CA1 Group Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41ecef8c",
   "metadata": {},
   "source": [
    "# Predicting type of crime in LA on given Time and location\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a8d1cb",
   "metadata": {},
   "source": [
    "### Dataset Description "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bdc30c",
   "metadata": {},
   "source": [
    "#### Columns\n",
    "    \n",
    "* DR_NO: Division of Records Number: Official file number made up of a 2 digit year, area ID, and 5 digits.\n",
    "* API Field Name: MM/DD/YYYY.\n",
    "* DATE OCC: MM/DD/YYYY.\n",
    "* TIME OCC: In 24 hour military time.\n",
    "* AREA: The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.\n",
    "* AREA NAME: The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for. For example 77th Street Division is located at the intersection of South * * Broadway and 77th Street, serving neighborhoods in South Los Angeles.\n",
    "* Rpt Dist No: A four-digit code that represents a sub-area within a Geographic Area. All crime records reference the \"RD\" that it occurred in for statistical comparisons.\n",
    "* Crm Cd: Indicates the crime committed. (Same as Crime Code 1)\n",
    "* Crm Cd Desc: Defines the Crime Code provided.\n",
    "* Mocodes: Modus Operandi: Activities associated with the suspect in commission of the crime.\n",
    "* Vict Age: Two character numeric.\n",
    "* Vict Sex: F - Female M - Male X - Unknown.\n",
    "* Vict Descent: Descent Code: A - Other Asian B - Black C - Chinese D - Cambodian F - Filipino G - Guamanian H - Hispanic/Latin/Mexican I - American Indian/Alaskan Native J - Japanese K - Korean L - Laotian O - Other P - Pacific Islander S - Samoan U - Hawaiian V - Vietnamese W - White X - Unknown Z - Asian Indian.\n",
    "* Premis Cd: The type of structure, vehicle, or location where the crime took place.\n",
    "* Premis Desc: Defines the Premise Code provided.\n",
    "* Weapon Used Cd: The type of weapon used in the crime.\n",
    "* Weapon Desc: Defines the Weapon Used Code provided.\n",
    "* Status: Status of the case. (IC is the default).\n",
    "* Status DEsc: Defines the Status Code provided.\n",
    "* Crm Cd 1: Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.\n",
    "* Crm Cd 2: May contain a code for an additional crime, less serious than Crime Code 1.\n",
    "* Crm Cd 3: May contain a code for an additional crime, less serious than Crime Code 1.\n",
    "* Crm Cd 4: May contain a code for an additional crime, less serious than Crime Code 1.\n",
    "* LOCATION: Street address of crime incident rounded to the nearest hundred block to maintain anonymity.\n",
    "* Cross Street: Cross Street of rounded Address.\n",
    "* LAT: Latitude.\n",
    "* LON: Longitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c714b20e",
   "metadata": {},
   "source": [
    "### Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3efb2c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0687d88e",
   "metadata": {},
   "source": [
    "The libraries is vital fro my dataset, because it will help us as a student to not restate the code upon implementing our data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cd97dfd",
   "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>DR_NO</th>\n",
       "      <th>Date Rptd</th>\n",
       "      <th>DATE OCC</th>\n",
       "      <th>TIME OCC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AREA NAME</th>\n",
       "      <th>Rpt Dist No</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Crm Cd</th>\n",
       "      <th>Crm Cd Desc</th>\n",
       "      <th>...</th>\n",
       "      <th>Status</th>\n",
       "      <th>Status Desc</th>\n",
       "      <th>Crm Cd 1</th>\n",
       "      <th>Crm Cd 2</th>\n",
       "      <th>Crm Cd 3</th>\n",
       "      <th>Crm Cd 4</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>Cross Street</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10304468</td>\n",
       "      <td>01/08/2020 12:00:00 AM</td>\n",
       "      <td>01/08/2020 12:00:00 AM</td>\n",
       "      <td>2230</td>\n",
       "      <td>3</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>377</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>...</td>\n",
       "      <td>AO</td>\n",
       "      <td>Adult Other</td>\n",
       "      <td>624.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100 W  39TH                         PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0141</td>\n",
       "      <td>-118.2978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190101086</td>\n",
       "      <td>01/02/2020 12:00:00 AM</td>\n",
       "      <td>01/01/2020 12:00:00 AM</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>Central</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>624.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700 S  HILL                         ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0459</td>\n",
       "      <td>-118.2545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201220752</td>\n",
       "      <td>09/16/2020 12:00:00 AM</td>\n",
       "      <td>09/16/2020 12:00:00 AM</td>\n",
       "      <td>1230</td>\n",
       "      <td>12</td>\n",
       "      <td>77th Street</td>\n",
       "      <td>1259</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>745.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700 E  73RD                         ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.9739</td>\n",
       "      <td>-118.2630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191501505</td>\n",
       "      <td>01/01/2020 12:00:00 AM</td>\n",
       "      <td>01/01/2020 12:00:00 AM</td>\n",
       "      <td>1730</td>\n",
       "      <td>15</td>\n",
       "      <td>N Hollywood</td>\n",
       "      <td>1543</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>745.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5400    CORTEEN                      PL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.1685</td>\n",
       "      <td>-118.4019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>191921269</td>\n",
       "      <td>01/01/2020 12:00:00 AM</td>\n",
       "      <td>01/01/2020 12:00:00 AM</td>\n",
       "      <td>415</td>\n",
       "      <td>19</td>\n",
       "      <td>Mission</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>740</td>\n",
       "      <td>VANDALISM - FELONY ($400 &amp; OVER, ALL CHURCH VA...</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>740.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14400    TITUS                        ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.2198</td>\n",
