crime_in_Vancouver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scientist Nanodegree/ Udacity\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# <center> Crime in Vancouver (Canada) 2003-2017 </center>\n",
    "\n",
    "\n",
    "<img src=\"https://i.cbc.ca/1.4429974.1533063033!/fileImage/httpImage/image.jpg_gen/derivatives/16x9_780/stock-vancouver-police-department-vpd-cruiser-traffic-stop-crime-liquor-laws.jpg\" width=\"500\" height=\"300\">\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Business Understanding\n",
    "\n",
    "### Crimes in Vancouver have become a very serious issue, it can be seen that the crimes rate in Vancouver has slightly increased in the past three years, so in this project we are going to investigate and analyses the dataset crims in Vancouver(Canada) between 2003 to 2017 which to find the reasons for committing these crimes, and to try to find a solution.\n",
    "\n",
    "\n",
    "### __I am going to perform a consideration on crims in Vancouver Dataset from Kaggle__.\n",
    "\n",
    "### In order to analyse the dataset I have come up with some questions which seem are important in this project:\n",
    "\n",
    "\n",
    "### __Questions__                  \n",
    "#### __Q1/Which year has the highest crime rate in Vancouver?__\n",
    " #### __Q2/What is the most committed type of crime between year 2003 to 2017? What is the correlation between the type of crime and Time?__ \n",
    "#### __Q3/Find the corrleation between the type of crime and Time?__\n",
    "\n",
    "________________________________________________________________________________________________________________________________\n",
    "\n",
    "## Columns description:\n",
    "\n",
    "| __Column name__ | __Description__  |\n",
    "|-------------|---------------|\n",
    "|-------------|---------------|\n",
    "|   TYPE  | Type of Crime|\n",
    "|   YEAR | Year when the reported crime activity occurred|\n",
    "|   MONTH  | Month when the reported crime activity occurred|\n",
    "|   DAY  | Day when the reported crime activity occurred|\n",
    "|   HOUR  | Hour when the reported crime activity occurred|\n",
    "|   MINUTE  | Minute when the reported crime activity occurred|\n",
    "|   HUNDRED_BLOCK  | Generalized location of the report crime activity|\n",
    "|   NEIGHBOURHOOD | Neighbourhood where the reported crime activity occurred|\n",
    "|   X | Coordinate values projected in UTM Zone 10|\n",
    "|   Y | Coordinate values projected in UTM Zone 10|\n",
    "|   Latitude | Coordinate values converted to Latitude|\n",
    "|   Longitude| Coordinate values converted to Longitude|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imported the libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding\n",
    "\n",
    "##### This project will be using dataset from Kaggle and in this section we will understand this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "df=pd.read_csv('crime.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Data_understanding():\n",
    "    \n",
    "    ' This function can only use to read the dataset and getting information about the dataset where are working with'\n",
    "    #print the top 30 rows \n",
    "    print('\\n\\nExplaring the dataframe:\\n\\n',df.head(31))\n",
    "    \n",
    "    # getting the basic information about this data\n",
    "    print('\\n\\nThe dataset describtion is:\\n\\n',df.describe())\n",
    "    \n",
    "    # counting the number of rows and columns\n",
    "    number_of_rows= df.shape[0]\n",
    "    number_of_columns=df.shape[1]\n",
    "    print('\\n\\nThe number of rows:\\n\\n',number_of_rows)\n",
    "    print('\\n\\nThe number of columns:\\n\\n',number_of_columns)\n",
    "    \n",
    "    # Getting information about the coulmns \n",
    "    print('\\n\\nColumn_Names:\\n\\n',df.columns)\n",
    "    \n",
    "    return Data_understanding   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Explaring the dataframe:\n",
      "\n",
      "                                  TYPE  YEAR  MONTH  DAY  HOUR  MINUTE  \\\n",
      "0                         Other Theft  2003      5   12  16.0    15.0   \n",
      "1                         Other Theft  2003      5    7  15.0    20.0   \n",
      "2                         Other Theft  2003      4   23  16.0    40.0   \n",
      "3                         Other Theft  2003      4   20  11.0    15.0   \n",
      "4                         Other Theft  2003      4   12  17.0    45.0   \n",
      "5                         Other Theft  2003      3   26  20.0    45.0   \n",
      "6   Break and Enter Residential/Other  2003      3   10  12.0     0.0   \n",
      "7                            Mischief  2003      6   28   4.0    13.0   \n",
      "8                         Other Theft  2003      2   16   9.0     2.0   \n",
      "9   Break and Enter Residential/Other  2003      7    9  18.0    15.0   \n",
      "10                        Other Theft  2003      1   31  19.0    45.0   \n",
      "11                           Mischief  2003      9   27   1.0     0.0   \n",
      "12  Break and Enter Residential/Other  2003      4   19  18.0     0.0   \n",
      "13  Break and Enter Residential/Other  2003      9   24  18.0    30.0   \n",
      "14  Break and Enter Residential/Other  2003     11    5   8.0    12.0   \n",
