ANUSHEELA_BANIK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CRIME IN LA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform Descriptive Analytics on the Crime Dataset to observe the trends in LA area. Then based on the observations, predict the possibility of crime occuring in the majorly affected areas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROBLEM STATEMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will a crime occur in the areas that are most affected in LA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages needed for the code\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn import ensemble\n",
    "from sklearn import datasets\n",
    "from sklearn import model_selection\n",
    "import statsmodels.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the csv file\n",
    "#Link for the dataset : https://www.kaggle.com/cityofLA/crime-in-los-angeles#Crime_Data_2010_2017.csv\n",
    "df = pd.read_csv('G:/analytics systems technology/LACrime/Crime_Data_2010_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DR Number</th>\n",
       "      <th>Date Reported</th>\n",
       "      <th>Date Occurred</th>\n",
       "      <th>Time Occurred</th>\n",
       "      <th>Area ID</th>\n",
       "      <th>Area Name</th>\n",
       "      <th>Reporting District</th>\n",
       "      <th>Crime Code</th>\n",
       "      <th>Crime Code Description</th>\n",
       "      <th>MO Codes</th>\n",
       "      <th>...</th>\n",
       "      <th>Weapon Description</th>\n",
       "      <th>Status Code</th>\n",
       "      <th>Status Description</th>\n",
       "      <th>Crime Code 1</th>\n",
       "      <th>Crime Code 2</th>\n",
       "      <th>Crime Code 3</th>\n",
       "      <th>Crime Code 4</th>\n",
       "      <th>Address</th>\n",
       "      <th>Cross Street</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1208575</td>\n",
       "      <td>03/14/2013</td>\n",
       "      <td>03/11/2013</td>\n",
       "      <td>1800</td>\n",
       "      <td>12</td>\n",
       "      <td>77th Street</td>\n",
       "      <td>1241</td>\n",
       "      <td>626</td>\n",
       "      <td>INTIMATE PARTNER - SIMPLE ASSAULT</td>\n",
       "      <td>0416 0446 1243 2000</td>\n",
       "      <td>...</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>AO</td>\n",
       "      <td>Adult Other</td>\n",
       "      <td>626.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6300    BRYNHURST                    AV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(33.9829, -118.3338)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102005556</td>\n",
       "      <td>01/25/2010</td>\n",
       "      <td>01/22/2010</td>\n",
       "      <td>2300</td>\n",
       "      <td>20</td>\n",
       "      <td>Olympic</td>\n",
       "      <td>2071</td>\n",
       "      <td>510</td>\n",
       "      <td>VEHICLE - STOLEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VAN NESS</td>\n",
       "      <td>15TH</td>\n",
       "      <td>(34.0454, -118.3157)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>418</td>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>03/18/2013</td>\n",
       "      <td>2030</td>\n",
       "      <td>18</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1823</td>\n",
       "      <td>510</td>\n",
       "      <td>VEHICLE - STOLEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 E  104TH                        ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(33.942, -118.2717)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101822289</td>\n",
       "      <td>11/11/2010</td>\n",
       "      <td>11/10/2010</td>\n",
       "      <td>1800</td>\n",
       "      <td>18</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1803</td>\n",
       "      <td>510</td>\n",
       "      <td>VEHICLE - STOLEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>510.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88TH</td>\n",
       "      <td>WALL</td>\n",
       "      <td>(33.9572, -118.2717)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42104479</td>\n",
       "      <td>01/11/2014</td>\n",
       "      <td>01/04/2014</td>\n",
       "      <td>2300</td>\n",
       "      <td>21</td>\n",
       "      <td>Topanga</td>\n",
       "      <td>2133</td>\n",
       "      <td>745</td>\n",
       "      <td>VANDALISM - MISDEAMEANOR ($399 OR UNDER)</td>\n",
       "      <td>0329</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</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>7200    CIRRUS                       WY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(34.2009, -118.6369)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DR Number Date Reported Date Occurred  Time Occurred  Area ID    Area Name  \\\n",
       "0    1208575    03/14/2013    03/11/2013           1800       12  77th Street   \n",
       "1  102005556    01/25/2010    01/22/2010           2300       20      Olympic   \n",
       "2        418    03/19/2013    03/18/2013           2030       18    Southeast   \n",
       "3  101822289    11/11/2010    11/10/2010           1800       18    Southeast   \n",
       "4   42104479    01/11/2014    01/04/2014           2300       21      Topanga   \n",
       "\n",
       "   Reporting District  Crime Code                    Crime Code Description  \\\n",
       "0                1241         626         INTIMATE PARTNER - SIMPLE ASSAULT   \n",
       "1                2071         510                          VEHICLE - STOLEN   \n",
       "2                1823         510                          VEHICLE - STOLEN   \n",
       "3                1803         510                          VEHICLE - STOLEN   \n",
       "4                2133         745  VANDALISM - MISDEAMEANOR ($399 OR UNDER)   \n",
       "\n",
       "              MO Codes          ...           \\\n",
       "0  0416 0446 1243 2000          ...            \n",
       "1                  NaN          ...            \n",
       "2                  NaN          ...            \n",
       "3                  NaN          ...            \n",
       "4                 0329          ...            \n",
       "\n",
       "                               Weapon Description Status Code  \\\n",
       "0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)          AO   \n",
       "1                                             NaN          IC   \n",
       "2                                             NaN          IC   \n",
       "3                                             NaN          IC   \n",
       "4                                             NaN          IC   \n",
       "\n",
       "  Status Description  Crime Code 1 Crime Code 2  Crime Code 3 Crime Code 4  \\\n",
       "0        Adult Other         626.0          NaN           NaN          NaN   \n",
