08_exploration of data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "df = pd.read_csv('~/Desktop/all_data.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Crime Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196450, 16)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('~/Desktop/all_data.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>incident_num</th>\n",
       "      <th>category</th>\n",
       "      <th>description</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>indicent_date</th>\n",
       "      <th>incident_time</th>\n",
       "      <th>pd_district</th>\n",
       "      <th>resolution</th>\n",
       "      <th>address</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>location</th>\n",
       "      <th>pd_id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>neighborhood_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151101177.0</td>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>00:45:00</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>ARREST, BOOKED</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>(37.775420706711,-122.403404791479)</td>\n",
       "      <td>1.510000e+13</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>33.0</td>\n",
       "      <td>South of Market</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141091334.0</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>BURGLARY OF STORE, FORCIBLE ENTRY</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>14:28:00</td>\n",
       "      <td>SOUTHERN</td>\n",
       "      <td>NONE</td>\n",
       "      <td>800 Block of BRYANT ST</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>(37.775420706711,-122.403404791479)</td>\n",
       "      <td>1.410000e+13</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>33.0</td>\n",
       "      <td>South of Market</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   incident_num  category                        description day_of_week  \\\n",
       "0   151101177.0  WARRANTS                     WARRANT ARREST      Friday   \n",
       "1   141091334.0  BURGLARY  BURGLARY OF STORE, FORCIBLE ENTRY   Wednesday   \n",
       "\n",
       "  indicent_date incident_time pd_district      resolution  \\\n",
       "0    2017-04-21      00:45:00    SOUTHERN  ARREST, BOOKED   \n",
       "1    2017-03-08      14:28:00    SOUTHERN            NONE   \n",
       "\n",
       "                  address           x          y  \\\n",
       "0  800 Block of BRYANT ST -122.403405  37.775421   \n",
       "1  800 Block of BRYANT ST -122.403405  37.775421   \n",
       "\n",
       "                              location         pd_id  \\\n",
       "0  (37.775420706711,-122.403404791479)  1.510000e+13   \n",
       "1  (37.775420706711,-122.403404791479)  1.410000e+13   \n",
       "\n",
       "                           geometry  index_right neighborhood_name  \n",
       "0  POINT (-122.4034048 37.77542071)         33.0   South of Market  \n",
       "1  POINT (-122.4034048 37.77542071)         33.0   South of Market  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "crime_types = df.groupby('category').count().reset_index()\n",
    "crime_types = crime_types[['category','incident_num']]\n",
    "crime_types = crime_types.sort_values(by='incident_num', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# df['incident_time'] =  pd.to_datetime(df['incident_time'], format='%H:%M:%S')\n",
    "import datetime as dt\n",
    "df['date_time'] = df['indicent_date'] + ' ' + df['incident_time']\n",
    "df['date_time'] = df['date_time'].apply(lambda x: \n",
    "                                    dt.datetime.strptime(x,'%Y-%m-%d %H:%M:%S'))\n",
    "\n",
    "df['hour_of_day'] = [r.hour for r in df['date_time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>description</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>indicent_date</th>\n",
       "      <th>incident_time</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>neighborhood_name</th>\n",
       "      <th>date_time</th>\n",
       "      <th>hour_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>00:45:00</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-04-21 00:45:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>BURGLARY OF STORE, FORCIBLE ENTRY</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>14:28:00</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-03-08 14:28:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category                        description day_of_week indicent_date  \\\n",
       "0  WARRANTS                     WARRANT ARREST      Friday    2017-04-21   \n",
       "1  BURGLARY  BURGLARY OF STORE, FORCIBLE ENTRY   Wednesday    2017-03-08   \n",
       "\n",
       "  incident_time           x          y                          geometry  \\\n",
       "0      00:45:00 -122.403405  37.775421  POINT (-122.4034048 37.77542071)   \n",
       "1      14:28:00 -122.403405  37.775421  POINT (-122.4034048 37.77542071)   \n",
       "\n",
       "  neighborhood_name           date_time  hour_of_day  \n",
       "0   South of Market 2017-04-21 00:45:00            0  \n",
       "1   South of Market 2017-03-08 14:28:00           14  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['incident_num','pd_district','location','pd_id','index_right','resolution','address'], axis=1, inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df['month'] = df['date_time'].apply(lambda x: \n",
