jyu-theartofml/Kaggle data-SF crime.ipynb

## Kaggle data-SF crime.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train=pd.read_csv('train.csv',parse_dates=['Dates']) \n",
    "#parse_dates to make sure the Dates column is handled as datetime63 dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-05-13 23:53:00\n",
       "1   2015-05-13 23:53:00\n",
       "2   2015-05-13 23:33:00\n",
       "3   2015-05-13 23:30:00\n",
       "4   2015-05-13 23:30:00\n",
       "Name: Dates, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Dates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hour = train.Dates.dt.hour\n",
    "train['hour']=hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"ticks\")\n",
    "sns.set_context({\"figure.figsize\": (24, 10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Category</th>\n",
       "      <th>Descript</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Address</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-05-13 23:53:00</td>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>OAK ST / LAGUNA ST</td>\n",
       "      <td>-122.425892</td>\n",
       "      <td>37.774599</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-05-13 23:53:00</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>TRAFFIC VIOLATION ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>OAK ST / LAGUNA ST</td>\n",
       "      <td>-122.425892</td>\n",
       "      <td>37.774599</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-05-13 23:33:00</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>TRAFFIC VIOLATION ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>VANNESS AV / GREENWICH ST</td>\n",
       "      <td>-122.424363</td>\n",
       "      <td>37.800414</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1500 Block of LOMBARD ST</td>\n",
       "      <td>-122.426995</td>\n",
       "      <td>37.800873</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>PARK</td>\n",
       "      <td>NONE</td>\n",
       "      <td>100 Block of BRODERICK ST</td>\n",
       "      <td>-122.438738</td>\n",
       "      <td>37.771541</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM UNLOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>INGLESIDE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0 Block of TEDDY AV</td>\n",
       "      <td>-122.403252</td>\n",
       "      <td>37.713431</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>VEHICLE THEFT</td>\n",
       "      <td>STOLEN AUTOMOBILE</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>INGLESIDE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AVALON AV / PERU AV</td>\n",
       "      <td>-122.423327</td>\n",
       "      <td>37.725138</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>VEHICLE THEFT</td>\n",
       "      <td>STOLEN AUTOMOBILE</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>BAYVIEW</td>\n",
       "      <td>NONE</td>\n",
       "      <td>KIRKWOOD AV / DONAHUE ST</td>\n",
       "      <td>-122.371274</td>\n",
       "      <td>37.727564</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-05-13 23:00:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>RICHMOND</td>\n",
       "      <td>NONE</td>\n",
       "      <td>600 Block of 47TH AV</td>\n",
       "      <td>-122.508194</td>\n",
       "      <td>37.776601</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-05-13 23:00:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>JEFFERSON ST / LEAVENWORTH ST</td>\n",
       "      <td>-122.419088</td>\n",
       "      <td>37.807802</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Dates        Category                        Descript  \\\n",
       "0 2015-05-13 23:53:00        WARRANTS                  WARRANT ARREST   \n",
       "1 2015-05-13 23:53:00  OTHER OFFENSES        TRAFFIC VIOLATION ARREST   \n",
       "2 2015-05-13 23:33:00  OTHER OFFENSES        TRAFFIC VIOLATION ARREST   \n",
       "3 2015-05-13 23:30:00   LARCENY/THEFT    GRAND THEFT FROM LOCKED AUTO   \n",
       "4 2015-05-13 23:30:00   LARCENY/THEFT    GRAND THEFT FROM LOCKED AUTO   \n",
       "5 2015-05-13 23:30:00   LARCENY/THEFT  GRAND THEFT FROM UNLOCKED AUTO   \n",
       "6 2015-05-13 23:30:00   VEHICLE THEFT               STOLEN AUTOMOBILE   \n",
       "7 2015-05-13 23:30:00   VEHICLE THEFT               STOLEN AUTOMOBILE   \n",