       "      <td>-118.4468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DR_NO               Date Rptd                DATE OCC  TIME OCC  AREA  \\\n",
       "0   10304468  01/08/2020 12:00:00 AM  01/08/2020 12:00:00 AM      2230     3   \n",
       "1  190101086  01/02/2020 12:00:00 AM  01/01/2020 12:00:00 AM       330     1   \n",
       "2  201220752  09/16/2020 12:00:00 AM  09/16/2020 12:00:00 AM      1230    12   \n",
       "3  191501505  01/01/2020 12:00:00 AM  01/01/2020 12:00:00 AM      1730    15   \n",
       "4  191921269  01/01/2020 12:00:00 AM  01/01/2020 12:00:00 AM       415    19   \n",
       "\n",
       "     AREA NAME  Rpt Dist No  Part 1-2  Crm Cd  \\\n",
       "0    Southwest          377         2     624   \n",
       "1      Central          163         2     624   \n",
       "2  77th Street         1259         2     745   \n",
       "3  N Hollywood         1543         2     745   \n",
       "4      Mission         1998         2     740   \n",
       "\n",
       "                                         Crm Cd Desc  ... Status  Status Desc  \\\n",
       "0                           BATTERY - SIMPLE ASSAULT  ...     AO  Adult Other   \n",
       "1                           BATTERY - SIMPLE ASSAULT  ...     IC  Invest Cont   \n",
       "2           VANDALISM - MISDEAMEANOR ($399 OR UNDER)  ...     IC  Invest Cont   \n",
       "3           VANDALISM - MISDEAMEANOR ($399 OR UNDER)  ...     IC  Invest Cont   \n",
       "4  VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...  ...     IC  Invest Cont   \n",
       "\n",
       "  Crm Cd 1 Crm Cd 2  Crm Cd 3 Crm Cd 4  \\\n",
       "0    624.0      NaN       NaN      NaN   \n",
       "1    624.0      NaN       NaN      NaN   \n",
       "2    745.0      NaN       NaN      NaN   \n",
       "3    745.0    998.0       NaN      NaN   \n",
       "4    740.0      NaN       NaN      NaN   \n",
       "\n",
       "                                   LOCATION Cross Street      LAT       LON  \n",
       "0   1100 W  39TH                         PL          NaN  34.0141 -118.2978  \n",
       "1    700 S  HILL                         ST          NaN  34.0459 -118.2545  \n",
       "2    700 E  73RD                         ST          NaN  33.9739 -118.2630  \n",
       "3   5400    CORTEEN                      PL          NaN  34.1685 -118.4019  \n",
       "4  14400    TITUS                        ST          NaN  34.2198 -118.4468  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load data\n",
    "df = pd.read_csv('Crime_Data_from_2020_to_Present.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20bffd49",
   "metadata": {},
   "source": [
    "This is the head of our dataset that visualize the first 5 rows and the 28 columns of our dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a010549",
   "metadata": {},
   "source": [
    "# Columns in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8bbb501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DR_NO', 'Date Rptd', 'DATE OCC', 'TIME OCC', 'AREA', 'AREA NAME',\n",
       "       'Rpt Dist No', 'Part 1-2', 'Crm Cd', 'Crm Cd Desc', 'Mocodes',\n",
       "       'Vict Age', 'Vict Sex', 'Vict Descent', 'Premis Cd', 'Premis Desc',\n",
       "       'Weapon Used Cd', 'Weapon Desc', 'Status', 'Status Desc', 'Crm Cd 1',\n",
       "       'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'LOCATION', 'Cross Street', 'LAT',\n",
       "       'LON'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518a0061",
   "metadata": {},
   "source": [
    "This is the columns of our dataset and this help us to see the names of columns of our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7536868b",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a23f3b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking for data info "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08c43e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 276584 entries, 0 to 276583\n",
      "Data columns (total 28 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   DR_NO           276584 non-null  int64  \n",
      " 1   Date Rptd       276584 non-null  object \n",
      " 2   DATE OCC        276584 non-null  object \n",
      " 3   TIME OCC        276584 non-null  int64  \n",
      " 4   AREA            276584 non-null  int64  \n",
      " 5   AREA NAME       276584 non-null  object \n",
      " 6   Rpt Dist No     276584 non-null  int64  \n",
      " 7   Part 1-2        276584 non-null  int64  \n",
      " 8   Crm Cd          276584 non-null  int64  \n",
      " 9   Crm Cd Desc     276584 non-null  object \n",
      " 10  Mocodes         238591 non-null  object \n",
      " 11  Vict Age        276584 non-null  int64  \n",
      " 12  Vict Sex        240227 non-null  object \n",
      " 13  Vict Descent    240222 non-null  object \n",
      " 14  Premis Cd       276580 non-null  float64\n",
      " 15  Premis Desc     276487 non-null  object \n",
      " 16  Weapon Used Cd  101085 non-null  float64\n",
      " 17  Weapon Desc     101085 non-null  object \n",
      " 18  Status          276584 non-null  object \n",
      " 19  Status Desc     276584 non-null  object \n",
      " 20  Crm Cd 1        276582 non-null  float64\n",
      " 21  Crm Cd 2        22741 non-null   float64\n",
      " 22  Crm Cd 3        776 non-null     float64\n",
      " 23  Crm Cd 4        25 non-null      float64\n",
      " 24  LOCATION        276584 non-null  object \n",
      " 25  Cross Street    49813 non-null   object \n",
      " 26  LAT             276584 non-null  float64\n",
      " 27  LON             276584 non-null  float64\n",
      "dtypes: float64(8), int64(7), object(13)\n",
      "memory usage: 59.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462d70be",
   "metadata": {},
   "source": [
    "We use df.info in order for us to know the number of object, d types and the non null values inside of our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb2b36dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing irrelevant/not meaningfull columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27d3e325",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from discrption of columns data there are some columns don't needed so we drop them\n",
    "col = ['Date Rptd','DR_NO','AREA NAME', 'Mocodes', 'Premis Desc', 'Weapon Desc', 'Status Desc','Crm Cd 1',\n",
    "       'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street','Weapon Used Cd']\n",