       "1        Invest Cont         510.0          NaN           NaN          NaN   \n",
       "2        Invest Cont         510.0          NaN           NaN          NaN   \n",
       "3        Invest Cont         510.0          NaN           NaN          NaN   \n",
       "4        Invest Cont         745.0          NaN           NaN          NaN   \n",
       "\n",
       "                                   Address Cross Street             Location   \n",
       "0  6300    BRYNHURST                    AV          NaN  (33.9829, -118.3338)  \n",
       "1                                 VAN NESS         15TH  (34.0454, -118.3157)  \n",
       "2   200 E  104TH                        ST          NaN   (33.942, -118.2717)  \n",
       "3                                     88TH         WALL  (33.9572, -118.2717)  \n",
       "4  7200    CIRRUS                       WY          NaN  (34.2009, -118.6369)  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1584316 entries, 0 to 1584315\n",
      "Data columns (total 26 columns):\n",
      "DR Number                 1584316 non-null int64\n",
      "Date Reported             1584316 non-null object\n",
      "Date Occurred             1584316 non-null object\n",
      "Time Occurred             1584316 non-null int64\n",
      "Area ID                   1584316 non-null int64\n",
      "Area Name                 1584316 non-null object\n",
      "Reporting District        1584316 non-null int64\n",
      "Crime Code                1584316 non-null int64\n",
      "Crime Code Description    1583904 non-null object\n",
      "MO Codes                  1412557 non-null object\n",
      "Victim Age                1455657 non-null float64\n",
      "Victim Sex                1439117 non-null object\n",
      "Victim Descent            1439084 non-null object\n",
      "Premise Code              1584240 non-null float64\n",
      "Premise Description       1581565 non-null object\n",
      "Weapon Used Code          524757 non-null float64\n",
      "Weapon Description        524756 non-null object\n",
      "Status Code               1584314 non-null object\n",
      "Status Description        1584316 non-null object\n",
      "Crime Code 1              1584309 non-null float64\n",
      "Crime Code 2              99997 non-null float64\n",
      "Crime Code 3              2183 non-null float64\n",
      "Crime Code 4              69 non-null float64\n",
      "Address                   1584316 non-null object\n",
      "Cross Street              262733 non-null object\n",
      "Location                  1584307 non-null object\n",
      "dtypes: float64(7), int64(5), object(14)\n",
      "memory usage: 314.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DR Number                       0\n",
       "Date Reported                   0\n",
       "Date Occurred                   0\n",
       "Time Occurred                   0\n",
       "Area ID                         0\n",
       "Area Name                       0\n",
       "Reporting District              0\n",
       "Crime Code                      0\n",
       "Crime Code Description        412\n",
       "MO Codes                   171759\n",
       "Victim Age                 128659\n",
       "Victim Sex                 145199\n",
       "Victim Descent             145232\n",
       "Premise Code                   76\n",
       "Premise Description          2751\n",
       "Weapon Used Code          1059559\n",
       "Weapon Description        1059560\n",
       "Status Code                     2\n",
       "Status Description              0\n",
       "Crime Code 1                    7\n",
       "Crime Code 2              1484319\n",
       "Crime Code 3              1582133\n",
       "Crime Code 4              1584247\n",
       "Address                         0\n",
       "Cross Street              1321583\n",
       "Location                        9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the Columns with Victim's Age, Sex and Descent.\n",
    "victim = df[['Victim Age', 'Victim Sex', 'Victim Descent']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec0d05b00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#remove the missing values\n",
    "sns.distplot(victim['Victim Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec0dc6438>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborns Count Plot\n",
    "plt.title('Number of Victims as per Sex')\n",
    "sns.countplot('Victim Sex', data=victim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec0dc6518>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of Victims according to Sex of the Victim b/w the age group 20 and 35 years\n",
    "sns.countplot('Victim Sex', data=victim[(victim['Victim Age'] >= 20) & (victim['Victim Age'] <= 35)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec1224208>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Countplot according to the Victim's Descent\n",
    "sns.countplot('Victim Descent', data=victim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Area ID, Area Name and Reporting District.\n",
    "area = df[['Area ID', 'Area Name', 'Reporting District']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec12dd518>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which area have more Crime.\n",
    "plt.title('Number of Crimes in Different Area')\n",
    "sns.countplot(x='Area ID', data=area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crime Code and it's Description\n",
    "crime = df[['Crime Code', 'Crime Code Description','Status Description','Weapon Description','Premise Description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BATTERY - SIMPLE ASSAULT                                        145767\n",
       "VEHICLE - STOLEN                                                121329\n",
       "BURGLARY FROM VEHICLE                                           121318\n",
       "BURGLARY                                                        114751\n",
       "THEFT PLAIN - PETTY ($950 & UNDER)                              113709\n",
       "THEFT OF IDENTITY                                               100653\n",
       "INTIMATE PARTNER - SIMPLE ASSAULT                                85908\n",
       "VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114     79433\n",
       "VANDALISM - MISDEAMEANOR ($399 OR UNDER)                         71523\n",
       "ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT                   67631\n",
       "Name: Crime Code Description, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 crimes\n",
    "crime['Crime Code Description'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec3a00b38>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot of the top 10 crimes\n",
    "plt.title('Number of Crimes according to Crime Description')\n",