       "8 2015-05-13 23:00:00   LARCENY/THEFT    GRAND THEFT FROM LOCKED AUTO   \n",
       "9 2015-05-13 23:00:00   LARCENY/THEFT    GRAND THEFT FROM LOCKED AUTO   \n",
       "\n",
       "   DayOfWeek PdDistrict      Resolution                        Address  \\\n",
       "0  Wednesday   NORTHERN  ARREST, BOOKED             OAK ST / LAGUNA ST   \n",
       "1  Wednesday   NORTHERN  ARREST, BOOKED             OAK ST / LAGUNA ST   \n",
       "2  Wednesday   NORTHERN  ARREST, BOOKED      VANNESS AV / GREENWICH ST   \n",
       "3  Wednesday   NORTHERN            NONE       1500 Block of LOMBARD ST   \n",
       "4  Wednesday       PARK            NONE      100 Block of BRODERICK ST   \n",
       "5  Wednesday  INGLESIDE            NONE            0 Block of TEDDY AV   \n",
       "6  Wednesday  INGLESIDE            NONE            AVALON AV / PERU AV   \n",
       "7  Wednesday    BAYVIEW            NONE       KIRKWOOD AV / DONAHUE ST   \n",
       "8  Wednesday   RICHMOND            NONE           600 Block of 47TH AV   \n",
       "9  Wednesday    CENTRAL            NONE  JEFFERSON ST / LEAVENWORTH ST   \n",
       "\n",
       "            X          Y  hour  \n",
       "0 -122.425892  37.774599    23  \n",
       "1 -122.425892  37.774599    23  \n",
       "2 -122.424363  37.800414    23  \n",
       "3 -122.426995  37.800873    23  \n",
       "4 -122.438738  37.771541    23  \n",
       "5 -122.403252  37.713431    23  \n",
       "6 -122.423327  37.725138    23  \n",
       "7 -122.371274  37.727564    23  \n",
       "8 -122.508194  37.776601    23  \n",
       "9 -122.419088  37.807802    23  "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16             LARCENY/THEFT\n",
       "21            OTHER OFFENSES\n",
       "20              NON-CRIMINAL\n",
       "1                    ASSAULT\n",
       "7              DRUG/NARCOTIC\n",
       "36             VEHICLE THEFT\n",
       "35                 VANDALISM\n",
       "37                  WARRANTS\n",
       "4                   BURGLARY\n",
       "32            SUSPICIOUS OCC\n",
       "19            MISSING PERSON\n",
       "25                   ROBBERY\n",
       "13                     FRAUD\n",
       "12    FORGERY/COUNTERFEITING\n",
       "27           SECONDARY CODES\n",
       "Name: Category, dtype: object"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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29OzZkzZt2oSmPgkPD+fZZ58lMjKSrl27smzZslA9//nPf/jwww954oknCAQC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\nbFANTKY1E1OZLw8QamavonkEqguag6/YoFoEio0XGERjLtmUS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sdA120yaZ+WjDvf3Cl6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YDdbufCCy/mp/9xD39+8BX+8ugHLDv3Z4zOX4bFXIDXq6Jp3atkEmK42bNbJ3NOSUznaH53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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf24b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## bar plot to examine crime categories based on PdDistrict\n",
    "crime_count=train.groupby(['Category','PdDistrict']).size()\n",
    "crime_count\n",
    "summary=pd.DataFrame({'count':crime_count}).reset_index()\n",
    "#find total number of crime in each category then plot pie chart\n",
    "summary2=summary.groupby('Category').sum().reset_index()\n",
    "\n",
    "\n",
    "\n",
    "plt.pie(summary2['count'], labels=summary2['Category'], colors=c)\n",
    "summary3=summary2.sort_values('count', ascending=False)\n",
    "top15=summary3[0:15]\n",
    "top15['Category']\n",
    "#g=sns.countplot(x=\"PdDistrict\", hue=\"Category\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>BAYVIEW</td>\n",
       "      <td>9857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>6977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>INGLESIDE</td>\n",
       "      <td>8533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>MISSION</td>\n",
       "      <td>11149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>8318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Category PdDistrict  count\n",
       "10  ASSAULT    BAYVIEW   9857\n",
       "11  ASSAULT    CENTRAL   6977\n",
       "12  ASSAULT  INGLESIDE   8533\n",
       "13  ASSAULT    MISSION  11149\n",