    "df = df.drop(col,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0736fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handeling null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddbde1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE OCC        0.000000\n",
       "TIME OCC        0.000000\n",
       "AREA            0.000000\n",
       "Rpt Dist No     0.000000\n",
       "Part 1-2        0.000000\n",
       "Crm Cd          0.000000\n",
       "Crm Cd Desc     0.000000\n",
       "Vict Age        0.000000\n",
       "Vict Sex        0.131450\n",
       "Vict Descent    0.131468\n",
       "Premis Cd       0.000014\n",
       "Status          0.000000\n",
       "LOCATION        0.000000\n",
       "LAT             0.000000\n",
       "LON             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afcb6011",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling victim sex empty rows with mode value\n",
    "df[\"Vict Sex\"].fillna(df[\"Vict Sex\"].mode()[0],inplace=True)\n",
    "\n",
    "# filling victim descent empty rows with \"X\" unknown symbol \n",
    "df[\"Vict Descent\"].fillna(df[\"Vict Descent\"].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b3c2fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the date column to datetime\n",
    "df['DATE OCC'] = pd.to_datetime(df['DATE OCC'], format='%m/%d/%Y %I:%M:%S %p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a450c641",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting year, month, day into separate columns - This would be useful for machine learning model\n",
    "df['year'] = df['DATE OCC'].dt.year\n",
    "df['month'] = df['DATE OCC'].dt.month\n",
    "df['day'] = df['DATE OCC'].dt.day\n",
    "df['day_of_week'] = df['DATE OCC'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a27c02f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting time to string\n",
    "df['TIME OCC'] = df['TIME OCC'].astype('str')\n",
    "\n",
    "# converting the column with 4 digits\n",
    "df['TIME OCC'] = df['TIME OCC'].apply(lambda x: x.zfill(4))\n",
    "\n",
    "# extracting hour and minute\n",
    "df['hour'] = df['TIME OCC'].str[:2]\n",
    "df['minute'] = df['TIME OCC'].str[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e9c963f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE OCC</th>\n",
       "      <th>TIME OCC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>Rpt Dist No</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Crm Cd</th>\n",
       "      <th>Crm Cd Desc</th>\n",
       "      <th>Vict Age</th>\n",
       "      <th>Vict Sex</th>\n",
       "      <th>Vict Descent</th>\n",
       "      <th>...</th>\n",
       "      <th>Status</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>2230</td>\n",
       "      <td>3</td>\n",
       "      <td>377</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>AO</td>\n",
       "      <td>1100 W  39TH                         PL</td>\n",
       "      <td>34.0141</td>\n",
       "      <td>-118.2978</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0330</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>700 S  HILL                         ST</td>\n",
       "      <td>34.0459</td>\n",
       "      <td>-118.2545</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>03</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>1230</td>\n",
       "      <td>12</td>\n",
       "      <td>1259</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>700 E  73RD                         ST</td>\n",
       "      <td>33.9739</td>\n",
       "      <td>-118.2630</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1730</td>\n",
       "      <td>15</td>\n",
       "      <td>1543</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>76</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>5400    CORTEEN                      PL</td>\n",
       "      <td>34.1685</td>\n",
       "      <td>-118.4019</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0415</td>\n",
       "      <td>19</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>740</td>\n",
       "      <td>VANDALISM - FELONY ($400 &amp; OVER, ALL CHURCH VA...</td>\n",
       "      <td>31</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>...</td>\n",
       "      <td>IC</td>\n",
       "      <td>14400    TITUS                        ST</td>\n",
       "      <td>34.2198</td>\n",
       "      <td>-118.4468</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>04</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DATE OCC TIME OCC  AREA  Rpt Dist No  Part 1-2  Crm Cd  \\\n",
       "0 2020-01-08     2230     3          377         2     624   \n",
       "1 2020-01-01     0330     1          163         2     624   \n",
       "2 2020-09-16     1230    12         1259         2     745   \n",
       "3 2020-01-01     1730    15         1543         2     745   \n",
       "4 2020-01-01     0415    19         1998         2     740   \n",
       "\n",
       "                                         Crm Cd Desc  Vict Age Vict Sex  \\\n",
       "0                           BATTERY - SIMPLE ASSAULT        36        F   \n",
       "1                           BATTERY - SIMPLE ASSAULT        25        M   \n",
       "2           VANDALISM - MISDEAMEANOR ($399 OR UNDER)        62        M   \n",
       "3           VANDALISM - MISDEAMEANOR ($399 OR UNDER)        76        F   \n",
       "4  VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...        31        X   \n",
       "\n",
       "  Vict Descent  ...  Status                                  LOCATION  \\\n",
       "0            B  ...      AO   1100 W  39TH                         PL   \n",
       "1            H  ...      IC    700 S  HILL                         ST   \n",
       "2            B  ...      IC    700 E  73RD                         ST   \n",
       "3            W  ...      IC   5400    CORTEEN                      PL   \n",
       "4            X  ...      IC  14400    TITUS                        ST   \n",
       "\n",
       "       LAT       LON  year  month  day  day_of_week hour minute  \n",
       "0  34.0141 -118.2978  2020      1    8    Wednesday   22     30  \n",
       "1  34.0459 -118.2545  2020      1    1    Wednesday   03     30  \n",
       "2  33.9739 -118.2630  2020      9   16    Wednesday   12     30  \n",
       "3  34.1685 -118.4019  2020      1    1    Wednesday   17     30  \n",
       "4  34.2198 -118.4468  2020      1    1    Wednesday   04     15  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e1dc6f",
   "metadata": {},
   "source": [