    "crime['Crime Code Description'].value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26ec39f4eb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot of the top 10 crimes according to their crime code\n",
    "plt.title('Number of Crimes according to Crime Code')\n",
    "crime['Crime Code'].value_counts()[:10].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plotting weapons used\n",
    "sns.barplot(y=crime[\"Weapon Description\"].value_counts().head(20).index,\n",
    "                 x=crime[\"Weapon Description\"].value_counts().head(20), ax=ax)\n",
    "\n",
    "# Axes\n",
    "ax.set_title(\"Weapon Used\")\n",
    "ax.set_xlabel(\"Total Crimes Occurred\")\n",
    "ax.set_ylabel(\"Weapon\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STREET                                          352160\n",
       "SINGLE FAMILY DWELLING                          328198\n",
       "MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)    204980\n",
       "PARKING LOT                                     112576\n",
       "SIDEWALK                                         79247\n",
       "Name: Premise Description, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 5 areas of crime\n",
    "crime[\"Premise Description\"].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Top 10 premises of crime by a juvie\n",
    "ax = sns.barplot(y=crime[\"Premise Description\"].value_counts().head(10).index,\n",
    "                 x=crime[\"Premise Description\"].value_counts().head(10))\n",
    "\n",
    "# Axes\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax.set_title(\"Crime Premises by Juvies\")\n",
    "ax.set_xlabel(\"Total Crimes Occurred\")\n",
    "ax.set_ylabel(\"Premise\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_crime = pd.concat(objs=[area, crime], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Crimes in Area ID 12 i.e 77th Street 110605\n",
      "Number of Crimes in Area ID 3 i.e Southwest 102259\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Crimes in Area ID 12 i.e 77th Street\", len(area_crime[(area_crime['Area ID'] == 12)]))\n",
    "print (\"Number of Crimes in Area ID 3 i.e Southwest\", len(area_crime[(area_crime['Area ID'] == 3)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the Area with the ID 12 and 3 Respectively.\n",
    "area_crime_12 = area_crime[(area_crime['Area ID'] == 12)]\n",
    "area_crime_3 = area_crime[(area_crime['Area ID'] == 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BATTERY - SIMPLE ASSAULT                          12208\n",
       "VEHICLE - STOLEN                                   9831\n",
       "ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT     8959\n",
       "ROBBERY                                            7840\n",
       "BURGLARY                                           7790\n",
       "INTIMATE PARTNER - SIMPLE ASSAULT                  7146\n",
       "BURGLARY FROM VEHICLE                              5866\n",
       "VANDALISM - MISDEAMEANOR ($399 OR UNDER)           5361\n",
       "THEFT PLAIN - PETTY ($950 & UNDER)                 5275\n",
       "THEFT OF IDENTITY                                  5146\n",
       "Name: Crime Code Description, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_crime_12['Crime Code Description'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BATTERY - SIMPLE ASSAULT                          11196\n",
       "THEFT PLAIN - PETTY ($950 & UNDER)                 9730\n",
       "VEHICLE - STOLEN                                   7085\n",
       "BURGLARY                                           6920\n",
       "BURGLARY FROM VEHICLE                              6026\n",
       "INTIMATE PARTNER - SIMPLE ASSAULT                  5624\n",
       "ROBBERY                                            5621\n",
       "ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT     5501\n",
       "THEFT OF IDENTITY                                  5174\n",
       "VANDALISM - MISDEAMEANOR ($399 OR UNDER)           4825\n",
       "Name: Crime Code Description, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_crime_3['Crime Code Description'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3Dh++uknhZCISDWrNIhCQ0Pp37+/4/bWrVsrnfTMmTOkpKSQk5NDbGwsubm5xMXFceLECby9vQHo3r07hw8fLvdYwzAuq/DPPvuMvLw8nnnmGeBCMG3atInAwECaNGlCkyZNHGMvPufBgwfZu3cvNpsNgJKSEjIzM5k/fz4xMTEsXryYrl27XnYNIiJy9SoMoo8//pjdu3ezZcsWvvnmG+DCL/vt27czZMiQ3530vffe48EHH3SsnvLz8x2n0NLT02nfvj3ff/89Hh4euLm5kZ2dTWlpKXl5eWRkZJSb71LBkJyczJw5c+jbty8AaWlpzJkzh8DAQJycyl76slgsAPj4+PD//t//IzIyErvdzhtvvEGbNm149dVXmT17Nm5ubjz66KN8/fXXODk5Ybfbf7dPERG5ehUGka+vL6dPn8bNzc2xorBYLPz5z3+udNKkpCQWLlzouH3DDTcwcOBAWrVqxYwZM2jQoAENGjTAw8OD5s2b07t3bwICAvDy8qJt27bl5mvfvj3Tpk1j8eLFAGRnZ/Ptt9/yyiuvOMb06NGDwsLC332DQf/+/fn3v//Nww8/zPnz57nvvvtwd3fnlltuISAggCZNmtCyZUtuv/123N3diY6OpkuXLpfVs4iI/DEWo5LzUHa7vcwK4+TJk7Ro0aLaC6ut0tLSCPtXmNllXJbL+YbWffv20alTpxqopmaon9pN/dRuZvVT6TWiZcuWER8fT3FxMQUFBbRr1+6q344tIiJyUaWfI0pJSSElJYVhw4axdetWWrZsWRN1iYhIHVFpEDVu3Bir1UpeXh5t27YlPz+/JuoSEZE6otIgatWqFcnJydxwww0sWbKE3NzcmqhLRETqiEqvEf31r3/l2LFjDBo0iH/84x+8+uqrNVGXiIjUERWuiEpLSykqKiIsLIwWLVpgtVoJDAxk9uzZNVmfiIhc5ypcEa1bt47ly5fz22+/MWjQIAzDwNnZmR49etRkfSIicp2rMIiCgoIICgoiOTmZgICAmqxJRETqkApPzRUVFfH222/z4IMPcuLECcLCwpg2bRpZWVk1WZ+IiFznKgyiyMhIMjMzsdvtRERE4Ovry/33319uF2wREZGrUeGpuczMTP7+979TWFhIWloar7/+Oq6ursTExNRkfbXS5WydIyIil6fCFdHFHat3797NbbfdhqurK8Alv/ZbRETkj6pwRVS/fn0SExP54IMPGDp0KHa7nXXr1tG6deuarE9ERK5zFa6IIiIiOHz4MAMGDGDEiBHs2rWLHTt26BqRiIhUqQpXRJ6enkyfPt1xu1evXvTq1atGihIRkbqj0r3mREREqpOCSERETFVpEL3xxhtlbi9ZsqTairlWfHrPvY7/RETk6lR4jSgpKYnk5GTS09NJSUkBLmyEWlJSwjPPPFNjBYqIyPWtwiAaPnw4vXr1YsWKFUyYMAEAJycnmjZtWmPFiYjI9a/CU3NWq5U2bdowa9YsnJ2dcXNzY/369dprTkREqlSl14imTZvGnj17WLhwIa6urrz44os1UZeIiNQRlQbR2bNnGTBgAMePH2fcuHEUFRXVRF0iIlJHVBpExcXFxMTE0KVLF3788Ufy8vJqoi4REakjKg2iGTNmkJ2dzcSJE9m1a5e2+BERkSpV4bvmLurevTsFBQX885//pEePHnh7e9dEXSIiUkdUGkRLly7l+PHjpKen4+rqysqVK1m6dGlN1CYiInVApafm0tLSWLhwIfXr12fEiBFkZGTURF0iIlJHVBpEpaWlFBYWYrFYKC0txclJ29OJiEjVqTRVQkJC8Pf359ChQwQGBjJq1Kgqe3KbzUZ6evpVzfHtt99y66238t1331VRVf+RmJhIcXFxlc8rIiL/Uek1osaNGxMfH8+vv/5KmzZt8PT0rIm6LltSUhKhoaHEx8fj5+dXpXOvWLGCv/zlL1U6p4iIlFXpiigqKgoPDw/8/PyqLYSioqJISEgAID09HZvNxv79+xk9erRjzPjx4/nhhx/KPC4vL4+dO3cyefJkdu/eTU5OjmO+sWPHEhwcTHp6OsOGDcNms7Fq1SoOHDiAzWbDZrPx5JNPcu7cOXJychg9ejQ2m43g4GAOHDhAUlISWVlZTJ06tVp6FhGRCypdEVksFp544gm8vb0d14eefvrpai/M19eXwsJCjh49iqurK6dOnaJz585lxmzdupU//elPuLm5MXjwYJKTkxk3bhwAPj4+vPDCC2RkZJCVlcW6deuwWq0EBQUxb948OnToQFJSEqtXr6Zbt240bNiQJUuW8OOPP5Kbm0tgYCDR0dG88sor1d6riEhdVmkQ/eUvf8HZ2bnKnjAvLw+r1YqrqytwIegqEhAQwIYNG7Barfj7+5e7PykpCWdnZx599FEKCgo4fvw4jz32GECZzzu1adMGq9UKXFhxzZ49G7iwa4S3tzf33HMPv/zyC5MmTcLFxYWJEydWWb8iIvL7Kg2irVu3EhMTU2VPOHPmTEaNGkXPnj3Jzs7G09MTNzc3x67ee/fudYwdMmQIISEhWCyWcjUcOHCA0tJS1q5d6zgWGhrKxx9/DFDm3X3//bO3tzcLFizgxhtvJC0tjaysLHbt2kWLFi2IiYnh66+/ZunSpcTGxmKxWLDb7VXWu4iIlFdpEDVs2JBt27aVOTV3NbsrhIaGMmfOHNzc3BgxYgSNGzdm8ODBTJkyhdTUVG699VbH2AYNGuDr60tJSQnu7u5l5klKSmL48OFljgUGBrJmzRq6detW4fNHREQwY8YMSktLAZg7dy6NGzdm6tSpvP322zg5OfHEE08AcMcddzBu3Djeeeed3125iYjIH2cxDMP4vQE2m+3CQIuFU6dO8csvv/D999/XSHG1UVpaGrlT/3ON7N6UT02s5urt27ePTp06mV1GlVE/tZv6qd3M6qfSd83FxsYyffp0WrVqRU5ODgEBATVRl4iI1BEVnporKipiy5YtxMfH4+rqSm5uLtu3b6devXo1WZ+IiFznKlwR9e/fnwMHDrBo0SLi4+Np0aKFQkhERKpchSui0aNHs3nzZo4ePUpAQACVXEoSERH5QypcEY0bN4733nsPm83G5s2b2bNnD4sWLeLgwYM1WZ+IiFznKn2zQs+ePVm0aBEfffQRrVq14tlnn62JukREpI647O90aNSoETabjQ0bNlRnPSIiUsfoy4VERMRUCiIRETFVpVv8SHnX+m4KIiK1iVZEIiJiKgWRiIiYSkEkIiKmUhCJiIipFEQiImIqBZGIiJhKQSQiIqZSEImIiKkURCIiYioFkYiImEpBJCIiplIQiYiIqRREIiJiKgWRiIiYSkEkIiKmUhCJiIipFEQiImIqBZGIiJiqVgSRzWYjPT39Dz8+Ly+POXPmMGrUKGw2GxMmTODnn38GoLCwkKSkJACioqJISEiokppFRKRq1Iogulrh4eG0bduWNWvWEBsby5QpU3jiiSc4d+4cWVlZjiASEZHap1YF0X+vWNLT07HZbOzfv5/Ro0c7xowfP54ffvjBcTsnJ4eDBw9is9kcx3x9fenXrx8ffvghy5cv58cff2TZsmUAbN++nTFjxjB8+HB27NgBwPvvv89DDz3EyJEjWbx4saOWsWPHEhwcfFWrNRER+X21KoguxdfXl8LCQo4ePcrJkyc5deoUnTt3dtyfkZHBzTffXO5xN998M5mZmUyYMIEOHTowefJkAFq2bMnbb7/Nc889R0JCAqdPnyYqKoq33nqLhIQETpw4wRdffAGAj48P7777Lu3bt6+ZZkVE6iAXs544Ly8Pq9WKq6srABaLpcKxAQEBbNiwAavVir+/f5n7WrRoQWZmZrnH/Prrr5cMkC5dugDQrFkzCgoKOHz4MDk5OYwbN85R15EjRwDw9vb+Y82JiMhlM21FNHPmTNLS0rDb7WRnZ+Pp6YmbmxtZWVkA7N271zF2yJAhfPLJJ3z00UcMHTq0zDytWrXCy8uLNWvWOI7t3buXHTt2MHDgQJycnLDb7Y77/jfw2rRpQ+vWrYmJiSE2NpZHHnmE22+/HQAnp1q/YBQRueaZtiIKDQ1lzpw5uLm5MWLECBo3bszgwYOZMmUKqamp3HrrrY6xDRo0wNfXl5KSEtzd3cvNtWDBAhYuXEhgYCDOzs40atSIN954g0aNGuHm5kZxcTGLFi2iXr165R7r6elJSEgINpuN0tJSbrrpJgYPHlytvYuIyH9YDMMwzC7iWpKWlkaPHj3MLqPK7Nu3j06dOpldRpVRP7Wb+qndzOpH555ERMRUCiIRETGVgkhEREylIBIREVMpiERExFQKIhERMZWCSERETKUgEhERUymIRETEVAoiERExlYJIRERMpSASERFTKYhERMRUCiIRETGVgkhEREylIBIREVMpiERExFQKIhERMZWCSERETKUgEhERUymIRETEVAoiERExlYJIRERMpSASERFTKYhERMRUCiIRETGVgkhEREylIBIREVNdl0G0a9cupk6dWubY4sWLWb9+PV26dGHPnj2O4wkJCURFRQHQv39/CgsLAcjKymLYsGFs3Lix5goXEamDrssg+j3u7u7MmjWLoqKiCsecOHGCRx99lKeeeorhw4fXYHUiInVPnQuitm3bcvfdd/PKK69c8v7MzExCQ0OZOXMm9913Xw1XJyJS99SpIMrMzARgypQpfPHFF3z11VflxoSFhVGvXj2ys7NrujwRkTrpugyievXqlTv1dv78edzc3ACwWq3Mnz+fF154gfz8/DLj5s2bx9/+9jeWLFlCenp6jdUsIlJXXZdB1L59e/bt28fJkycBKCwsJDU1lYEDBzrGdOnShaFDh7Jq1aoyj+3YsSOtW7dm5syZTJkyhYKCghqtXUSkrrkug8jd3Z2ZM2cyfvx4Ro4cyahRo7DZbDg7O5cZN2HCBG688cZLzjFo0CD8/PyYPXt2TZQsIlJnuZhdQHUZOHBgmRXQRWvXrnX87OLiwrp16xy3d+zYUWbs3Llzq69AEREBrtMVkYiIXDsURCIiYioFkYiImEpBJCIiplIQiYiIqRREIiJiKgWRiIiYSkEkIiKmUhCJiIipFEQiImIqBZGIiJhKQSQiIqZSEImIiKkshmEYZhdxLUlLSzO7BBGRa1KPHj0ueVxBJCIiptKpORERMZWCSERETHXdfkNrVbPb7URERHDgwAGsVitz5syhbdu2ZpdFcXExzz33HEePHqWoqIiJEyfSoUMHZs6cicVi4f/+7/946aWXcHJyYtmyZXzyySe4uLjw3HPP4efnx6+//nrVY6tDdnY2/v7+xMTE4OLick33s2LFCnbs2EFxcTEjR46kZ8+e12w/xcXFzJw5k6NHj+Lk5ERkZOQ1++fz7bffsnjxYmJjY6ukrqsdW5X97Nu3j8jISJydnbFarSxYsIBmzZqxdu1a3n33XVxcXJg4cSL9+vUjJyeHadOmUVBQQIsWLZg/fz433HDDVY+9IoZclg8++MCYMWOGYRiG8fXXXxsTJkwwuaILkpOTjTlz5hiGYRg5OTnGvffea4wfP97YuXOnYRiGER4ebnz44YfGnj17DJvNZtjtduPo0aOGv7+/YRjGVY+tDkVFRcakSZOMgQMHGj/++OM13c/OnTuN8ePHG6WlpUZubq7x+uuvX9P9fPTRR0ZYWJhhGIbx+eefG5MnT74m+1m5cqUxdOhQIzAwsErqqoqxVdnPqFGjjB9++MEwDMNISEgw5s2bZ5w8edIYOnSoUVhYaJw9e9bxc2RkpLFu3TrDMAxjxYoVxptvvlklY6+ETs1dprS0NO6++24Aunbtyp49e0yu6IJBgwbx1FNPOW47Ozuzd+9eevbsCcA999zDv/71L9LS0ujTpw8Wi4Ubb7yR0tJScnJyrnpsdViwYAHBwcG0aNEC4Jru5/PPP6djx4488cQTTJgwgb59+17T/Xh7e1NaWordbic3NxcXF5drsh8vLy+ioqIct2uyh4rGVmU/S5cupVOnTgCUlpbi5ubGd999R7du3bBarTRs2BAvLy/2799f5nfbxRqrYuyVUBBdptzcXNzd3R23nZ2dKSkpMbGiCxo0aIC7uzu5ubmEhYUxZcoUDMPAYrE47j937ly5+i8ev9qxVW3jojj9AAAIq0lEQVT9+vV4eno6/rID13Q/p06dYs+ePbz22mvMnj2badOmXdP91K9fn6NHjzJ48GDCw8Ox2WzXZD/3338/Li7/uTJRkz1UNLYq+7n4j7jdu3cTFxdHSEgIubm5NGzYsMzz5ubmljn+3zVe7dgroWtEl8nd3Z28vDzHbbvdXuYP3kzHjh3jiSee4OGHH2bYsGEsWrTIcV9eXh6NGjUqV39eXh4NGzYsc879j4ytauvWrcNisfDll1+yb98+ZsyYUeZfi9daP40bN8bHxwer1YqPjw9ubm4cP378mu3nrbfeok+fPjzzzDMcO3aMMWPGUFxcfM32c9HV1lUVY6va1q1biY6OZuXKlXh6elb4vBeP16tXr9Iar2TsldCK6DJ1796dlJQUAL755hs6duxockUX/Pbbb4wdO5bp06cTEBAAQOfOndm1axcAKSkp3HHHHXTv3p3PP/8cu91OZmYmdrsdT0/Pqx5b1dasWUNcXByxsbF06tSJBQsWcM8991yz/fTo0YPPPvsMwzA4ceIE+fn59OrV65rtp1GjRo5fMh4eHpSUlFzTf98uqskeKhpblTZu3Oj4/+jmm28GwM/Pj7S0NAoLCzl37hzp6el07NiR7t278+mnnzpq7NGjR5WMvRL6QOtluviuuYMHD2IYBvPmzaN9+/Zml8WcOXN4//338fHxcRx7/vnnmTNnDsXFxfj4+DBnzhycnZ2JiooiJSUFu93OrFmzuOOOO/j5558JDw+/qrHVxWazERERgZOT01XXaGY/CxcuZNeuXRiGwdSpU2nTps01209eXh7PPfccWVlZFBcXM3r0aG699dZrsp+MjAyefvpp1q5dWyV1Xe3YquonISGBXr160bp1a8cq8s477yQsLIy1a9eSmJiIYRiMHz+e+++/n99++40ZM2aQl5dHkyZNWLJkCfXr17/qsVdCQSQiIqbSqTkRETGVgkhEREylIBIREVMpiERExFQKIhERMVXt+ESmiIlefvll9u7dS1ZWFgUFBdx88800adKE119//ZLjMzIyOHToUIUbO17c6DIhIaHM8dOnT7NgwQKOHDlCSUkJN910E7Nnzy7zKXuA6Oho7r77bm699daqafASvvzyS9544w3gwkamgwcPZvTo0Y5dAMwSFhbG6NGjq/UzQ1L7KIikzps5cyZwYXuhn376iWnTpv3u+C+//JKMjIwr3mF46tSp2Gw2+vfvD8Dq1auJiIhg8eLFZcZNnDjxiua9Uvv372fRokWsWLGC5s2bU1JSQnh4OG+//TYhISHV+twil6IgEvkdc+fO5ZtvvgFg+PDhBAUFsXr1aoqKiujWrRtubm5ER0cDUFhYWGZ7pf925MgRzpw54wghgJCQEAoKCigpKWHgwIG0a9eOW265haysLPz9/Tl69CifffYZ+fn5ZGVlMXr0aLZt28aPP/7IrFmz6NevH1u2bOGdd97BycmJnj17MnXqVL766isWLFiAq6srHh4ejg8dXhQfH8+kSZNo3rw5AC4uLsyaNYvAwEBCQkLYtm0b0dHRGIbBbbfdRkREBNu3by937N5772XHjh24uLiwYMECfH19ad68Oa+++iouLi6MHDmS5cuX065dO+rVq8eLL77I888/z5kzZ7BYLLz44ot06NCBd955h/Xr19O8eXN+++236vqjlFpMQSRSgW3btnHy5EnWrl1LcXExwcHB3HXXXTz22GNkZGTQt29fYmNjWbp0Kc2aNWPZsmX885//vOSnyk+cOEGbNm3KHHNxccHd3Z2SkhKOHz/Ohg0baNSoUZkVWX5+PqtWrWLjxo0kJCSQkJDAv/71LxITE7n99tuJjo4mOTmZevXq8fTTT7Nz50527NjBkCFDGDNmDNu2bePs2bNlgigjI8Ox7ctFjRo1Ijc3l6KiIubNm0dycjKenp5ERUVx7NixcsdOnDhR4etWUlLC2rVrAVi0aBFhYWHccsstvPzyy9xzzz0EBQWRnp5OREQEr7/+OvHx8bz33nsAjBgx4sr/oOSapyASqUB6ejp33HEHFosFq9XK7bffTnp6epkxLVu25K9//Sv169fn+PHjji3//1fr1q3L/fIuKiriww8/ZNCgQXh6el5yU8/OnTsDF4Kiffv2WCwWGjVqRGFhIb/88gvZ2dk8/vjjwIUd4o8cOcKkSZNYvnw5Y8aMoVWrVnTr1q1czUePHuWWW25xHDtz5gw33HADOTk5NGnSxLH32ZNPPsnx48fLHftf/71Bi7e3d5n7Lt4+ePAgX331FZs2bQIuXDP76aef6NixI1arFYDbbrvtkq+fXN/0rjmRCrRv3560tDTgQmh88803tG3bFovF4vjFGx4ezssvv8zLL79M06ZNqWjHrJtuuon69evzySefOI699dZbfPzxxwB/6JtHvby8aN26NTExMcTGxjJq1Cj8/PzYuHEjAQEBxMbG0q5dO5KTk8s8buTIkSxbtsxxGuziKujhhx+mWbNmnDp1irNnzwIwe/ZsTpw4Ue7Ynj17cHNzIysrC8Mw2Ldvn2P+/33Dw8XefHx8GDt2rGMVOWzYMLy8vDh06BBFRUWUlJTwww8/XPHrINc+rYhEKjBgwAD+/e9/ExwcTFFREUOHDsXX15fi4mJWrVpFp06dGDZsGAEBATRq1IimTZty8uTJCudbsmQJERERrFq1iqKiItq2bcvs2bP/cH3NmjXDZrNhs9koLS3l5ptvZujQoeTl5TF9+nTc3d1xdXUlMjKyzOP8/Px46qmneOqpp7Db7RQXFzNo0CBCQ0OxWCyEh4fz+OOP4+TkRJcuXfDz8yt3rEuXLjz++OOMHTuWNm3a0Lhx40rrnTRpEs8//zwJCQnk5eURFhZG8+bNmTRpEkFBQTRt2rTcOwilbtCmpyIiYiqdmhMREVMpiERExFQKIhERMZWCSERETKUgEhERUymIRETEVAoiERExlYJIRERM9f8BpOrG+lNs0hMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plotting the arrest status\n",
    "sns.barplot(y=crime[\"Status Description\"].value_counts().index,\n",
    "                 x=crime[\"Status Description\"].value_counts(), ax=ax)\n",
    "\n",
    "# Axes\n",
    "ax.set_title(\"Status\")\n",
    "ax.set_xlabel(\"Total Crimes Occurred\")\n",
    "ax.set_ylabel(\"Arrest Status\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DR Number</th>\n",
       "      <th>Time Occurred</th>\n",
       "      <th>Area ID</th>\n",
       "      <th>Reporting District</th>\n",
       "      <th>Crime Code</th>\n",
       "      <th>Victim Age</th>\n",