       "14  ASSAULT   NORTHERN   8318"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topCrime=summary[summary['Category'].isin(top15['Category'])]\n",
    "## this selects the top 15 crimes using the boolean function .isin\n",
    "topCrime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Lly6kp6fX6X07Y8CAASQnJ1+S2HJ5qMArIiIiIiIiIiIN1u7du+nUqRMAx44dY+nSpaSk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5387b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar plots for the PdDistrict\n",
    "pivotPlot=topCrime.pivot(index='PdDistrict', columns='Category')\n",
    "p1=pivotPlot.plot(kind='bar', stacked=True, colormap='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Category</th>\n",
       "      <th>Descript</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>PdDistrict</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Address</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-05-13 23:53:00</td>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>OAK ST / LAGUNA ST</td>\n",
       "      <td>-122.425892</td>\n",
       "      <td>37.774599</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-05-13 23:53:00</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>TRAFFIC VIOLATION ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>OAK ST / LAGUNA ST</td>\n",
       "      <td>-122.425892</td>\n",
       "      <td>37.774599</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-05-13 23:33:00</td>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>TRAFFIC VIOLATION ARREST</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>VANNESS AV / GREENWICH ST</td>\n",
       "      <td>-122.424363</td>\n",
       "      <td>37.800414</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>NORTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>1500 Block of LOMBARD ST</td>\n",
       "      <td>-122.426995</td>\n",
       "      <td>37.800873</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-13 23:30:00</td>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>PARK</td>\n",
       "      <td>NONE</td>\n",
       "      <td>100 Block of BRODERICK ST</td>\n",
       "      <td>-122.438738</td>\n",
       "      <td>37.771541</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Dates        Category                      Descript  \\\n",
       "0 2015-05-13 23:53:00        WARRANTS                WARRANT ARREST   \n",
       "1 2015-05-13 23:53:00  OTHER OFFENSES      TRAFFIC VIOLATION ARREST   \n",
       "2 2015-05-13 23:33:00  OTHER OFFENSES      TRAFFIC VIOLATION ARREST   \n",
       "3 2015-05-13 23:30:00   LARCENY/THEFT  GRAND THEFT FROM LOCKED AUTO   \n",
       "4 2015-05-13 23:30:00   LARCENY/THEFT  GRAND THEFT FROM LOCKED AUTO   \n",
       "\n",
       "   DayOfWeek PdDistrict      Resolution                    Address  \\\n",
       "0  Wednesday   NORTHERN  ARREST, BOOKED         OAK ST / LAGUNA ST   \n",
       "1  Wednesday   NORTHERN  ARREST, BOOKED         OAK ST / LAGUNA ST   \n",
       "2  Wednesday   NORTHERN  ARREST, BOOKED  VANNESS AV / GREENWICH ST   \n",
       "3  Wednesday   NORTHERN            NONE   1500 Block of LOMBARD ST   \n",
       "4  Wednesday       PARK            NONE  100 Block of BRODERICK ST   \n",
       "\n",
       "            X          Y  hour  \n",
       "0 -122.425892  37.774599    23  \n",
       "1 -122.425892  37.774599    23  \n",
       "2 -122.424363  37.800414    23  \n",
       "3 -122.426995  37.800873    23  \n",
       "4 -122.438738  37.771541    23  "
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vuv6NYDLKu4tczWVmZhIYGMiWLVvw8fG50eGIiIiIiIiIiMj/oMLCQiwWS6Wu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\nu1OuSWFhYan1Yq+F2Wwu9wVhANOnTyc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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9dd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topCrime2=train[train['Category'].isin(top15['Category'])]\n",
    "## this selects the top 15 crimes using the boolean function .isin\n",
    "CrimeHr=pd.DataFrame(topCrime2['Category'], topCrime2['hour']).reset_index()\n",
    "\n",
    "crime_count2=pd.DataFrame(CrimeHr.groupby(['Category','hour']).size()).reset_index()\n",
    "pivotPlot2=crime_count2.pivot(index='hour', columns='Category')\n",
    "p2=pivotPlot2.plot(kind='bar', stacked=True, colormap='Set2')\n",
