210]}\n",
    "police_division_coordinates = pd.DataFrame(coordinates)\n",
    "\n",
    "for i,row in police_division_coordinates.iterrows():\n",
    "    folium.CircleMarker((row[\"lat\"], row[\"long\"]), radius=3, popup=row[\"name\"], weight=2, color='red', fill_color='red', fill_opacity=.5).add_to(houston_map)\n",
    "\n",
    "houston_map.save(\"crime_data.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Model to Predict Frequency of Crime Based on Offense Type and Police Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['1', 'Aggravated Assault', 'Auto Theft', 'Burglary', 'Murder',\n",
       "        'Rape', 'Robbery', 'Theft'], dtype=object),\n",
       " array(['Airport-IAH Division', 'Central Division', 'Clear Lake Division',\n",
       "        'Eastside Division', 'Kingwood Division', 'Midwest Division',\n",
       "        'North Division', 'Northeast Division', 'Northwest Division',\n",
       "        'South Central Division', 'South Gessner Division',\n",
       "        'Southeast Division', 'Southwest Division',\n",
       "        'Special Operations Division', 'Westside Division'], dtype=object)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "crime = crime.dropna()\n",
    "crime2 = crime[[\"Offense_Type\", \"Police_Division\"]]\n",
    "categorical_features = [\"Offense_Type\", \"Police_Division\"]\n",
    "# Create an Encoder for vectorizing our categorical features.\n",
    "enc = OneHotEncoder(handle_unknown=\"ignore\", sparse=False)\n",
    "# Constructs a matrix of 0s and 1s which encode our occupation and work class\n",
    "X_categorical = enc.fit_transform(crime2[categorical_features])\n",
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17573, 0), (17573, 23), (17573, 23))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_numerical = crime2.drop(columns=categorical_features).values.astype(int)\n",
    "X = np.hstack((X_numerical, X_categorical))\n",
    "X_numerical.shape, X_categorical.shape, X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# percent of full dataset to use for test set.  important to restrict \n",
    "test_size = 0.2\n",
    "y = crime[\"Hour\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Srivinay Tummarakota\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(solver=\"lbfgs\")\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy_score': 0.07197724039829304}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score, roc_auc_score\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "{\n",
    "    \"accuracy_score\": accuracy_score(y_test, y_pred)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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