       "      <th>Premise Code</th>\n",
       "      <th>Weapon Used Code</th>\n",
       "      <th>Crime Code 1</th>\n",
       "      <th>Crime Code 2</th>\n",
       "      <th>Crime Code 3</th>\n",
       "      <th>Crime Code 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DR Number</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007390</td>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.016248</td>\n",
       "      <td>-0.009166</td>\n",
       "      <td>0.007397</td>\n",
       "      <td>-0.015414</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>-0.009321</td>\n",
       "      <td>-0.099782</td>\n",
       "      <td>-0.010357</td>\n",
       "      <td>0.036429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Occurred</th>\n",
       "      <td>-0.007390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011809</td>\n",
       "      <td>0.012023</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>-0.049945</td>\n",
       "      <td>-0.095394</td>\n",
       "      <td>-0.002821</td>\n",
       "      <td>0.017759</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>-0.088805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area ID</th>\n",
       "      <td>0.016191</td>\n",
       "      <td>0.011809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998998</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>0.009835</td>\n",
       "      <td>0.014475</td>\n",
       "      <td>-0.000490</td>\n",
       "      <td>-0.004650</td>\n",
       "      <td>-0.046569</td>\n",
       "      <td>-0.026894</td>\n",
       "      <td>0.012957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reporting District</th>\n",
       "      <td>0.016248</td>\n",
       "      <td>0.012023</td>\n",
       "      <td>0.998998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004624</td>\n",
       "      <td>0.010262</td>\n",
       "      <td>0.014411</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.004686</td>\n",
       "      <td>-0.045945</td>\n",
       "      <td>-0.024548</td>\n",
       "      <td>0.012456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime Code</th>\n",
       "      <td>-0.009166</td>\n",
       "      <td>0.017712</td>\n",
       "      <td>-0.004587</td>\n",
       "      <td>-0.004624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034478</td>\n",
       "      <td>0.098754</td>\n",
       "      <td>0.432260</td>\n",
       "      <td>0.999638</td>\n",
       "      <td>0.059313</td>\n",
       "      <td>0.092420</td>\n",
       "      <td>0.030399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victim Age</th>\n",
       "      <td>0.007397</td>\n",
       "      <td>-0.049945</td>\n",
       "      <td>0.009835</td>\n",
       "      <td>0.010262</td>\n",
       "      <td>-0.034478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167376</td>\n",
       "      <td>0.074359</td>\n",
       "      <td>-0.034394</td>\n",
       "      <td>-0.002280</td>\n",
       "      <td>-0.071872</td>\n",
       "      <td>-0.106784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Premise Code</th>\n",
       "      <td>-0.015414</td>\n",
       "      <td>-0.095394</td>\n",
       "      <td>0.014475</td>\n",
       "      <td>0.014411</td>\n",
       "      <td>0.098754</td>\n",
       "      <td>0.167376</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216194</td>\n",
       "      <td>0.098761</td>\n",
       "      <td>-0.036025</td>\n",
       "      <td>-0.069356</td>\n",
       "      <td>0.170824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weapon Used Code</th>\n",
       "      <td>0.000441</td>\n",
       "      <td>-0.002821</td>\n",
       "      <td>-0.000490</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>0.432260</td>\n",
       "      <td>0.074359</td>\n",
       "      <td>0.216194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.432594</td>\n",
       "      <td>-0.095010</td>\n",
       "      <td>0.022898</td>\n",
       "      <td>-0.048323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime Code 1</th>\n",
       "      <td>-0.009321</td>\n",
       "      <td>0.017759</td>\n",
       "      <td>-0.004650</td>\n",
       "      <td>-0.004686</td>\n",
       "      <td>0.999638</td>\n",
       "      <td>-0.034394</td>\n",
       "      <td>0.098761</td>\n",
       "      <td>0.432594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069727</td>\n",
       "      <td>0.124671</td>\n",
       "      <td>0.021249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime Code 2</th>\n",
       "      <td>-0.099782</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>-0.046569</td>\n",
       "      <td>-0.045945</td>\n",
       "      <td>0.059313</td>\n",
       "      <td>-0.002280</td>\n",
       "      <td>-0.036025</td>\n",
       "      <td>-0.095010</td>\n",
       "      <td>0.069727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255056</td>\n",
       "      <td>0.267714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime Code 3</th>\n",
       "      <td>-0.010357</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>-0.026894</td>\n",
       "      <td>-0.024548</td>\n",
       "      <td>0.092420</td>\n",
       "      <td>-0.071872</td>\n",
       "      <td>-0.069356</td>\n",
       "      <td>0.022898</td>\n",
       "      <td>0.124671</td>\n",
       "      <td>0.255056</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime Code 4</th>\n",
       "      <td>0.036429</td>\n",
       "      <td>-0.088805</td>\n",
       "      <td>0.012957</td>\n",
       "      <td>0.012456</td>\n",
       "      <td>0.030399</td>\n",
       "      <td>-0.106784</td>\n",
       "      <td>0.170824</td>\n",
       "      <td>-0.048323</td>\n",
       "      <td>0.021249</td>\n",
       "      <td>0.267714</td>\n",
       "      <td>0.506226</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    DR Number  Time Occurred   Area ID  Reporting District  \\\n",
       "DR Number            1.000000      -0.007390  0.016191            0.016248   \n",
       "Time Occurred       -0.007390       1.000000  0.011809            0.012023   \n",
       "Area ID              0.016191       0.011809  1.000000            0.998998   \n",
       "Reporting District   0.016248       0.012023  0.998998            1.000000   \n",
       "Crime Code          -0.009166       0.017712 -0.004587           -0.004624   \n",
       "Victim Age           0.007397      -0.049945  0.009835            0.010262   \n",
       "Premise Code        -0.015414      -0.095394  0.014475            0.014411   \n",
       "Weapon Used Code     0.000441      -0.002821 -0.000490           -0.000368   \n",
       "Crime Code 1        -0.009321       0.017759 -0.004650           -0.004686   \n",
       "Crime Code 2        -0.099782       0.007710 -0.046569           -0.045945   \n",