    "This is the head our that has already the converted columns which is the date and time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d3b398c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a col with Voilent and non voilent crime "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "edd487ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Regular expressions for violent and non-violent crimes\n",
    "violent_patterns = [\n",
    "    r'\\bRAPE\\b', r'\\bASSAULT\\b', r'\\bROBBERY\\b', r'\\bMURDER\\b', r'\\bBATTERY\\b', r'\\bKIDNAPPING\\b', r'\\bARSON\\b', r'\\bMANSLAUGHTER\\b',\n",
    "    r'\\bDEADLY WEAPON\\b', r'\\bORAL COPULATION\\b', r'\\bSEXUAL PENETRATION\\b', r'\\bSTALKING\\b', r'\\bLEWD CONDUCT\\b', r'\\bCHILD ABUSE\\b'\n",
    "]\n",
    "\n",
    "non_violent_patterns = [\n",
    "    r'\\bTHEFT\\b', r'\\bFRAUD\\b', r'\\bBURGLARY\\b', r'\\bVANDALISM\\b', r'\\bTRESPASSING\\b', r'\\bEMBEZZLEMENT\\b', r'\\bFORGERY\\b', r'\\bDISORDERLY CONDUCT\\b',\n",
    "    r'\\bDISRUPTING PEACE\\b', r'\\bCRIMINAL DAMAGE\\b', r'\\bSTOLEN PROPERTY\\b', r'\\bIDENTITY THEFT\\b', r'\\bCREDIT CARD FRAUD\\b', r'\\bBUNCO\\b',\n",
    "    r'\\bPROPERTY DAMAGE\\b', r'\\bCONSPIRACY\\b', r'\\bDOCUMENT FORGERY\\b', r'\\bEXTORTION\\b', r'\\bBRIBERY\\b', r'\\bCONTRIBUTING TO DELINQUENCY\\b',\n",
    "    r'\\bANIMAL CRUELTY\\b', r'\\bILLEGAL DUMPING\\b', r'\\bHUMAN TRAFFICKING\\b', r'\\bINCEST\\b'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b5fa52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Crm Cd Desc\"] = df[\"Crm Cd Desc\"].astype(str)  # Converting the column to string\n",
    "\n",
    "# The following function classifies crimes as violent or non-violent using regular expressions\n",
    "def classify_crime(description):\n",
    "    for pattern in violent_patterns:\n",
    "        if re.search(pattern, description, re.IGNORECASE):\n",
    "            return 'violent'\n",
    "    for pattern in non_violent_patterns:\n",
    "        if re.search(pattern, description, re.IGNORECASE):\n",
    "            return 'non-violent'\n",
    "    return 'unknown'\n",
    "\n",
    "# Classify the crimes\n",
    "df[\"Crime\"] = df[\"Crm Cd Desc\"].apply(classify_crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92fa69c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crime\n",
       "non-violent    135573\n",
       "violent         78231\n",
       "unknown         62780\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Crime\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38071db5",
   "metadata": {},
   "source": [
    "We used this to see the numbers of non-violent, violent, unknown crime. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1b4a241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping rows with 'unknown' classification\n",
    "df = df[df[\"Crime\"] != 'unknown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1433c767",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AREA'] = df['AREA'].astype('category')\n",
    "df['Rpt Dist No'] = df['Rpt Dist No'].astype('category')\n",
    "df['Premis Cd'] = df['Premis Cd'].astype('category')\n",
    "df['hour'] = df['hour'].astype('category')\n",
    "df['minute'] = df['minute'].astype('category')\n",
    "df['day'] = df['day'].astype('category')\n",
    "df['month'] = df['month'].astype('category')\n",
    "df['Status'] = df['Status'].astype('category')\n",
    "df['Part 1-2'] = df['Part 1-2'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02b8cdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract block number from location so we can add this to our model\n",
    "\n",
    "import re\n",
    "\n",
    "# regular expression patterns\n",
    "pattern_block_number = r'^\\d+'\n",
    "\n",
    "df['block'] = df['LOCATION'].apply(lambda x: re.findall(pattern_block_number, x)[0] if re.findall(pattern_block_number, x) else '')\n",
    "\n",
    "# replacing empty strings with NaN\n",
    "df['block'] = df['block'].replace('', pd.NA)\n",
    "\n",
    "# fill missing values with mode\n",
    "df['block'] = df['block'].fillna(df['block'].mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88355e49",
   "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>DATE OCC</th>\n",
       "      <th>TIME OCC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>Rpt Dist No</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Crm Cd</th>\n",
       "      <th>Crm Cd Desc</th>\n",
       "      <th>Vict Age</th>\n",
       "      <th>Vict Sex</th>\n",
       "      <th>Vict Descent</th>\n",
       "      <th>...</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>Crime</th>\n",
       "      <th>block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>2230</td>\n",
       "      <td>3</td>\n",
       "      <td>377</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0141</td>\n",
       "      <td>-118.2978</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>violent</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0330</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>BATTERY - SIMPLE ASSAULT</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0459</td>\n",
       "      <td>-118.2545</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>03</td>\n",
       "      <td>30</td>\n",
       "      <td>violent</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-16</td>\n",
       "      <td>1230</td>\n",
       "      <td>12</td>\n",
       "      <td>1259</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>33.9739</td>\n",
       "      <td>-118.2630</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>non-violent</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1730</td>\n",
       "      <td>15</td>\n",
       "      <td>1543</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>76</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>...</td>\n",
       "      <td>34.1685</td>\n",
       "      <td>-118.4019</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>non-violent</td>\n",
       "      <td>5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0415</td>\n",
       "      <td>19</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>740</td>\n",
       "      <td>VANDALISM - FELONY ($400 &amp; OVER, ALL CHURCH VA...</td>\n",
       "      <td>31</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>...</td>\n",
       "      <td>34.2198</td>\n",
       "      <td>-118.4468</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>04</td>\n",
       "      <td>15</td>\n",
       "      <td>non-violent</td>\n",
       "      <td>14400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    DATE OCC TIME OCC AREA Rpt Dist No Part 1-2  Crm Cd  \\\n",
       "0 2020-01-08     2230    3         377        2     624   \n",
       "1 2020-01-01     0330    1         163        2     624   \n",
       "2 2020-09-16     1230   12        1259        2     745   \n",