    "                                    dt.datetime.strftime(x,'%b'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>description</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>neighborhood_name</th>\n",
       "      <th>date_time</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Friday</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-04-21 00:45:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>BURGLARY OF STORE, FORCIBLE ENTRY</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-03-08 14:28:00</td>\n",
       "      <td>14</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category                        description day_of_week           x  \\\n",
       "0  WARRANTS                     WARRANT ARREST      Friday -122.403405   \n",
       "1  BURGLARY  BURGLARY OF STORE, FORCIBLE ENTRY   Wednesday -122.403405   \n",
       "\n",
       "           y                          geometry neighborhood_name  \\\n",
       "0  37.775421  POINT (-122.4034048 37.77542071)   South of Market   \n",
       "1  37.775421  POINT (-122.4034048 37.77542071)   South of Market   \n",
       "\n",
       "            date_time  hour_of_day month  \n",
       "0 2017-04-21 00:45:00            0   Apr  \n",
       "1 2017-03-08 14:28:00           14   Mar  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['indicent_date','incident_time'], axis=1, inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# crime_by_category = df.groupby('category')['description'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# crime_by_category.sort_values(by='description', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def rename_categories(input_val):\n",
    "    if input_val in ['SEX OFFENSES, FORCIBLE','SEX OFFENSES, NON FORCIBLE','PORNOGRAPHY/OBSCENE MAT','PROSTITUTION']:\n",
    "        return 'SEX OFFENSES'\n",
    "    elif input_val in ['BAD CHECKS','EMBEZZLEMENT','FORGERY/COUNTERFEITING','FRAUD']:\n",
    "        return 'WHITE COLLAR'\n",
    "    elif input_val in ['DISORDERLY CONDUCT','DRUNKENNESS','DRIVING UNDER THE INFLUENCE']:\n",
    "        return 'DRUNK AND DISORDERLY'\n",
    "    elif input_val in ['ROBBERY','ASSAULT']:\n",
    "        return 'ROBBERY/ASSAULT'\n",
    "    elif input_val in ['TREA','LOITERING']:\n",
    "        return 'LOITERING'\n",
    "    else:\n",
    "        return input_val\n",
    "df = df.applymap(rename_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>53809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>25495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>22925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ROBBERY/ASSAULT</td>\n",
       "      <td>21852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>11533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>VEHICLE THEFT</td>\n",
       "      <td>8346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SUSPICIOUS OCC</td>\n",
       "      <td>7757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>7641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>7335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MISSING PERSON</td>\n",
       "      <td>5562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DRUG/NARCOTIC</td>\n",
       "      <td>5402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WHITE COLLAR</td>\n",
       "      <td>4307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SECONDARY CODES</td>\n",
       "      <td>2456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TRESPASS</td>\n",
       "      <td>2392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>WEAPON LAWS</td>\n",
       "      <td>2177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SEX OFFENSES</td>\n",
       "      <td>2042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DRUNK AND DISORDERLY</td>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>STOLEN PROPERTY</td>\n",
       "      <td>1154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RECOVERED VEHICLE</td>\n",
       "      <td>958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARSON</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KIDNAPPING</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LIQUOR LAWS</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RUNAWAY</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SUICIDE</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRIBERY</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EXTORTION</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FAMILY OFFENSES</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LOITERING</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GAMBLING</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                category  description\n",
       "9          LARCENY/THEFT        53809\n",
       "14        OTHER OFFENSES        25495\n",
       "13          NON-CRIMINAL        22925\n",
       "16       ROBBERY/ASSAULT        21852\n",
       "24             VANDALISM        11533\n",
       "25         VEHICLE THEFT         8346\n",
       "22        SUSPICIOUS OCC         7757\n",
       "2               BURGLARY         7641\n",
       "26              WARRANTS         7335\n",
       "12        MISSING PERSON         5562\n",
       "3          DRUG/NARCOTIC         5402\n",