       "Crime Code 3        -0.010357       0.005377 -0.026894           -0.024548   \n",
       "Crime Code 4         0.036429      -0.088805  0.012957            0.012456   \n",
       "\n",
       "                    Crime Code  Victim Age  Premise Code  Weapon Used Code  \\\n",
       "DR Number            -0.009166    0.007397     -0.015414          0.000441   \n",
       "Time Occurred         0.017712   -0.049945     -0.095394         -0.002821   \n",
       "Area ID              -0.004587    0.009835      0.014475         -0.000490   \n",
       "Reporting District   -0.004624    0.010262      0.014411         -0.000368   \n",
       "Crime Code            1.000000   -0.034478      0.098754          0.432260   \n",
       "Victim Age           -0.034478    1.000000      0.167376          0.074359   \n",
       "Premise Code          0.098754    0.167376      1.000000          0.216194   \n",
       "Weapon Used Code      0.432260    0.074359      0.216194          1.000000   \n",
       "Crime Code 1          0.999638   -0.034394      0.098761          0.432594   \n",
       "Crime Code 2          0.059313   -0.002280     -0.036025         -0.095010   \n",
       "Crime Code 3          0.092420   -0.071872     -0.069356          0.022898   \n",
       "Crime Code 4          0.030399   -0.106784      0.170824         -0.048323   \n",
       "\n",
       "                    Crime Code 1  Crime Code 2  Crime Code 3  Crime Code 4  \n",
       "DR Number              -0.009321     -0.099782     -0.010357      0.036429  \n",
       "Time Occurred           0.017759      0.007710      0.005377     -0.088805  \n",
       "Area ID                -0.004650     -0.046569     -0.026894      0.012957  \n",
       "Reporting District     -0.004686     -0.045945     -0.024548      0.012456  \n",
       "Crime Code              0.999638      0.059313      0.092420      0.030399  \n",
       "Victim Age             -0.034394     -0.002280     -0.071872     -0.106784  \n",
       "Premise Code            0.098761     -0.036025     -0.069356      0.170824  \n",
       "Weapon Used Code        0.432594     -0.095010      0.022898     -0.048323  \n",
       "Crime Code 1            1.000000      0.069727      0.124671      0.021249  \n",
       "Crime Code 2            0.069727      1.000000      0.255056      0.267714  \n",
       "Crime Code 3            0.124671      0.255056      1.000000      0.506226  \n",
       "Crime Code 4            0.021249      0.267714      0.506226      1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PREDICTIVE ANALYTICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_use=df[['Time Occurred']+['Area ID']+['Reporting District']+['Crime Code']+\n",
    "          ['Victim Sex']+['Victim Age']+['Victim Descent']+['Premise Code']+['Weapon Used Code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1584316 entries, 0 to 1584315\n",
      "Data columns (total 9 columns):\n",
      "Time Occurred         1584316 non-null int64\n",
      "Area ID               1584316 non-null int64\n",
      "Reporting District    1584316 non-null int64\n",
      "Crime Code            1584316 non-null int64\n",
      "Victim Sex            1439117 non-null object\n",
      "Victim Age            1455657 non-null float64\n",
      "Victim Descent        1439084 non-null object\n",
      "Premise Code          1584240 non-null float64\n",
      "Weapon Used Code      524757 non-null float64\n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 108.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_use.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#fill up the missing values for the categorical data\n",
    "df_use['Victim Sex'] = df_use['Victim Sex'].fillna('NaN')\n",
    "df_use['Victim Descent'] = df_use['Victim Descent'].fillna('NaN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#fill up the missing values for numeric data\n",
    "df_use['Victim Age'] = df_use['Victim Age'].fillna(0)\n",
    "df_use['Premise Code'] = df_use['Premise Code'].fillna(0)\n",
    "df_use['Weapon Used Code'] = df_use['Weapon Used Code'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anush\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#define OUTPUT LABEL based on area ID\n",
    "df_use['OUTPUT_LABEL'] = (df_use['Area ID'] == 12).astype('int') | (df_use['Area ID'] == 3).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>510</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1800</td>\n",
       "      <td>18</td>\n",
       "      <td>1803</td>\n",
       "      <td>510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2300</td>\n",
       "      <td>21</td>\n",
       "      <td>2133</td>\n",
       "      <td>745</td>\n",
       "      <td>84.0</td>\n",
       "      <td>W</td>\n",
       "      <td>501.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time Occurred  Area ID  Reporting District  Crime Code  Victim Age  \\\n",
       "0           1800       12                1241         626        30.0   \n",
       "1           2300       20                2071         510         0.0   \n",
       "2           2030       18                1823         510        12.0   \n",
       "3           1800       18                1803         510         0.0   \n",
       "4           2300       21                2133         745        84.0   \n",
       "\n",
       "  Victim Descent  Premise Code  Weapon Used Code  OUTPUT_LABEL  \n",
       "0              W         502.0             400.0             1  \n",
       "1            NaN         101.0               0.0             0  \n",
       "2            NaN         101.0               0.0             0  \n",
       "3            NaN         101.0               0.0             0  \n",
       "4              W         501.0               0.0             0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final dataframe to be used for prediction\n",
    "df_final=df_use[['Time Occurred']+['Area ID']+['Reporting District']+['Crime Code']\n",
    "         +['Victim Age']+['Victim Descent']+['Premise Code']+['Weapon Used Code']+['OUTPUT_LABEL']]\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1803</td>\n",
       "      <td>510</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2300</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2133</td>\n",
       "      <td>745</td>\n",
       "      <td>501.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time Occurred  Victim Age  Reporting District  Crime Code  Premise Code  \\\n",
       "0           1800        30.0                1241         626         502.0   \n",
       "1           2300         0.0                2071         510         101.0   \n",
       "2           2030        12.0                1823         510         101.0   \n",
       "3           1800         0.0                1803         510         101.0   \n",
       "4           2300        84.0                2133         745         501.0   \n",