       "3 2020-01-01     1730   15        1543        2     745   \n",
       "4 2020-01-01     0415   19        1998        2     740   \n",
       "\n",
       "                                         Crm Cd Desc  Vict Age Vict Sex  \\\n",
       "0                           BATTERY - SIMPLE ASSAULT        36        F   \n",
       "1                           BATTERY - SIMPLE ASSAULT        25        M   \n",
       "2           VANDALISM - MISDEAMEANOR ($399 OR UNDER)        62        M   \n",
       "3           VANDALISM - MISDEAMEANOR ($399 OR UNDER)        76        F   \n",
       "4  VANDALISM - FELONY ($400 & OVER, ALL CHURCH VA...        31        X   \n",
       "\n",
       "  Vict Descent  ...      LAT       LON  year  month  day  day_of_week hour  \\\n",
       "0            B  ...  34.0141 -118.2978  2020      1    8    Wednesday   22   \n",
       "1            H  ...  34.0459 -118.2545  2020      1    1    Wednesday   03   \n",
       "2            B  ...  33.9739 -118.2630  2020      9   16    Wednesday   12   \n",
       "3            W  ...  34.1685 -118.4019  2020      1    1    Wednesday   17   \n",
       "4            X  ...  34.2198 -118.4468  2020      1    1    Wednesday   04   \n",
       "\n",
       "  minute        Crime  block  \n",
       "0     30      violent   1100  \n",
       "1     30      violent    700  \n",
       "2     30  non-violent    700  \n",
       "3     30  non-violent   5400  \n",
       "4     15  non-violent  14400  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "222e222f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting features for model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "451a8d24",
   "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>AREA</th>\n",
       "      <th>block</th>\n",
       "      <th>Premis Cd</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>Crime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>501.0</td>\n",
       "      <td>34.0141</td>\n",
       "      <td>-118.2978</td>\n",
       "      <td>22</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>102.0</td>\n",
       "      <td>34.0459</td>\n",
       "      <td>-118.2545</td>\n",
       "      <td>03</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>700</td>\n",
       "      <td>502.0</td>\n",
       "      <td>33.9739</td>\n",
       "      <td>-118.2630</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>5400</td>\n",
       "      <td>502.0</td>\n",
       "      <td>34.1685</td>\n",
       "      <td>-118.4019</td>\n",
       "      <td>17</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>14400</td>\n",
       "      <td>409.0</td>\n",
       "      <td>34.2198</td>\n",
       "      <td>-118.4468</td>\n",
       "      <td>04</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AREA  block Premis Cd      LAT       LON hour day_of_week        Crime\n",
       "0    3   1100     501.0  34.0141 -118.2978   22   Wednesday      violent\n",
       "1    1    700     102.0  34.0459 -118.2545   03   Wednesday      violent\n",
       "2   12    700     502.0  33.9739 -118.2630   12   Wednesday  non-violent\n",
       "3   15   5400     502.0  34.1685 -118.4019   17   Wednesday  non-violent\n",
       "4   19  14400     409.0  34.2198 -118.4468   04   Wednesday  non-violent"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['AREA','block','Premis Cd','LAT','LON','hour','day_of_week','Crime']\n",
    "\n",
    "df = df[features]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48d9e35b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8858bb3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Grouping the DataFrame by 'Crime' and counting the occurrences\n",
    "crime_counts = df.groupby('Crime').size().sort_values(ascending=True)\n",
    "\n",
    "# Creating a donut chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title('Total Voilent and Non Voilent Crimes')\n",
    "\n",
    "# Data for the donut chart\n",
    "colors = plt.cm.Paired(range(len(crime_counts)))\n",
    "wedges, texts, autotexts = plt.pie(crime_counts, labels=crime_counts.index, autopct='%1.1f%%',\n",
    "                                   startangle=90, pctdistance=0.85, wedgeprops=dict(width=0.4, edgecolor='w'), colors=colors)\n",
    "\n",
    "# Increase the size of percentage labels inside the donut chart\n",
    "for autotext in autotexts:\n",
    "    autotext.set(size=10)\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "plt.setp(wedges, width=0.4)\n",
    "plt.gca().set_aspect('equal')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b0e438a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Convert 'hour' column to integers\n",
    "df['hour'] = df['hour'].astype(int)\n",
    "\n",
    "# Assuming df is your DataFrame and 'hour' is the column you want to plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df['hour'], bins=24, edgecolor='black')\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Total Crime of Hours')\n",
    "plt.xticks(range(0, max(df['hour']) + 1))  # Sorting x-ticks from 0 to max hour\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3ce3fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Bar Chart visualize for Crime Types\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.title('Total crime by day of week')\n",
    "plt.ylabel('day_of_week')\n",
    "plt.xlabel('Amount of Crimes')\n",
    "\n",
    "df.groupby([df['day_of_week']]).size().sort_values(ascending=True).plot(kind='barh')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e390167f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "top_n = 10\n",
    "# top 10 categories\n",
    "top_categories = df['Premis Cd'].value_counts().nlargest(top_n).index\n",
    "\n",
    "# Filtering the DataFrame for the top 10 categories\n",
    "df_top_categories = df[df['Premis Cd'].isin(top_categories)]\n",
    "\n",
    "# Count plot for the top 10 categories\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=df_top_categories, x='Premis Cd', order=top_categories)\n",
    "plt.xlabel('Premis Cd')\n",
    "plt.ylabel('Total Crime')\n",
    "plt.title('Premise code with most crimes')\n",
    "plt.xticks(rotation=45)  # Rotating x-axis labels for better visibility\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0caf514f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing data for ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f2334e58",
   "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>AREA</th>\n",
       "      <th>block</th>\n",
       "      <th>Premis Cd</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>Crime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1100</td>\n",