    "topCrime2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7286d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the crime on X,Y map\n",
    "c=[\n",
    "\"#356146\",\n",
    "\"#6B2ED8\",\n",
    "\t\"#4EBC32\",\n",
    "\t\"#CF35E0\",\n",
    "\t\"#85B234\",\n",
    "\t\"#A85DE7\",\n",
    "\t\"#B4A82E\",\n",
    "\t\"#5157D0\",\n",
    "\t\"#51BA67\",\n",
    "\t\"#6A2DA6\",\n",
    "\t\"#407B2A\",\n",
    "\t\"#D463D5\",\n",
    "\t\"#90A355\",\n",
    "\t\"#DB3CAA\",\n",
    "\t\"#5AB086\",\n",
    "\t\"#E4412A\",\n",
    "\t\"#6587DF\",\n",
    "\t\"#DF8A2F\",\n",
    "\t\"#AC7CDA\",\n",
    "\t\"#B18C3C\",\n",
    "\t\"#842F89\",\n",
    "\t\"#585A1F\",\n",
    "\t\"#D773B6\",\n",
    "\t\"#50A6A8\",\n",
    "\t\"#AE4220\",\n",
    "\t\"#6CA2CC\",\n",
    "\t\"#E35F5D\",\n",
    "\t\"#52498E\",\n",
    "\t\"#824D21\",\n",
    "\t\"#AD90C5\",\n",
    "\t\"#9B2A3B\",\n",
    "\t\"#445E7A\",\n",
    "\t\"#DE427A\",\n",
    "\t\"#D28461\",\n",
    "\t\"#932E69\",\n",
    "\t\"#733A34\",\n",
    "\t\"#D98AA2\",\n",
    "\t\"#734968\",\n",
    "\t\"#AD5F6A\"\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter (topCrime2['X'],topCrime2['Y'],marker = 'o', color=c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CrimeData = preprocessing.LabelEncoder()\n",
    "CrimeData2 = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  0,  0,  4,  5, 10,  5,  5,  3, 13,  5,  3,  3, 12, 12,  9, 11,\n",
       "        4, 12, 12,  3,  3,  5,  4,  1,  0,  5,  4, 13, 13,  5, 12, 12,  5,\n",
       "        3,  5,  3,  5,  3,  5, 13,  3,  5,  2,  5,  9, 12, 13,  5,  2,  5,\n",
       "        0,  3,  3, 13,  3, 13, 12, 12,  8, 13,  3,  5,  5,  7,  6,  3], dtype=int64)"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cData = CrimeData.fit_transform(train.Category)\n",
    "cData2=CrimeData2.fit_transform(north.Category)\n",
    "#Convert crime labels to label numbers named cData\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-05-13 23:53:00\n",
       "1   2015-05-13 23:53:00\n",
       "2   2015-05-13 23:33:00\n",
       "3   2015-05-13 23:30:00\n",
       "4   2015-05-13 23:30:00\n",
       "Name: Dates, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Dates.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
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      ],
      "text/plain": [
       "   Jan  Feb  March  April  May  June  July  Aug  Sep  Oct  Nov  Dec\n",
       "0    0    0      0      0    1     0     0    0    0    0    0    0\n",
       "1    0    0      0      0    1     0     0    0    0    0    0    0\n",
       "2    0    0      0      0    1     0     0    0    0    0    0    0\n",
       "3    0    0      0      0    1     0     0    0    0    0    0    0\n",
       "4    0    0      0      0    1     0     0    0    0    0    0    0"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get binary vectors for weekdays, districts, and hours.\n",
    "month=pd.get_dummies(train.Dates.dt.month)\n",
    "neighborhood = pd.get_dummies(train.PdDistrict)\n",
    "neighborhood2 = pd.get_dummies(north.PdDistrict)\n",
    "hour = train.Dates.dt.hour\n",
    "hour = pd.get_dummies(hour) \n",
    "hour2=north.Dates.dt.hour\n",
    "hour2=pd.get_dummies(hour2)\n",
    "month.columns=['Jan','Feb','March','April','May','June','July','Aug','Sep','Oct','Nov','Dec']\n",
    "month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Build new array\n",
    "train_data = pd.concat([hour, month,neighborhood], axis=1)\n",
    "train_data['crime']=cData\n",
    "train_data['Y']=train['Y']\n",
    "train_data2 = pd.concat([hour2, neighborhood2], axis=1)\n",
    "train_data2['crime']=cData2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jan',\n",
       " 'Feb',\n",
       " 'March',\n",
       " 'April',\n",
       " 'May',\n",
       " 'June',\n",
       " 'July',\n",
       " 'Aug',\n",
       " 'Sep',\n",
       " 'Oct',\n",
       " 'Nov',\n",
       " 'Dec']"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months=month.columns.tolist()\n",
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 1,\n",
       " 2,\n",
       " 3,\n",
       " 4,\n",
       " 5,\n",
       " 6,\n",
       " 7,\n",
       " 8,\n",
       " 9,\n",
       " 10,\n",
       " 11,\n",
       " 12,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 16,\n",