       "28          WHITE COLLAR         4307\n",
       "18       SECONDARY CODES         2456\n",
       "23              TRESPASS         2392\n",
       "27           WEAPON LAWS         2177\n",
       "19          SEX OFFENSES         2042\n",
       "4   DRUNK AND DISORDERLY         1856\n",
       "20       STOLEN PROPERTY         1154\n",
       "15     RECOVERED VEHICLE          958\n",
       "0                  ARSON          378\n",
       "8             KIDNAPPING          320\n",
       "10           LIQUOR LAWS          179\n",
       "17               RUNAWAY          175\n",
       "21               SUICIDE           91\n",
       "1                BRIBERY           89\n",
       "5              EXTORTION           75\n",
       "6        FAMILY OFFENSES           69\n",
       "11             LOITERING           51\n",
       "7               GAMBLING           24"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_by_category = df.groupby('category')['description'].count().reset_index()\n",
    "crime_by_category.sort_values(by='description', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>description</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>neighborhood_name</th>\n",
       "      <th>date_time</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WARRANTS</td>\n",
       "      <td>WARRANT ARREST</td>\n",
       "      <td>Friday</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-04-21 00:45:00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>BURGLARY OF STORE, FORCIBLE ENTRY</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-03-08 14:28:00</td>\n",
       "      <td>14</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRESPASS</td>\n",
       "      <td>TRESPASSING</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>-122.458680</td>\n",
       "      <td>37.748155</td>\n",
       "      <td>POINT (-122.4586802 37.74815547)</td>\n",
       "      <td>Twin Peaks</td>\n",
       "      <td>2017-01-14 13:10:00</td>\n",
       "      <td>13</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>PETTY THEFT FROM LOCKED AUTO</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>-122.403405</td>\n",
       "      <td>37.775421</td>\n",
       "      <td>POINT (-122.4034048 37.77542071)</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>2017-01-19 08:45:00</td>\n",
       "      <td>8</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>AIDED CASE, MENTAL DISTURBED</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>-122.392350</td>\n",
       "      <td>37.792645</td>\n",
       "      <td>POINT (-122.3923498 37.79264509)</td>\n",
       "      <td>Financial District/South Beach</td>\n",
       "      <td>2017-01-19 16:08:00</td>\n",
       "      <td>16</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                        description day_of_week           x  \\\n",
       "0       WARRANTS                     WARRANT ARREST      Friday -122.403405   \n",
       "1       BURGLARY  BURGLARY OF STORE, FORCIBLE ENTRY   Wednesday -122.403405   \n",
       "2       TRESPASS                        TRESPASSING    Saturday -122.458680   \n",
       "3  LARCENY/THEFT       PETTY THEFT FROM LOCKED AUTO    Thursday -122.403405   \n",
       "4   NON-CRIMINAL       AIDED CASE, MENTAL DISTURBED    Thursday -122.392350   \n",
       "\n",
       "           y                          geometry  \\\n",
       "0  37.775421  POINT (-122.4034048 37.77542071)   \n",
       "1  37.775421  POINT (-122.4034048 37.77542071)   \n",
       "2  37.748155  POINT (-122.4586802 37.74815547)   \n",
       "3  37.775421  POINT (-122.4034048 37.77542071)   \n",
       "4  37.792645  POINT (-122.3923498 37.79264509)   \n",
       "\n",
       "                neighborhood_name           date_time  hour_of_day month  \n",
       "0                 South of Market 2017-04-21 00:45:00            0   Apr  \n",
       "1                 South of Market 2017-03-08 14:28:00           14   Mar  \n",
       "2                      Twin Peaks 2017-01-14 13:10:00           13   Jan  \n",
       "3                 South of Market 2017-01-19 08:45:00            8   Jan  \n",
       "4  Financial District/South Beach 2017-01-19 16:08:00           16   Jan  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# income_data = pickle.load(open('median_income_data.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# income_data.drop(['latitude','longitude','geometry'], axis=1, inplace=True)\n",
    "# income_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# new_df = df.merge(income_data, left_on='neighborhood_name', right_on='nhood', how='left')\n",
    "# new_df.drop(['nhood','index_right'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# mask = new_df['zip_code'].isnull()\n",
    "# new_df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_set = df.drop(['description','x','y','geometry','date_time'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>neighborhood_name</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102851</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>South of Market</td>\n",
       "      <td>19</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28508</th>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>Mission</td>\n",