      "15         Break and Enter Commercial  2003      9   26   2.0    30.0   \n",
      "16  Break and Enter Residential/Other  2003     10   21  10.0     0.0   \n",
      "17                        Other Theft  2003      1   25  12.0    30.0   \n",
      "18           Offence Against a Person  2003      2   12   NaN     NaN   \n",
      "19                        Other Theft  2003      1    9   6.0    45.0   \n",
      "20                        Other Theft  2003      4   30  13.0     6.0   \n",
      "21                        Other Theft  2003     12   12  15.0    50.0   \n",
      "22                        Other Theft  2003      3    7  16.0    15.0   \n",
      "23           Offence Against a Person  2003      4    4   NaN     NaN   \n",
      "24                           Mischief  2003      1   23  23.0     0.0   \n",
      "25                           Mischief  2003      4    1   0.0     0.0   \n",
      "26                           Mischief  2003      6   29   0.0    30.0   \n",
      "27                           Mischief  2003     10   16  14.0    10.0   \n",
      "28                        Other Theft  2003      1   28  10.0    30.0   \n",
      "29                 Theft from Vehicle  2003      8    5  12.0    25.0   \n",
      "30                 Theft from Vehicle  2003      6   17  16.0    15.0   \n",
      "\n",
      "                HUNDRED_BLOCK              NEIGHBOURHOOD          X  \\\n",
      "0            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "1            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "2            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "3            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "4            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "5            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "6           63XX WILTSHIRE ST                 Kerrisdale  489325.58   \n",
      "7             40XX W 19TH AVE          Dunbar-Southlands  485903.09   \n",
      "8            9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "9              18XX E 3RD AVE         Grandview-Woodland  495078.19   \n",
      "10           9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "11            40XX W 21ST AVE          Dunbar-Southlands  485852.96   \n",
      "12             18XX E 3RD AVE         Grandview-Woodland  495093.69   \n",
      "13             18XX E 3RD AVE         Grandview-Woodland  495103.82   \n",
      "14            63XX WINDSOR ST                     Sunset  493790.48   \n",
      "15            10XX ALBERNI ST                   West End  491067.65   \n",
      "16             18XX E 3RD AVE         Grandview-Woodland  495119.32   \n",
      "17           9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "18  OFFSET TO PROTECT PRIVACY                        NaN       0.00   \n",
      "19           9XX TERMINAL AVE                 Strathcona  493906.50   \n",
      "20             9XX SEYMOUR ST  Central Business District  491205.19   \n",
      "21             9XX SEYMOUR ST  Central Business District  491143.26   \n",
      "22              9XX ROBSON ST  Central Business District  491132.15   \n",
      "23  OFFSET TO PROTECT PRIVACY                        NaN       0.00   \n",
      "24            40XX W 27TH AVE          Dunbar-Southlands  485822.32   \n",
      "25            40XX W 27TH AVE          Dunbar-Southlands  485855.58   \n",
      "26            40XX W 27TH AVE          Dunbar-Southlands  485896.98   \n",
      "27            40XX W 27TH AVE          Dunbar-Southlands  485896.98   \n",
      "28              9XX NICOLA ST                   West End  490245.93   \n",
      "29            10XX CARDERO ST                   West End  490148.05   \n",
      "30           11XX MAINLAND ST  Central Business District  491144.35   \n",
      "\n",
      "             Y   Latitude   Longitude  \n",
      "0   5457452.47  49.269802 -123.083763  \n",
      "1   5457452.47  49.269802 -123.083763  \n",
      "2   5457452.47  49.269802 -123.083763  \n",
      "3   5457452.47  49.269802 -123.083763  \n",
      "4   5457452.47  49.269802 -123.083763  \n",
      "5   5457452.47  49.269802 -123.083763  \n",
      "6   5452817.95  49.228051 -123.146610  \n",
      "7   5455883.77  49.255559 -123.193725  \n",
      "8   5457452.47  49.269802 -123.083763  \n",
      "9   5457221.38  49.267734 -123.067654  \n",
      "10  5457452.47  49.269802 -123.083763  \n",
      "11  5455684.11  49.253762 -123.194407  \n",
      "12  5457230.31  49.267814 -123.067441  \n",
      "13  5457221.02  49.267731 -123.067302  \n",
      "14  5452630.90  49.226430 -123.085283  \n",
      "15  5459114.22  49.284715 -123.122824  \n",
      "16  5457229.95  49.267811 -123.067089  \n",
      "17  5457452.47  49.269802 -123.083763  \n",
      "18        0.00   0.000000    0.000000  \n",
      "19  5457452.47  49.269802 -123.083763  \n",
      "20  5458520.26  49.279374 -123.120920  \n",
      "21  5458445.58  49.278701 -123.121770  \n",
      "22  5458889.26  49.282692 -123.121932  \n",
      "23        0.00   0.000000    0.000000  \n",
      "24  5455051.83  49.248074 -123.194806  \n",
      "25  5455060.69  49.248154 -123.194349  \n",
      "26  5455051.18  49.248070 -123.193780  \n",