       " 17,\n",
       " 18,\n",
       " 19,\n",
       " 20,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 'Jan',\n",
       " 'Feb',\n",
       " 'March',\n",
       " 'April',\n",
       " 'May',\n",
       " 'June',\n",
       " 'July',\n",
       " 'Aug',\n",
       " 'Sep',\n",
       " 'Oct',\n",
       " 'Nov',\n",
       " 'Dec',\n",
       " 'BAYVIEW',\n",
       " 'CENTRAL',\n",
       " 'INGLESIDE',\n",
       " 'MISSION',\n",
       " 'NORTHERN',\n",
       " 'PARK',\n",
       " 'RICHMOND',\n",
       " 'SOUTHERN',\n",
       " 'TARAVAL',\n",
       " 'TENDERLOIN',\n",
       " 'Y']"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = hours + months+ ['BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION',\n",
    " 'NORTHERN', 'PARK', 'RICHMOND', 'SOUTHERN', 'TARAVAL', 'TENDERLOIN', 'Y']\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "      <th>SOUTHERN</th>\n",
       "      <th>TARAVAL</th>\n",
       "      <th>TENDERLOIN</th>\n",
       "      <th>crime</th>\n",
       "      <th>Y</th>\n",
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       "  </thead>\n",
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       "      <td>16</td>\n",
       "      <td>37.800873</td>\n",
       "    </tr>\n",
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       "      <td>37.771541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3  4  5  6  7  8  9    ...      INGLESIDE  MISSION  NORTHERN  \\\n",
       "0  0  0  0  0  0  0  0  0  0  0    ...              0        0         1   \n",
       "1  0  0  0  0  0  0  0  0  0  0    ...              0        0         1   \n",
       "2  0  0  0  0  0  0  0  0  0  0    ...              0        0         1   \n",
       "3  0  0  0  0  0  0  0  0  0  0    ...              0        0         1   \n",
       "4  0  0  0  0  0  0  0  0  0  0    ...              0        0         0   \n",
       "\n",
       "   PARK  RICHMOND  SOUTHERN  TARAVAL  TENDERLOIN  crime          Y  \n",
       "0     0         0         0        0           0     37  37.774599  \n",
       "1     0         0         0        0           0     21  37.774599  \n",
       "2     0         0         0        0           0     21  37.800414  \n",
       "3     0         0         0        0           0     16  37.800873  \n",
       "4     1         0         0        0           0     16  37.771541  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 618,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "training, validation = train_test_split(train_data, train_size=.80, random_state=3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = BernoulliNB()\n",
    "#BernoulliNB--> this class requires samples to be represented as binary-valued feature vectors; \n",
    "#if handed any other kind of data, \n",
    "#a BernoulliNB instance may binarize its input (depending on the binarize parameter)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(training[features], training['crime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predicted = np.array(model.predict_proba(validation[features]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "accuracy=model.score(validation[features], validation['crime'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2225841353\n"
     ]
    }
   ],
   "source": [
    "print ( accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5851601067276961"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(validation['crime'], predicted) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result=pd.DataFrame(predicted, columns=CrimeData.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>ARSON</th>\n",
       "      <th>ASSAULT</th>\n",
       "      <th>BAD CHECKS</th>\n",
       "      <th>BRIBERY</th>\n",
       "      <th>BURGLARY</th>\n",
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       "      <th>DRIVING UNDER THE INFLUENCE</th>\n",
       "      <th>DRUG/NARCOTIC</th>\n",
       "      <th>DRUNKENNESS</th>\n",
       "      <th>EMBEZZLEMENT</th>\n",
       "      <th>...</th>\n",
       "      <th>SEX OFFENSES NON FORCIBLE</th>\n",
       "      <th>STOLEN PROPERTY</th>\n",
       "      <th>SUICIDE</th>\n",
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       "      <th>TRESPASS</th>\n",