       "      <td>11</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52270</th>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Pacific Heights</td>\n",
       "      <td>14</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49253</th>\n",
       "      <td>VEHICLE THEFT</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>West of Twin Peaks</td>\n",
       "      <td>11</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29782</th>\n",
       "      <td>ROBBERY/ASSAULT</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>Mission</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               category day_of_week   neighborhood_name  hour_of_day month\n",
       "102851    LARCENY/THEFT    Saturday     South of Market           19   Aug\n",
       "28508    OTHER OFFENSES      Sunday             Mission           11   Apr\n",
       "52270    OTHER OFFENSES     Tuesday     Pacific Heights           14   Mar\n",
       "49253     VEHICLE THEFT    Saturday  West of Twin Peaks           11   Jan\n",
       "29782   ROBBERY/ASSAULT    Thursday             Mission            0   Apr"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data_set = pd.get_dummies(data_set, columns=['day_of_week','neighborhood_name','month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>day_of_week_Friday</th>\n",
       "      <th>day_of_week_Monday</th>\n",
       "      <th>day_of_week_Saturday</th>\n",
       "      <th>day_of_week_Sunday</th>\n",
       "      <th>day_of_week_Thursday</th>\n",
       "      <th>day_of_week_Tuesday</th>\n",
       "      <th>day_of_week_Wednesday</th>\n",
       "      <th>neighborhood_name_Bayview Hunters Point</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>month_Jan</th>\n",
       "      <th>month_Jul</th>\n",
       "      <th>month_Jun</th>\n",
       "      <th>month_Mar</th>\n",
       "      <th>month_May</th>\n",
       "      <th>month_Nov</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WARRANTS</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>14</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRESPASS</td>\n",
       "      <td>13</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>8</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>16</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category  hour_of_day  day_of_week_Friday  day_of_week_Monday  \\\n",
       "0       WARRANTS            0                   1                   0   \n",
       "1       BURGLARY           14                   0                   0   \n",
       "2       TRESPASS           13                   0                   0   \n",
       "3  LARCENY/THEFT            8                   0                   0   \n",
       "4   NON-CRIMINAL           16                   0                   0   \n",
       "\n",
       "   day_of_week_Saturday  day_of_week_Sunday  day_of_week_Thursday  \\\n",
       "0                     0                   0                     0   \n",
       "1                     0                   0                     0   \n",
       "2                     1                   0                     0   \n",
       "3                     0                   0                     1   \n",
       "4                     0                   0                     1   \n",
       "\n",
       "   day_of_week_Tuesday  day_of_week_Wednesday  \\\n",
       "0                    0                      0   \n",
       "1                    0                      1   \n",
       "2                    0                      0   \n",
       "3                    0                      0   \n",
       "4                    0                      0   \n",
       "\n",
       "   neighborhood_name_Bayview Hunters Point    ...      month_Dec  month_Feb  \\\n",
       "0                                        0    ...              0          0   \n",
       "1                                        0    ...              0          0   \n",
       "2                                        0    ...              0          0   \n",
       "3                                        0    ...              0          0   \n",
       "4                                        0    ...              0          0   \n",
       "\n",
       "   month_Jan  month_Jul  month_Jun  month_Mar  month_May  month_Nov  \\\n",
       "0          0          0          0          0          0          0   \n",
       "1          0          0          0          1          0          0   \n",
       "2          1          0          0          0          0          0   \n",
       "3          1          0          0          0          0          0   \n",
       "4          1          0          0          0          0          0   \n",
       "\n",
       "   month_Oct  month_Sep  \n",
       "0          0          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          0          0  \n",
       "4          0          0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['category', 'hour_of_day', 'day_of_week_Friday', 'day_of_week_Monday',\n",
       "       'day_of_week_Saturday', 'day_of_week_Sunday', 'day_of_week_Thursday',\n",
       "       'day_of_week_Tuesday', 'day_of_week_Wednesday',\n",
       "       'neighborhood_name_Bayview Hunters Point',\n",
       "       'neighborhood_name_Bernal Heights',\n",
       "       'neighborhood_name_Castro/Upper Market', 'neighborhood_name_Chinatown',\n",