       "      <td>501.0</td>\n",
       "      <td>34.0141</td>\n",
       "      <td>-118.2978</td>\n",
       "      <td>22</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>102.0</td>\n",
       "      <td>34.0459</td>\n",
       "      <td>-118.2545</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>700</td>\n",
       "      <td>502.0</td>\n",
       "      <td>33.9739</td>\n",
       "      <td>-118.2630</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>5400</td>\n",
       "      <td>502.0</td>\n",
       "      <td>34.1685</td>\n",
       "      <td>-118.4019</td>\n",
       "      <td>17</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>14400</td>\n",
       "      <td>409.0</td>\n",
       "      <td>34.2198</td>\n",
       "      <td>-118.4468</td>\n",
       "      <td>4</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>non-violent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AREA  block Premis Cd      LAT       LON  hour day_of_week        Crime\n",
       "0    3   1100     501.0  34.0141 -118.2978    22   Wednesday      violent\n",
       "1    1    700     102.0  34.0459 -118.2545     3   Wednesday      violent\n",
       "2   12    700     502.0  33.9739 -118.2630    12   Wednesday  non-violent\n",
       "3   15   5400     502.0  34.1685 -118.4019    17   Wednesday  non-violent\n",
       "4   19  14400     409.0  34.2198 -118.4468     4   Wednesday  non-violent"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dbb0d7d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a dictionary mapping weekday names to integers and target variable ie. Crime to 0 and 1 - Label encoding\n",
    "weekday_to_int = {\n",
    "    'Sunday': 0,\n",
    "    'Monday': 1,\n",
    "    'Tuesday': 2,\n",
    "    'Wednesday': 3,\n",
    "    'Thursday': 4,\n",
    "    'Friday': 5,\n",
    "    'Saturday': 6\n",
    "}\n",
    "\n",
    "# using .map() function to replace the day_of_week and crime column with integers\n",
    "df['day_of_week'] = df['day_of_week'].map(weekday_to_int)\n",
    "df['day_of_week'] = df['day_of_week'].astype('category')\n",
    "\n",
    "crime_map = {\n",
    "    'non-violent': 0,\n",
    "    'violent': 1,\n",
    "}\n",
    "\n",
    "df['Crime'] = df['Crime'].map(crime_map)\n",
    "df['Crime'] = df['Crime'].astype('category')\n",
    "\n",
    "# Reducing the data\n",
    "df = df.head(50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b28b787e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "552edee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating the target and feature ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "33f6b95f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(columns=['Crime'])\n",
    "y = df['Crime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cd15d42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Cheking for unique count of voilent and non voilent crimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "66671dc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crime\n",
       "0    34018\n",
       "1    15982\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc3b20b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here there are total 135573 'non-voilent' and 135573 'voilent' crime\n",
    "# Hence the data is imbalance so to fix it we use under-sampling technique to balance the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a80a2989",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name '_MissingValues' from 'sklearn.utils._param_validation' (C:\\Users\\ladem\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[39], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimblearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01munder_sampling\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomUnderSampler\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Create an instance of RandomUnderSampler\u001b[39;00m\n\u001b[0;32m      4\u001b[0m rus \u001b[38;5;241m=\u001b[39m RandomUnderSampler(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\imblearn\\__init__.py:52\u001b[0m\n\u001b[0;32m     48\u001b[0m     sys\u001b[38;5;241m.\u001b[39mstderr\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPartial import of imblearn during the build process.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;66;03m# We are not importing the rest of scikit-learn during the build\u001b[39;00m\n\u001b[0;32m     50\u001b[0m     \u001b[38;5;66;03m# process, as it may not be compiled yet\u001b[39;00m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 52\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m     53\u001b[0m         combine,\n\u001b[0;32m     54\u001b[0m         ensemble,\n\u001b[0;32m     55\u001b[0m         exceptions,\n\u001b[0;32m     56\u001b[0m         metrics,\n\u001b[0;32m     57\u001b[0m         over_sampling,\n\u001b[0;32m     58\u001b[0m         pipeline,\n\u001b[0;32m     59\u001b[0m         tensorflow,\n\u001b[0;32m     60\u001b[0m         under_sampling,\n\u001b[0;32m     61\u001b[0m         utils,\n\u001b[0;32m     62\u001b[0m     )\n\u001b[0;32m     63\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_version\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__\n\u001b[0;32m     64\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FunctionSampler\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\imblearn\\combine\\__init__.py:5\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"The :mod:`imblearn.combine` provides methods which combine\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mover-sampling and under-sampling.\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_smote_enn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SMOTEENN\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_smote_tomek\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SMOTETomek\n\u001b[0;32m      8\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSMOTEENN\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSMOTETomek\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\imblearn\\combine\\_smote_enn.py:12\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m clone\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_X_y\n\u001b[1;32m---> 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseSampler\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mover_sampling\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SMOTE\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mover_sampling\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseOverSampler\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\imblearn\\base.py:21\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmulticlass\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_classification_targets\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_sampling_strategy, check_target_type\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m