      "27  5455051.18  49.248070 -123.193780  \n",
      "28  5459326.44  49.286611 -123.134128  \n",
      "29  5459395.79  49.287233 -123.135476  \n",
      "30  5458003.10  49.274721 -123.121745  \n",
      "\n",
      "\n",
      "The dataset describtion is:\n",
      "\n",
      "                 YEAR          MONTH            DAY           HOUR  \\\n",
      "count  530652.000000  530652.000000  530652.000000  476290.000000   \n",
      "mean     2009.197956       6.450597      15.408590      13.711254   \n",
      "std         4.386272       3.399212       8.750642       6.767744   \n",
      "min      2003.000000       1.000000       1.000000       0.000000   \n",
      "25%      2005.000000       4.000000       8.000000       9.000000   \n",
      "50%      2009.000000       6.000000      15.000000      15.000000   \n",
      "75%      2013.000000       9.000000      23.000000      19.000000   \n",
      "max      2017.000000      12.000000      31.000000      23.000000   \n",
      "\n",
      "              MINUTE              X             Y       Latitude  \\\n",
      "count  476290.000000  530652.000000  5.306520e+05  530652.000000   \n",
      "mean       16.940039  441802.149909  4.897663e+06      44.216029   \n",
      "std        18.418950  149281.547656  1.654633e+06      14.938004   \n",
      "min         0.000000       0.000000  0.000000e+00       0.000000   \n",
      "25%         0.000000  489944.452500  5.453651e+06      49.235573   \n",
      "50%        10.000000  491499.240000  5.456840e+06      49.264251   \n",
      "75%        30.000000  493547.210000  5.458638e+06      49.280441   \n",
      "max        59.000000  511303.000000  5.512579e+06      49.755314   \n",
      "\n",
      "           Longitude  \n",
      "count  530652.000000  \n",
      "mean     -110.495297  \n",
      "std        37.329862  \n",
      "min      -124.549757  \n",
      "25%      -123.127194  \n",
      "50%      -123.107128  \n",
      "75%      -123.065932  \n",
      "max         0.000000  \n",
      "\n",
      "\n",
      "The number of rows:\n",
      "\n",
      " 530652\n",
      "\n",
      "\n",
      "The number of columns:\n",
      "\n",
      " 12\n",
      "\n",
      "\n",
      "Column_Names:\n",
      "\n",
      " Index(['TYPE', 'YEAR', 'MONTH', 'DAY', 'HOUR', 'MINUTE', 'HUNDRED_BLOCK',\n",
      "       'NEIGHBOURHOOD', 'X', 'Y', 'Latitude', 'Longitude'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.Data_understanding()>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get information about the dataset\n",
    "Data_understanding() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the data type to categorical \n",
    "for col in ['TYPE','NEIGHBOURHOOD','HUNDRED_BLOCK']:\n",
    "    df[col] = df[col].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data\n",
    "\n",
    "In this section, some preparation steps will be performed which can help to answer all the questions before using the dataset:\n",
    "#### 1- Searching for missing values in each column \n",
    "#### 2- Fillin all the missing values \n",
    "#### 3- Drop unwanted columns \n",
    "#### 3- Using the one-Hot-Encoding method to deal with categorical data such as TYPE, NEIGHBOURHOOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TYPE                 0\n",
       "YEAR                 0\n",
       "MONTH                0\n",
       "DAY                  0\n",
       "HOUR             54362\n",
       "MINUTE           54362\n",
       "HUNDRED_BLOCK       13\n",
       "NEIGHBOURHOOD    56624\n",
       "X                    0\n",
       "Y                    0\n",
       "Latitude             0\n",
       "Longitude            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the missing values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### After checking the missing values which show only exist in column  \"HOUR\" & \"MINUTE\", \"NEIGHBOURHOOD\" & \"HUNDRED_BLOCK\".  My understanding is that missing values in the columns which nan-values is that these nan-values mean many crimes are not reported correctly with location and time, Thus it's a better idea to use zero to fill in these missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of missing values is 165361\n"
     ]
    }
   ],
   "source": [
    "# Finding the number of Missing vlaues \n",
    "missing_values=df.isnull().sum()\n",
    "missing= missing_values.sum()\n",
    "print('The number of missing values is',missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Fillin all the missing values with zero \n",
    "df = df.fillna(df.mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of missing values is 0\n"
     ]
    }
   ],
   "source": [
    "# Finding the number of Missing vlaues \n",
    "missing_values=df.isnull().sum()\n",
    "missing= missing_values.sum()\n",
    "print('The number of missing values is',missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unwanted columns \n",
    "\n",
    "columns_un_used=['X', 'Y', 'Latitude','MINUTE','HUNDRED_BLOCK', 'Longitude']\n",