       "       'neighborhood_name_Excelsior',\n",
       "       'neighborhood_name_Financial District/South Beach',\n",
       "       'neighborhood_name_Glen Park', 'neighborhood_name_Golden Gate Park',\n",
       "       'neighborhood_name_Haight Ashbury', 'neighborhood_name_Hayes Valley',\n",
       "       'neighborhood_name_Inner Richmond', 'neighborhood_name_Inner Sunset',\n",
       "       'neighborhood_name_Japantown', 'neighborhood_name_Lakeshore',\n",
       "       'neighborhood_name_Lincoln Park', 'neighborhood_name_Lone Mountain/USF',\n",
       "       'neighborhood_name_Marina', 'neighborhood_name_McLaren Park',\n",
       "       'neighborhood_name_Mission', 'neighborhood_name_Mission Bay',\n",
       "       'neighborhood_name_Nob Hill', 'neighborhood_name_Noe Valley',\n",
       "       'neighborhood_name_North Beach',\n",
       "       'neighborhood_name_Oceanview/Merced/Ingleside',\n",
       "       'neighborhood_name_Outer Mission', 'neighborhood_name_Outer Richmond',\n",
       "       'neighborhood_name_Pacific Heights', 'neighborhood_name_Portola',\n",
       "       'neighborhood_name_Potrero Hill', 'neighborhood_name_Presidio',\n",
       "       'neighborhood_name_Presidio Heights', 'neighborhood_name_Russian Hill',\n",
       "       'neighborhood_name_Seacliff', 'neighborhood_name_South of Market',\n",
       "       'neighborhood_name_Sunset/Parkside', 'neighborhood_name_Tenderloin',\n",
       "       'neighborhood_name_Treasure Island', 'neighborhood_name_Twin Peaks',\n",
       "       'neighborhood_name_Visitacion Valley',\n",
       "       'neighborhood_name_West of Twin Peaks',\n",
       "       'neighborhood_name_Western Addition', 'month_Apr', 'month_Aug',\n",
       "       'month_Dec', 'month_Feb', 'month_Jan', 'month_Jul', 'month_Jun',\n",
       "       'month_Mar', 'month_May', 'month_Nov', 'month_Oct', 'month_Sep'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = data_set['category']\n",
    "X = data_set.drop('category', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def baseline_predictor(input_data):\n",
    "    predictor_dict = dict(input_data.value_counts())\n",
    "    most_freq = max(predictor_dict, key=predictor_dict.get)\n",
    "    return [most_freq for i in range(len(input_data))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=4444)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": 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>category</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>day_of_week_Friday</th>\n",
       "      <th>day_of_week_Monday</th>\n",
       "      <th>day_of_week_Saturday</th>\n",
       "      <th>day_of_week_Sunday</th>\n",
       "      <th>day_of_week_Thursday</th>\n",
       "      <th>day_of_week_Tuesday</th>\n",
       "      <th>day_of_week_Wednesday</th>\n",
       "      <th>neighborhood_name_Bayview Hunters Point</th>\n",
       "      <th>...</th>\n",
       "      <th>month_Dec</th>\n",
       "      <th>month_Feb</th>\n",
       "      <th>month_Jan</th>\n",
       "      <th>month_Jul</th>\n",
       "      <th>month_Jun</th>\n",
       "      <th>month_Mar</th>\n",
       "      <th>month_May</th>\n",
       "      <th>month_Nov</th>\n",
       "      <th>month_Oct</th>\n",
       "      <th>month_Sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18099</th>\n",
       "      <td>SUSPICIOUS OCC</td>\n",
       "      <td>12</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9295</th>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>16</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>...</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>144256</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>19</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>...</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>90568</th>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>14</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163791</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>11</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153479</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>19</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22979</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>14</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128724</th>\n",
       "      <td>VANDALISM</td>\n",
       "      <td>21</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182631</th>\n",
       "      <td>LARCENY/THEFT</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137016</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>14</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>...</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",
       "    <tr>\n",
       "      <th>149532</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>18</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>...</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>134410</th>\n",
       "      <td>VEHICLE THEFT</td>\n",
       "      <td>6</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>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155843</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>20</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146475</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>9</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>...</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>172129</th>\n",