validate_parameter_constraints\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ArraysTransformer\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mSamplerMixin\u001b[39;00m(BaseEstimator, metaclass\u001b[38;5;241m=\u001b[39mABCMeta):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\imblearn\\utils\\_param_validation.py:908\u001b[0m\n\u001b[0;32m    906\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m generate_valid_param  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[0;32m    907\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m validate_parameter_constraints  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m--> 908\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m    909\u001b[0m     HasMethods,\n\u001b[0;32m    910\u001b[0m     Hidden,\n\u001b[0;32m    911\u001b[0m     Interval,\n\u001b[0;32m    912\u001b[0m     Options,\n\u001b[0;32m    913\u001b[0m     StrOptions,\n\u001b[0;32m    914\u001b[0m     _ArrayLikes,\n\u001b[0;32m    915\u001b[0m     _Booleans,\n\u001b[0;32m    916\u001b[0m     _Callables,\n\u001b[0;32m    917\u001b[0m     _CVObjects,\n\u001b[0;32m    918\u001b[0m     _InstancesOf,\n\u001b[0;32m    919\u001b[0m     _IterablesNotString,\n\u001b[0;32m    920\u001b[0m     _MissingValues,\n\u001b[0;32m    921\u001b[0m     _NoneConstraint,\n\u001b[0;32m    922\u001b[0m     _PandasNAConstraint,\n\u001b[0;32m    923\u001b[0m     _RandomStates,\n\u001b[0;32m    924\u001b[0m     _SparseMatrices,\n\u001b[0;32m    925\u001b[0m     _VerboseHelper,\n\u001b[0;32m    926\u001b[0m     make_constraint,\n\u001b[0;32m    927\u001b[0m     validate_params,\n\u001b[0;32m    928\u001b[0m )\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name '_MissingValues' from 'sklearn.utils._param_validation' (C:\\Users\\ladem\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py)"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Create an instance of RandomUnderSampler\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "\n",
    "# Resample the data using RandomUnderSampler\n",
    "x_resampled, y_resampled = rus.fit_resample(x, y)                                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afff075b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = x_resampled.join(y_resampled)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad43ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Crime.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f7069b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking for correlation between independent variables \n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00781f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping LON as it is highly correlated with LAT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9543af28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['LON'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a487a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8635819e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding all columns except for LAT, LON, and Crime\n",
    "df_encoded = pd.get_dummies(df[['AREA','block','Premis Cd','hour','day_of_week']],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80816409",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenating the one-hot encoded columns with the original dataframe\n",
    "new_df = pd.concat([df[['LAT', 'Crime']], df_encoded], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d061fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36d4d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33259f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df.drop(columns=['Crime'])\n",
    "y = new_df[\"Crime\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7b8fdeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score,f1_score,classification_report,confusion_matrix,roc_curve, auc\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c95a93a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spliting the dataset to Training Set & Test Set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01984d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(clf, X_train, y_train):\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "def predict_data(clf, X_test, y_test):\n",
    "    # Predict labels using the classifier\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    # Calculate F1 score\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    # Generate confusion matrix\n",
    "    con_matrix = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    # Generate classification report\n",
    "    class_report = classification_report(y_test, y_pred)\n",
    "    \n",
    "    # Calculate ROC curve and AUC\n",
    "    y_score = clf.predict_proba(X_test)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_score)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    return f1, acc, con_matrix, class_report, fpr, tpr, roc_auc\n",
    "\n",
    "def model(clf, X_train, y_train, X_test, y_test):\n",
    "    train_model(clf, X_train, y_train)\n",
    "    \n",
    "    f1, acc, con_matrix, class_report, fpr, tpr, roc_auc = predict_data(clf, X_test, y_test)\n",
    "    print(\"Test Data:\")\n",
    "    print(\"-\" * 20)\n",
    "    print(\"F1 Score: {}\".format(f1))\n",
    "    print(\"Accuracy: {}\".format(acc))\n",
    "    print(\"Confusion Matrix:\")\n",
    "    print(con_matrix)\n",
    "    print(\"Classification Report:\")\n",
    "    print(class_report)\n",
    "    print(\"-\" * 20)\n",
    "\n",
    "    # Plot ROC curve\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be7b5331",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Logistic Regression\")\n",
    "print(\"-\" * 20)\n",
    "lrc = LogisticRegression()\n",
    "model(lrc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f60dfcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nDecision Tree\")\n",
    "print(\"-\" * 20)\n",
    "dtc = DecisionTreeClassifier()\n",
    "model(dtc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32e98edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\nRandom Forest Classifier\")\n",
    "print(\"-\" * 20)\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "model(rfc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78140532",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter tuning the Logistic model using Grid search cv and applying cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d4ccbaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import make_scorer, f1_score\n",