       "      <th>VANDALISM</th>\n",
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       "      <th>WARRANTS</th>\n",
       "      <th>WEAPON LAWS</th>\n",
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       "      <td>0.022860</td>\n",
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       "      <td>0.000849</td>\n",
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       "      <td>0.002845</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.036815</td>\n",
       "      <td>3.196941e-07</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>0.086081</td>\n",
       "      <td>0.128098</td>\n",
       "      <td>0.021703</td>\n",
       "      <td>0.007775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003430</td>\n",
       "      <td>0.070202</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.049278</td>\n",
       "      <td>0.002446</td>\n",
       "      <td>0.016692</td>\n",
       "      <td>0.017152</td>\n",
       "      <td>0.005098</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.034704</td>\n",
       "      <td>6.305881e-07</td>\n",
       "      <td>0.002422</td>\n",
       "      <td>0.095256</td>\n",
       "      <td>0.131233</td>\n",
       "      <td>0.019844</td>\n",
       "      <td>0.008243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.106582</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.034883</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.021573</td>\n",
       "      <td>0.002496</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>0.032063</td>\n",
       "      <td>3.223179e-07</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.098961</td>\n",
       "      <td>0.178411</td>\n",
       "      <td>0.022362</td>\n",
       "      <td>0.015954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
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       "      ARSON   ASSAULT  BAD CHECKS   BRIBERY  BURGLARY  DISORDERLY CONDUCT  \\\n",
       "0  0.000861  0.094912    0.000067  0.000763  0.025956            0.005559   \n",
       "1  0.000565  0.071548    0.000420  0.000135  0.053951            0.003414   \n",
       "2  0.001289  0.067843    0.000292  0.000216  0.058457            0.000992   \n",
       "3  0.003430  0.070202    0.000116  0.000192  0.049278            0.002446   \n",
       "4  0.001556  0.106582    0.000068  0.000590  0.034883            0.000917   \n",
       "\n",
       "   DRIVING UNDER THE INFLUENCE  DRUG/NARCOTIC  DRUNKENNESS  EMBEZZLEMENT  \\\n",
       "0                     0.003486       0.058091     0.007503      0.000395   \n",
       "1                     0.001180       0.063885     0.004783      0.000716   \n",
       "2                     0.001373       0.022860     0.003433      0.000849   \n",
       "3                     0.016692       0.017152     0.005098      0.000274   \n",
       "4                     0.003007       0.021573     0.002496      0.000360   \n",
       "\n",
       "      ...       SEX OFFENSES NON FORCIBLE  STOLEN PROPERTY   SUICIDE  \\\n",
       "0     ...                        0.000244         0.003817  0.000592   \n",
       "1     ...                        0.000119         0.004799  0.000349   \n",
       "2     ...                        0.000229         0.002845  0.001110   \n",
       "3     ...                        0.000104         0.004254  0.000403   \n",
       "4     ...                        0.000261         0.003056  0.000738   \n",
       "\n",
       "   SUSPICIOUS OCC          TREA  TRESPASS  VANDALISM  VEHICLE THEFT  WARRANTS  \\\n",
       "0        0.025183  4.974754e-07  0.005732   0.059544       0.086547  0.043353   \n",
       "1        0.036404  5.045335e-07  0.004873   0.036816       0.062692  0.049441   \n",
       "2        0.036815  3.196941e-07  0.004011   0.086081       0.128098  0.021703   \n",
       "3        0.034704  6.305881e-07  0.002422   0.095256       0.131233  0.019844   \n",
       "4        0.032063  3.223179e-07  0.003127   0.098961       0.178411  0.022362   \n",
       "\n",
       "   WEAPON LAWS  \n",
       "0     0.012137  \n",
       "1     0.007493  \n",
       "2     0.007775  \n",
       "3     0.008243  \n",
       "4     0.015954  \n",
       "\n",
       "[5 rows x 39 columns]"
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