       "      <td>NON-CRIMINAL</td>\n",
       "      <td>14</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>...</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",
       "    <tr>\n",
       "      <th>28962</th>\n",
       "      <td>OTHER OFFENSES</td>\n",
       "      <td>8</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127177</th>\n",
       "      <td>WHITE COLLAR</td>\n",
       "      <td>4</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83225</th>\n",
       "      <td>ROBBERY/ASSAULT</td>\n",
       "      <td>19</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178703</th>\n",
       "      <td>LARCENY/THEFT</td>\n",
       "      <td>21</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>1</td>\n",
       "      <td>...</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>129872</th>\n",
       "      <td>MISSING PERSON</td>\n",
       "      <td>8</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>...</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",
       "<p>20 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               category  hour_of_day  day_of_week_Friday  day_of_week_Monday  \\\n",
       "18099    SUSPICIOUS OCC           12                   0                   0   \n",
       "9295     OTHER OFFENSES           16                   0                   0   \n",
       "144256    LARCENY/THEFT           19                   0                   0   \n",
       "90568         VANDALISM           14                   0                   0   \n",
       "163791    LARCENY/THEFT           11                   0                   0   \n",
       "153479    LARCENY/THEFT           19                   0                   1   \n",
       "22979      NON-CRIMINAL           14                   0                   0   \n",
       "128724        VANDALISM           21                   1                   0   \n",
       "182631    LARCENY/THEFT            0                   0                   0   \n",
       "137016     NON-CRIMINAL           14                   0                   0   \n",
       "149532    LARCENY/THEFT           18                   0                   0   \n",
       "134410    VEHICLE THEFT            6                   0                   0   \n",
       "155843    LARCENY/THEFT           20                   0                   0   \n",
       "146475    LARCENY/THEFT            9                   0                   0   \n",
       "172129     NON-CRIMINAL           14                   1                   0   \n",
       "28962    OTHER OFFENSES            8                   0                   0   \n",
       "127177     WHITE COLLAR            4                   0                   1   \n",
       "83225   ROBBERY/ASSAULT           19                   0                   0   \n",
       "178703    LARCENY/THEFT           21                   0                   1   \n",
       "129872   MISSING PERSON            8                   0                   0   \n",
       "\n",
       "        day_of_week_Saturday  day_of_week_Sunday  day_of_week_Thursday  \\\n",
       "18099                      0                   0                     0   \n",
       "9295                       0                   0                     0   \n",
       "144256                     0                   0                     0   \n",
       "90568                      0                   0                     0   \n",
       "163791                     0                   0                     1   \n",
       "153479                     0                   0                     0   \n",
       "22979                      0                   0                     1   \n",
       "128724                     0                   0                     0   \n",
       "182631                     1                   0                     0   \n",
       "137016                     0                   0                     0   \n",
       "149532                     0                   0                     1   \n",
       "134410                     0                   0                     0   \n",
       "155843                     0                   0                     0   \n",
       "146475                     0                   0                     0   \n",
       "172129                     0                   0                     0   \n",
       "28962                      0                   0                     0   \n",
       "127177                     0                   0                     0   \n",
       "83225                      0                   0                     0   \n",
       "178703                     0                   0                     0   \n",
       "129872                     0                   0                     0   \n",
       "\n",
       "        day_of_week_Tuesday  day_of_week_Wednesday  \\\n",
       "18099                     0                      1   \n",
       "9295                      0                      1   \n",
       "144256                    1                      0   \n",
       "90568                     0                      1   \n",
       "163791                    0                      0   \n",
       "153479                    0                      0   \n",