       "\n",
       "   Weapon Used Code  OUTPUT_LABEL  \n",
       "0             400.0             1  \n",
       "1               0.0             0  \n",
       "2               0.0             0  \n",
       "3               0.0             0  \n",
       "4               0.0             0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[['Time Occurred']+['Victim Age']+['Reporting District']+['Crime Code']\n",
    "         +['Premise Code']+['Weapon Used Code']+['OUTPUT_LABEL']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Time Occurred',\n",
       " 'Victim Age',\n",
       " 'Reporting District',\n",
       " 'Crime Code',\n",
       " 'Premise Code',\n",
       " 'Weapon Used Code']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_num = ['Time Occurred','Victim Age','Reporting District','Crime Code','Premise Code','Weapon Used Code']\n",
    "cols_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time Occurred         0\n",
       "Victim Age            0\n",
       "Reporting District    0\n",
       "Crime Code            0\n",
       "Premise Code          0\n",
       "Weapon Used Code      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final[cols_num].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_cat=['Victim Descent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Victim Descent_A</th>\n",
       "      <th>Victim Descent_B</th>\n",
       "      <th>Victim Descent_C</th>\n",
       "      <th>Victim Descent_D</th>\n",
       "      <th>Victim Descent_F</th>\n",
       "      <th>Victim Descent_G</th>\n",
       "      <th>Victim Descent_H</th>\n",
       "      <th>Victim Descent_I</th>\n",
       "      <th>Victim Descent_J</th>\n",
       "      <th>Victim Descent_K</th>\n",
       "      <th>Victim Descent_L</th>\n",
       "      <th>Victim Descent_NaN</th>\n",
       "      <th>Victim Descent_O</th>\n",
       "      <th>Victim Descent_P</th>\n",
       "      <th>Victim Descent_S</th>\n",
       "      <th>Victim Descent_U</th>\n",
       "      <th>Victim Descent_V</th>\n",
       "      <th>Victim Descent_W</th>\n",
       "      <th>Victim Descent_X</th>\n",
       "      <th>Victim Descent_Z</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Victim Descent_A  Victim Descent_B  Victim Descent_C  Victim Descent_D  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   Victim Descent_F  Victim Descent_G  Victim Descent_H  Victim Descent_I  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   Victim Descent_J  Victim Descent_K  Victim Descent_L  Victim Descent_NaN  \\\n",
       "0                 0                 0                 0                   0   \n",
       "1                 0                 0                 0                   1   \n",
       "2                 0                 0                 0                   1   \n",
       "3                 0                 0                 0                   1   \n",
       "4                 0                 0                 0                   0   \n",
       "\n",
       "   Victim Descent_O  Victim Descent_P  Victim Descent_S  Victim Descent_U  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   Victim Descent_V  Victim Descent_W  Victim Descent_X  Victim Descent_Z  \n",
       "0                 0                 1                 0                 0  \n",
       "1                 0                 0                 0                 0  \n",
       "2                 0                 0                 0                 0  \n",
       "3                 0                 0                 0                 0  \n",
       "4                 0                 1                 0                 0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = pd.get_dummies(df_use[cols_cat],drop_first = True)\n",
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_final,df_cat], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of categorical: 20\n"
     ]
    }
   ],
   "source": [
    "cols_all_cat = list(df_cat.columns)\n",
    "print('number of categorical:',len(cols_all_cat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Engineering Features Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_input = cols_num + cols_all_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Time Occurred</th>\n",
       "      <th>Victim Age</th>\n",
       "      <th>Reporting District</th>\n",
       "      <th>Crime Code</th>\n",
       "      <th>Premise Code</th>\n",
       "      <th>Weapon Used Code</th>\n",
       "      <th>Victim Descent_A</th>\n",
       "      <th>Victim Descent_B</th>\n",
       "      <th>Victim Descent_C</th>\n",
       "      <th>Victim Descent_D</th>\n",
       "      <th>...</th>\n",
       "      <th>Victim Descent_NaN</th>\n",
       "      <th>Victim Descent_O</th>\n",
       "      <th>Victim Descent_P</th>\n",
       "      <th>Victim Descent_S</th>\n",
       "      <th>Victim Descent_U</th>\n",
       "      <th>Victim Descent_V</th>\n",
       "      <th>Victim Descent_W</th>\n",
       "      <th>Victim Descent_X</th>\n",
       "      <th>Victim Descent_Z</th>\n",
       "      <th>OUTPUT_LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1800</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1241</td>\n",
       "      <td>626</td>\n",
       "      <td>502.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2071</td>\n",
       "      <td>510</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2030</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1823</td>\n",
       "      <td>510</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1803</td>\n",
       "      <td>510</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2300</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2133</td>\n",
       "      <td>745</td>\n",
       "      <td>501.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time Occurred  Victim Age  Reporting District  Crime Code  Premise Code  \\\n",
       "0           1800        30.0                1241         626         502.0   \n",
       "1           2300         0.0                2071         510         101.0   \n",
       "2           2030        12.0                1823         510         101.0   \n",
       "3           1800         0.0                1803         510         101.0   \n",
       "4           2300        84.0                2133         745         501.0   \n",
       "\n",
       "   Weapon Used Code  Victim Descent_A  Victim Descent_B  Victim Descent_C  \\\n",
       "0             400.0                 0                 0                 0   \n",
       "1               0.0                 0                 0                 0   \n",
       "2               0.0                 0                 0                 0   \n",
       "3               0.0                 0                 0                 0   \n",