    "df.drop(axis=0,columns=columns_un_used,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>TYPE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>NEIGHBOURHOOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>Strathcona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Strathcona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Strathcona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Strathcona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Strathcona</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          TYPE  YEAR  MONTH  DAY  HOUR NEIGHBOURHOOD\n",
       "0  Other Theft  2003      5   12  16.0    Strathcona\n",
       "1  Other Theft  2003      5    7  15.0    Strathcona\n",
       "2  Other Theft  2003      4   23  16.0    Strathcona\n",
       "3  Other Theft  2003      4   20  11.0    Strathcona\n",
       "4  Other Theft  2003      4   12  17.0    Strathcona"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TYPE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>NEIGHBOURHOOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2003</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2003</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2003</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2003</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2003</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530622</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530623</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530624</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530625</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530626</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530627</th>\n",
       "      <td>Theft of Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530628</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530629</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530630</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530631</th>\n",
       "      <td>Theft of Bicycle</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530632</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530633</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530634</th>\n",
       "      <td>Theft of Bicycle</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530635</th>\n",
       "      <td>Other Theft</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530636</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530637</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530638</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530639</th>\n",
       "      <td>Theft of Bicycle</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530640</th>\n",
       "      <td>Theft of Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530641</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530642</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530643</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530644</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530645</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530646</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530647</th>\n",
       "      <td>Break and Enter Residential/Other</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530648</th>\n",
       "      <td>Mischief</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530649</th>\n",
       "      <td>Offence Against a Person</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530650</th>\n",
       "      <td>Theft from Vehicle</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530651</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>530652 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     TYPE  YEAR  MONTH  DAY  \\\n",
       "0                                             Other Theft  2003      5   12   \n",
       "1                                             Other Theft  2003      5    7   \n",
       "2                                             Other Theft  2003      4   23   \n",
       "3                                             Other Theft  2003      4   20   \n",
       "4                                             Other Theft  2003      4   12   \n",
       "5                                             Other Theft  2003      3   26   \n",
       "6                       Break and Enter Residential/Other  2003      3   10   \n",
       "7                                                Mischief  2003      6   28   \n",
       "8                                             Other Theft  2003      2   16   \n",
       "9                       Break and Enter Residential/Other  2003      7    9   \n",
       "10                                            Other Theft  2003      1   31   \n",
       "11                                               Mischief  2003      9   27   \n",
       "12                      Break and Enter Residential/Other  2003      4   19   \n",
       "13                      Break and Enter Residential/Other  2003      9   24   \n",
       "14                      Break and Enter Residential/Other  2003     11    5   \n",
       "15                             Break and Enter Commercial  2003      9   26   \n",
       "16                      Break and Enter Residential/Other  2003     10   21   \n",
       "17                                            Other Theft  2003      1   25   \n",
       "18                               Offence Against a Person  2003      2   12   \n",
       "19                                            Other Theft  2003      1    9   \n",
       "20                                            Other Theft  2003      4   30   \n",