       "22979                     0                      0   \n",
       "128724                    0                      0   \n",
       "182631                    0                      0   \n",
       "137016                    1                      0   \n",
       "149532                    0                      0   \n",
       "134410                    1                      0   \n",
       "155843                    0                      1   \n",
       "146475                    1                      0   \n",
       "172129                    0                      0   \n",
       "28962                     1                      0   \n",
       "127177                    0                      0   \n",
       "83225                     1                      0   \n",
       "178703                    0                      0   \n",
       "129872                    0                      1   \n",
       "\n",
       "        neighborhood_name_Bayview Hunters Point    ...      month_Dec  \\\n",
       "18099                                         0    ...              0   \n",
       "9295                                          0    ...              0   \n",
       "144256                                        0    ...              1   \n",
       "90568                                         0    ...              0   \n",
       "163791                                        0    ...              0   \n",
       "153479                                        0    ...              0   \n",
       "22979                                         1    ...              0   \n",
       "128724                                        0    ...              0   \n",
       "182631                                        0    ...              0   \n",
       "137016                                        0    ...              0   \n",
       "149532                                        0    ...              1   \n",
       "134410                                        1    ...              0   \n",
       "155843                                        0    ...              0   \n",
       "146475                                        0    ...              1   \n",
       "172129                                        0    ...              0   \n",
       "28962                                         0    ...              0   \n",
       "127177                                        0    ...              0   \n",
       "83225                                         0    ...              0   \n",
       "178703                                        1    ...              0   \n",
       "129872                                        0    ...              0   \n",
       "\n",
       "        month_Feb  month_Jan  month_Jul  month_Jun  month_Mar  month_May  \\\n",
       "18099           0          0          0          0          1          0   \n",
       "9295            1          0          0          0          0          0   \n",
       "144256          0          0          0          0          0          0   \n",
       "90568           0          0          1          0          0          0   \n",
       "163791          0          0          1          0          0          0   \n",
       "153479          0          1          0          0          0          0   \n",
       "22979           0          0          0          0          1          0   \n",
       "128724          0          0          0          0          0          0   \n",
       "182631          0          1          0          0          0          0   \n",
       "137016          0          0          0          0          0          0   \n",
       "149532          0          0          0          0          0          0   \n",
       "134410          0          0          0          0          0          0   \n",
       "155843          0          0          0          0          1          0   \n",
       "146475          0          0          0          0          0          0   \n",
       "172129          0          0          0          0          0          0   \n",
       "28962           0          0          0          0          1          0   \n",
       "127177          0          0          0          0          0          0   \n",
       "83225           0          0          0          1          0          0   \n",
       "178703          1          0          0          0          0          0   \n",
       "129872          0          0          0          0          0          0   \n",
       "\n",
       "        month_Nov  month_Oct  month_Sep  \n",
       "18099           0          0          0  \n",
       "9295            0          0          0  \n",
       "144256          0          0          0  \n",
       "90568           0          0          0  \n",
       "163791          0          0          0  \n",
       "153479          0          0          0  \n",
       "22979           0          0          0  \n",
       "128724          0          1          0  \n",
       "182631          0          0          0  \n",
       "137016          1          0          0  \n",
       "149532          0          0          0  \n",
       "134410          1          0          0  \n",
       "155843          0          0          0  \n",