       "4               0.0                 0                 0                 0   \n",
       "\n",
       "   Victim Descent_D      ...       Victim Descent_NaN  Victim Descent_O  \\\n",
       "0                 0      ...                        0                 0   \n",
       "1                 0      ...                        1                 0   \n",
       "2                 0      ...                        1                 0   \n",
       "3                 0      ...                        1                 0   \n",
       "4                 0      ...                        0                 0   \n",
       "\n",
       "   Victim Descent_P  Victim Descent_S  Victim Descent_U  Victim Descent_V  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   Victim Descent_W  Victim Descent_X  Victim Descent_Z  OUTPUT_LABEL  \n",
       "0                 1                 0                 0             1  \n",
       "1                 0                 0                 0             0  \n",
       "2                 0                 0                 0             0  \n",
       "3                 0                 0                 0             0  \n",
       "4                 1                 0                 0             0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data = df[cols_input + ['OUTPUT_LABEL']]\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Training and Test Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#shuffle the samples\n",
    "df_data = df_data.sample(n = len(df_data))\n",
    "df_data = df_data.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split size: 0.300\n"
     ]
    }
   ],
   "source": [
    "# Save 30% of the data as validation and test data \n",
    "df_test=df_data.sample(frac=0.30)\n",
    "print('Split size: %.3f'%(len(df_test)/len(df_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the rest of the data as training data\n",
    "df_train_all=df_data.drop(df_test.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the training data into positive and negative\n",
    "rows_pos = df_train_all.OUTPUT_LABEL == 1\n",
    "df_train_pos = df_train_all.loc[rows_pos]\n",
    "df_train_neg = df_train_all.loc[~rows_pos]\n",
    "\n",
    "# merge the balanced data\n",
    "df_train = pd.concat([df_train_pos, df_train_neg.sample(n = len(df_train_pos))],axis = 0)\n",
    "\n",
    "# shuffle the order of training samples \n",
    "df_train = df_train.sample(n = len(df_train)).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is defined for calculating Prevalance.\n",
    "def calc_prevalence(y_actual):\n",
    "    return (sum(y_actual)/len(y_actual))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Test prevalence(n = %d):%.3f'%(len(df_test),calc_prevalence(df_test.OUTPUT_LABEL.values)))\n",
    "print('Valid prevalence(n = %d):%.3f'%(len(df_valid),calc_prevalence(df_valid.OUTPUT_LABEL.values)))\n",
    "print('Train all prevalence(n = %d):%.3f'%(len(df_train_all), calc_prevalence(df_train_all.OUTPUT_LABEL.values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n"
     ]
    }
   ],
   "source": [
    "col2use = [c for c in list(df_train_all.columns) if c != 'OUTPUT_LABEL']\n",
    "print(len(col2use))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training All shapes: (1109021, 26)\n",
      "Training shapes: (298268, 26) (298268,)\n"
     ]
    }
   ],
   "source": [
    "X_train = df_train[col2use].values\n",
    "X_train_all = df_train_all[col2use].values\n",
    "\n",
    "y_train = df_train['OUTPUT_LABEL'].values\n",
    "\n",
    "print('Training All shapes:',X_train_all.shape)\n",
    "print('Training shapes:',X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler  = StandardScaler()\n",
    "scaler.fit(X_train_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "scalerfile = 'scaler.sav'\n",
    "pickle.dump(scaler, open(scalerfile, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = pickle.load(open(scalerfile, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tf = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score\n",
    "def calc_specificity(y_actual, y_pred, thresh):\n",
    "    # calculates specificity\n",
    "    return sum((y_pred < thresh) & (y_actual == 0)) /sum(y_actual ==0)\n",
    "\n",
    "def print_report(y_actual, y_pred, thresh):\n",
    "    \n",
    "    auc = roc_auc_score(y_actual, y_pred)\n",
    "    accuracy = accuracy_score(y_actual, (y_pred > thresh))\n",
    "    recall = recall_score(y_actual, (y_pred > thresh))\n",
    "    precision = precision_score(y_actual, (y_pred > thresh))\n",
    "    specificity = calc_specificity(y_actual, y_pred, thresh)\n",
    "    print('AUC:%.3f'%auc)\n",
    "    print('accuracy:%.3f'%accuracy)\n",
    "    print('recall:%.3f'%recall)\n",
    "    print('precision:%.3f'%precision)\n",
    "    print('specificity:%.3f'%specificity)\n",
    "    print(' ')\n",
    "    return auc, accuracy, recall, precision, specificity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(max_depth = 6)\n",
    "rf.fit(X_train_tf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "Training:\n",
      "AUC:0.969\n",
      "accuracy:0.914\n",
      "recall:0.974\n",
      "precision:0.870\n",
      "specificity:0.854\n",
      " \n"
     ]
    }
   ],
   "source": [
    "y_train_preds = rf.predict_proba(X_train_tf)[:,1]\n",
    "\n",
    "print('Random Forest')\n",
    "print('Training:')\n",
    "rf_train_auc, rf_train_accuracy, rf_train_recall, rf_train_precision, rf_train_specificity =print_report(y_train,y_train_preds, thresh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create X and Y matrix\n",
    "X_test = df_test[col2use].values\n",
    "y_test = df_test['OUTPUT_LABEL'].values\n",
    "\n",
    "#transform our data matrices\n",
    "X_test_tf = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(rf, open('best_classifier.pkl', 'wb'),protocol = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# load the model\n",
    "best_model = pickle.load(open('best_classifier.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_preds = best_model.predict_proba(X_train_tf)[:,1]\n",
    "y_test_preds = best_model.predict_proba(X_test_tf)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test:\n",
      "AUC:0.969\n",
      "accuracy:0.871\n",
      "recall:0.974\n",
      "precision:0.509\n",
      "specificity:0.855\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('Test:')\n",
    "test_auc, test_accuracy, test_recall, test_precision, test_specificity = print_report(y_test,y_test_preds, thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10393927536674834\n",
      "0.11310657927506981\n"
     ]
    }
   ],
   "source": [
    "mse_error = np.mean((y_train_preds-y_train) ** 2)\n",
    "print(mse_error)\n",
    "mse_error2=np.mean((y_test_preds-y_test)**2)\n",
    "print(mse_error2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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