       "21                                            Other Theft  2003     12   12   \n",
       "22                                            Other Theft  2003      3    7   \n",
       "23                               Offence Against a Person  2003      4    4   \n",
       "24                                               Mischief  2003      1   23   \n",
       "25                                               Mischief  2003      4    1   \n",
       "26                                               Mischief  2003      6   29   \n",
       "27                                               Mischief  2003     10   16   \n",
       "28                                            Other Theft  2003      1   28   \n",
       "29                                     Theft from Vehicle  2003      8    5   \n",
       "...                                                   ...   ...    ...  ...   \n",
       "530622                                        Other Theft  2017      2    7   \n",
       "530623                                        Other Theft  2017      5    3   \n",
       "530624                                 Theft from Vehicle  2017      4   25   \n",
       "530625                           Offence Against a Person  2017      5    9   \n",
       "530626                                 Theft from Vehicle  2017      1    2   \n",
       "530627                                   Theft of Vehicle  2017      1   11   \n",
       "530628                                 Theft from Vehicle  2017      3   16   \n",
       "530629                  Break and Enter Residential/Other  2017      5   13   \n",
       "530630                  Break and Enter Residential/Other  2017      6    7   \n",
       "530631                                   Theft of Bicycle  2017      4   22   \n",
       "530632                           Offence Against a Person  2017      7   11   \n",
       "530633                         Break and Enter Commercial  2017      2    2   \n",
       "530634                                   Theft of Bicycle  2017      4   25   \n",
       "530635                                        Other Theft  2017      3   12   \n",
       "530636                                           Mischief  2017      4   15   \n",
       "530637                                 Theft from Vehicle  2017      6   19   \n",
       "530638  Vehicle Collision or Pedestrian Struck (with I...  2017      7   10   \n",
       "530639                                   Theft of Bicycle  2017      3   15   \n",
       "530640                                   Theft of Vehicle  2017      3   10   \n",
       "530641                           Offence Against a Person  2017      2    8   \n",
       "530642                                 Theft from Vehicle  2017      3   26   \n",
       "530643                                 Theft from Vehicle  2017      2    9   \n",
       "530644                                           Mischief  2017      5   25   \n",
       "530645                  Break and Enter Residential/Other  2017      6   17   \n",
       "530646                                           Mischief  2017      1   18   \n",
       "530647                  Break and Enter Residential/Other  2017      3    3   \n",
       "530648                                           Mischief  2017      5   29   \n",
       "530649                           Offence Against a Person  2017      4   13   \n",
       "530650                                 Theft from Vehicle  2017      6    5   \n",
       "530651  Vehicle Collision or Pedestrian Struck (with I...  2017      6    6   \n",
       "\n",
       "        HOUR  NEIGHBOURHOOD  \n",
       "0       16.0             19  \n",
       "1       15.0             19  \n",
       "2       16.0             19  \n",
       "3       11.0             19  \n",
       "4       17.0             19  \n",
       "5       20.0             19  \n",
       "6       12.0              7  \n",
       "7        4.0              2  \n",
       "8        9.0             19  \n",
       "9       18.0              4  \n",
       "10      19.0             19  \n",
       "11       1.0              2  \n",
       "12      18.0              4  \n",
       "13      18.0              4  \n",
       "14       8.0             20  \n",
       "15       2.0             22  \n",
       "16      10.0              4  \n",
       "17      12.0             19  \n",
       "18      18.0              1  \n",
       "19       6.0             19  \n",
       "20      13.0              1  \n",
       "21      15.0              1  \n",
       "22      16.0              1  \n",
       "23      18.0              1  \n",
       "24      23.0              2  \n",
       "25       0.0              2  \n",
       "26       0.0              2  \n",
       "27      14.0              2  \n",
       "28      10.0             22  \n",
       "29      12.0             22  \n",
       "...      ...            ...  \n",
       "530622  14.0              1  \n",
       "530623  16.0              1  \n",
       "530624   6.0             22  \n",
       "530625  18.0              1  \n",
       "530626   1.0             11  \n",
       "530627  22.0             21  \n",
       "530628  18.0             11  \n",