       "146475          0          0          0  \n",
       "172129          1          0          0  \n",
       "28962           0          0          0  \n",
       "127177          0          1          0  \n",
       "83225           0          0          0  \n",
       "178703          0          0          0  \n",
       "129872          1          0          0  \n",
       "\n",
       "[20 rows x 62 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0.18613726987358956)\n",
      "(2, 0.21496564011198779)\n",
      "(3, 0.22224484601679817)\n",
      "(4, 0.22156613217951981)\n",
      "(5, 0.22677526088063121)\n",
      "(6, 0.23459743785526427)\n",
      "(7, 0.24155425468736744)\n",
      "(8, 0.24732332230423348)\n",
      "(9, 0.25027572749639432)\n",
      "(10, 0.2521930940867057)\n",
      "(11, 0.25482311020615933)\n",
      "(12, 0.25606176295919231)\n",
      "(13, 0.25741919063374907)\n",
      "(14, 0.25825061508441505)\n",
      "(15, 0.26145753796555526)\n",
      "(16, 0.26267922287265633)\n",
      "(17, 0.26403665054721304)\n",
      "(18, 0.26437600746585221)\n",
      "(19, 0.26488504284381098)\n",
      "(20, 0.26556375668108934)\n"
     ]
    }
   ],
   "source": [
    "knn_scores = []\n",
    "\n",
    "for i in range(1, 21):\n",
    "    \n",
    "    # create and fit model to training data\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # predict y values agaisnt test data\n",
    "    y_pred_test = knn.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred_test)\n",
    "    \n",
    "    # store scores\n",
    "    tup = (i, score)\n",
    "    print(tup)\n",
    "    knn_scores.append(tup)\n",
    "    \n",
    "knn_scores = dict(knn_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x125900e48>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fmDdvfjaQHemNb3wTN9zwXqqqKqiqquXw4VaOP95w5plnc/31V+M4Dm99658TjUZZt249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\nqQMYyiRJkjqAoUySJKkDGMokSZI6wP8DE7O7xK8Ege0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11efb0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(list(knn_scores.keys()) , list(knn_scores.values()), alpha = 0.5, linewidth = 3.0, label = 'Accuracy scores')\n",
    "plt.title('Accuracy scores at different values of K')\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 0.26729447696614916)\n",
      "(22, 0.26775260880631202)\n",
      "(23, 0.26790531941969969)\n",
      "(24, 0.26885551879188935)\n",
      "(25, 0.27072198184440488)\n",
      "(26, 0.27058623907694918)\n",
      "(27, 0.27034868923390176)\n",
      "(28, 0.27056927123101721)\n",
      "(29, 0.2720963773648935)\n",
      "(30, 0.27318231950453892)\n",
      "(31, 0.27355561211504198)\n",
      "(32, 0.27285993043183165)\n",
      "(33, 0.27316535165860695)\n",
      "(34, 0.27369135488249768)\n",
      "(35, 0.27340290150165436)\n",
      "(36, 0.2737083227284296)\n",
      "(37, 0.27477729702214304)\n",
      "(38, 0.27523542886230595)\n",
      "(39, 0.27582930346992451)\n",
      "(40, 0.27615169254263172)\n",
      "(41, 0.27535420378382963)\n",
      "(42, 0.27616866038856369)\n",
      "(43, 0.27620259608042758)\n",
      "(44, 0.27591414269958431)\n",
      "(45, 0.27688130991770593)\n",
      "(46, 0.27722066683634511)\n",
      "(47, 0.27723763468227708)\n",
      "(48, 0.27713582760668531)\n",
      "(49, 0.27723763468227708)\n",
      "(50, 0.27671163145838634)\n",
      "(51, 0.27706795622295749)\n",
      "(52, 0.27759395944684823)\n",
      "(53, 0.27810299482480699)\n",
      "(54, 0.27796725205735134)\n",
      "(55, 0.27810299482480699)\n",
      "(56, 0.27784847713582761)\n",
      "(57, 0.27803512344107917)\n",
      "(58, 0.27845931958937814)\n",
      "(59, 0.27818783405446679)\n",
      "(60, 0.27861203020276576)\n",
      "(61, 0.27845931958937814)\n",
      "(62, 0.2790531941969967)\n",
      "(63, 0.27922287265631629)\n",
      "(64, 0.27881564435394929)\n",
      "(65, 0.2794604224993637)\n",
      "(66, 0.27918893696445235)\n",
      "(67, 0.27907016204292867)\n",
      "(68, 0.27930771188597608)\n",
      "(69, 0.27869686943242555)\n",
      "(70, 0.27949435819122764)\n",
      "(71, 0.27929074404004411)\n",
      "(72, 0.27876474081615338)\n",
      "(73, 0.27832357682192244)\n",
      "(74, 0.27828964113005855)\n",
      "(75, 0.27817086620853482)\n",
      "(76, 0.27851022312717399)\n",
      "(77, 0.27844235174344617)\n",
      "(78, 0.27930771188597608)\n",
      "(79, 0.27900229065920079)\n",
      "(80, 0.27886654789174514)\n",
      "(81, 0.27900229065920079)\n"
     ]
    }
   ],
   "source": [
    "knn_scores = []\n",
    "\n",
    "for i in range(21, 101):\n",
    "    \n",
    "    # create and fit model to training data\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # predict y values agaisnt test data\n",
    "    y_pred_test = knn.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred_test)\n",
    "    \n",
    "    # store scores\n",
    "    tup = (i, score)\n",
    "    print(tup)\n",
    "    knn_scores.append(tup)\n",
    "    \n",
    "knn_scores = dict(knn_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(list(knn_scores.keys()) , list(knn_scores.values()), alpha = 0.5, linewidth = 3.0, label = 'Accuracy scores')\n",
    "plt.title('Accuracy scores at different values of K')\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}