    "\n",
    "# Define F1 score as the evaluation metric\n",
    "f1_scorer = make_scorer(f1_score)\n",
    "\n",
    "# Random Forest Classifier (rfc)\n",
    "rfc_params = {'n_estimators': [100, 150]}\n",
    "rfc_grid = GridSearchCV(rfc, rfc_params, cv=5, scoring=f1_scorer)\n",
    "rfc_grid.fit(X_train, y_train)\n",
    "\n",
    "# Best parameters and results\n",
    "print(\"Best Parameters for Random Forest: \", rfc_grid.best_params_)\n",
    "print(\"Cross-validated F1 Score: {:.2f}\".format(rfc_grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "313030b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "def plot_top_features(clf, X, top_n=5):\n",
    "    \n",
    "    # Selecting top features using SelectFromModel\n",
    "    sfm = SelectFromModel(clf, prefit=True, threshold=-np.inf, max_features=top_n)\n",
    "    X_transformed = sfm.transform(X)\n",
    "    \n",
    "    feature_indices = sfm.get_support(indices=True)\n",
    "    \n",
    "    # feature names\n",
    "    feature_names = X.columns[feature_indices]\n",
    "    \n",
    "    # feature importances\n",
    "    if hasattr(clf, 'coef_'):\n",
    "        feature_importances = clf.coef_.flatten()[feature_indices]\n",
    "    elif hasattr(clf, 'feature_importances_'):\n",
    "        feature_importances = clf.feature_importances_[feature_indices]\n",
    "    else:\n",
    "        raise ValueError(\"Feature importances are not available for this type of classifier.\")\n",
    "    \n",
    "    # Sorting features by importance values\n",
    "    sorted_indices = feature_importances.argsort()\n",
    "    feature_names = feature_names[sorted_indices]\n",
    "    feature_importances = feature_importances[sorted_indices]\n",
    "\n",
    "    # Plot \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(range(top_n), feature_importances[-top_n:], align='center')\n",
    "    plt.yticks(range(top_n), feature_names[-top_n:])\n",
    "    plt.xlabel('Feature Importance')\n",
    "    plt.title(f'Top {top_n} Features Contributing to Crime Type Prediction')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae7d690d",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_top_features(rfc_grid.best_estimator_, X, top_n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf1e972",
   "metadata": {},
   "source": [
    "### Implementing PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a5bac1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa29074",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb36444",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cd8482b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding the target variable 'Crime'\n",
    "X = df.drop('Crime', axis=1) \n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_standardized = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d515845d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X_imputed = imputer.fit_transform(X_standardized)\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_standardized = scaler.fit_transform(X_imputed)\n",
    "\n",
    "# Applying PCA\n",
    "pca = PCA(n_components=0.95)\n",
    "X_pca = pca.fit_transform(X_standardized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c841bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the number of components after PCA\n",
    "print(\"Number of components after PCA:\", pca.n_components_)\n",
    "\n",
    "# Now X_pca contains the reduced feature matrix\n",
    "\n",
    "# Optional: Visualize the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "cumulative_variance_ratio = explained_variance_ratio.cumsum()\n",
    "\n",
    "plt.plot(cumulative_variance_ratio, marker='o')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cumulative Explained Variance Ratio')\n",
    "plt.title('Explained Variance Ratio with PCA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53bcc5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Using PCA features and dependent variable to train test the model\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize Random Forest classifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = rf_classifier.predict(X_test)\n",
    "\n",
    "# Check accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce0f571",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "In this project, our primary aim was to develop accurate crime prediction models, with a specific focus on binary classification – differentiating between violent and non-violent crimes. \n",
    "\n",
    "We assessed three models: the Random Forest Classifier, Logistic Classifier, and Decision Tree model. By analyzing historical crime data and training these machine learning models, we intended to equip law enforcement agencies with effective tools for distinguishing between these two major crime categories, aiding strategic decision-making and targeted interventions for crime prevention.\n",
    "\n",
    "Among the evaluated models, the Random Forest Classifier emerged as the top-performing model, achieving an accuracy of 69%. \n",
    "\n",
    "The Logistic Classifier consistently demonstrated competitive accuracy levels around 68%. However, the Decision Tree model exhibited relatively lower accuracy, standing at 64%.\n",
    "\n",
    "This binary classification approach provides law enforcement agencies with robust tools to categorize crimes based on severity, enabling targeted interventions and optimized resource allocation. \n",
    "\n",
    "While the Random Forest Classifier proved to be the most accurate in this context, the Logistic Classifier also displayed reliable performance. The Decision Tree model, while less accurate, still contributes valuable insights.\n",
    "\n",
    "In conclusion, our project successfully developed accurate crime prediction models for binary classification, emphasizing the distinction between violent and non-violent crimes. \n",
    "\n",
    "The top-performing models, particularly the Random Forest Classifier, demonstrate promising accuracy levels and hold the potential for enhancing the effectiveness of crime prevention strategies, resource allocation, and response mechanisms. \n",
    "\n",
    "Future research can focus on refining these models, exploring additional features, and incorporating real-time data to ensure their continued relevance and applicability in the evolving landscape of crime analysis.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78a3400c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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