       "530629  20.0              2  \n",
       "530630   8.0              7  \n",
       "530631   8.0             20  \n",
       "530632  18.0              1  \n",
       "530633   8.0             19  \n",
       "530634  23.0              9  \n",
       "530635  15.0              1  \n",
       "530636  14.0              4  \n",
       "530637   8.0              1  \n",
       "530638   9.0             11  \n",
       "530639  17.0              0  \n",
       "530640   0.0              5  \n",
       "530641  18.0              1  \n",
       "530642  15.0             21  \n",
       "530643  22.0              1  \n",
       "530644  20.0             11  \n",
       "530645  12.0              9  \n",
       "530646  14.0              4  \n",
       "530647   9.0              5  \n",
       "530648  22.0              4  \n",
       "530649  18.0              1  \n",
       "530650  17.0              1  \n",
       "530651  17.0             10  \n",
       "\n",
       "[530652 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using the one-Hot-Encoding method to deal with categorical data and convert it to numerical varibles\n",
    "label = LabelEncoder()  \n",
    "df['NEIGHBOURHOOD']= label.fit_transform(df['NEIGHBOURHOOD'].astype('category'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answer Questions base on dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Highest Crime rate was in 2003\n"
     ]
    }
   ],
   "source": [
    "## Q1/Which year has the highest crime rate in Vancouver?\n",
    "\n",
    "\n",
    "# check the most frequent values in the YEAR column \n",
    "\n",
    "df_check=df['YEAR'].value_counts().idxmax()\n",
    "print('The Highest Crime rate was in',df_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting histogram\n",
    "plt.hist(df.YEAR,bins=30)\n",
    "plt.xlim(xmin=2003, xmax = 2017)\n",
    "plt.title('The Crime rate in Vancouver between 2003-2017')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see above the highest year of committing crimes in Vancouver was in 2003 and 2004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the most committed type of crime is Theft from Vehicle\n"
     ]
    }
   ],
   "source": [
    "# Q2/What is the most committed type of crime between year 2003 to 2017? What is the corrleation between the type of crime and Time?\n",
    "\n",
    "\n",
    "\n",
    "# counting the most frequent type of crime\n",
    "df_check=df['TYPE'].value_counts().idxmax()\n",
    "print('the most committed type of crime is',df_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142e0c41dd8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a graph\n",
    "plt.figure(figsize = (20,8))\n",
    "sns.countplot(df['HOUR'],hue=df['TYPE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  As we can see above Theft from Vehicle is the common crime in Vancouver which has the highest rate between the year 2003 until 2017.\n",
    "## From the chart above It can be seen that Theft from vehicle increased in the evening starting from 6 pm until late at night. On the other hand, in the morning it seems that there wasn't a lot of crimes, I think that because most of the stores open early. So there is no chance for a criminal to steal between those hours. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Console'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q3/ What the relation between the type of crime and the neighborhood that has a high rate of crimes?\n",
    "\n",
    "list1=list()\n",
    "mylist=list()\n",
    "for ii in df.TYPE.cat.categories:\n",
    "    list1.append(df[df.TYPE==ii].NEIGHBOURHOOD)\n",
    "    mylist.append(ii)\n",
    "sns.set_style('whitegrid') \n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.7,8.27)   \n",
    "plt.title('The Relation Between Type of Crime and The Neighbourhood' , fontsize=20, color='Black',fontname='Console')\n",
    "plt.ylabel('Type of crime',fontsize=20,color='Black') \n",
    "plt.xlabel('Neigbourhood Areas',fontsize=20,color='Black')\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xticks(fontsize=10.5)\n",
    "h=plt.hist(list1,stacked=True, bins=20, rwidth=1,label=mylist)\n",
    "plt.legend(frameon=True,fancybox=True,shadow=True,framealpha=0 ,fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From the chart, we can see that neighborhood 0 which represents Arbutus Ridge and neighborhood 1 which represent Central Business District have the most crime rate which means these areas should be check by the police because it has a high crime rate comparing with another neighborhood which seems it has a low crime rate, As well as Theft from vehicle and theft of the vehicle,  are the most committed crimes in these areas.\n",
    "\n",
    "# In conclusion,\n",
    "\n",
    "### Despite the fact that Vancouver has become the safest place to visit, the crime rate is slightly increased. To reduce the number of crimes, the relevant institutions in the region should increase CCTV cameras, particularly in areas that have a high crime rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference:\n",
    "#### 1- https://medium.com/@wise_plum_macaw_832/crime-in-vancouver-canada-2003-2017-e5ece4ec3080 (Blog-post)\n",
    "#### 2-https://www.kaggle.com/wosaku/crime-in-vancouver\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}