4.1. Model and feature importance -CRIME.ipynb

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   "source": [
    "# importing necessary libraries\n",
    "\n",
    "# Plotting libraries/settings# Plott \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "# Data manipulation libraries/settings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from functools import reduce;\n",
    "\n",
    "# Machine learning library/functions\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "np.random.seed(42)\n",
    "\n",
    "# Additional librariers used for modeling and evaluation\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV \n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout':True})\n",
    "\n",
    "# setting visual displays\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TPOT used to optimize and model pipelines\n",
    "\n",
    "from tpot import TPOTClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing crime data\n",
    "\n",
    "test18 = pd.read_csv('./Crime_Incidents_in_2018.csv')\n",
    "df17 = pd.read_csv('./Crime_Incidents_in_2017.csv')\n",
    "df16 = pd.read_csv('./Crime_Incidents_in_2016.csv')\n",
    "df15 = pd.read_csv('./Crime_Incidents_in_2015.csv')\n",
    "df14 = pd.read_csv('./Crime_Incidents_in_2014.csv')\n",
    "df13 = pd.read_csv('./Crime_Incidents_in_2013.csv')\n",
    "df12 = pd.read_csv('./Crime_Incidents_in_2012.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the data, most EDA was done in the EDA notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>CCN</th>\n",
       "      <th>REPORT_DAT</th>\n",
       "      <th>SHIFT</th>\n",
       "      <th>METHOD</th>\n",
       "      <th>OFFENSE</th>\n",
       "      <th>BLOCK</th>\n",
       "      <th>XBLOCK</th>\n",
       "      <th>YBLOCK</th>\n",
       "      <th>...</th>\n",
       "      <th>NEIGHBORHOOD_CLUSTER</th>\n",
       "      <th>BLOCK_GROUP</th>\n",
       "      <th>CENSUS_TRACT</th>\n",
       "      <th>VOTING_PRECINCT</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>BID</th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>END_DATE</th>\n",
       "      <th>OBJECTID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-77.013517</td>\n",
       "      <td>38.920967</td>\n",
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       "      <td>2017-06-30T08:28:54.000Z</td>\n",
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       "      <td>OTHERS</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>100 - 199 BLOCK OF BRYANT STREET NW</td>\n",
       "      <td>398828.0</td>\n",
       "      <td>139232.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Cluster 21</td>\n",
       "      <td>003301 3</td>\n",
       "      <td>3301.0</td>\n",
       "      <td>Precinct 135</td>\n",
       "      <td>38.920959</td>\n",
       "      <td>-77.013515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-22T18:00:49.000Z</td>\n",
       "      <td>2017-06-30T07:30:47.000Z</td>\n",
       "      <td>165452237</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-76.922956</td>\n",
       "      <td>38.903484</td>\n",
       "      <td>17111426</td>\n",
       "      <td>2017-06-30T08:46:47.000Z</td>\n",
       "      <td>DAY</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>934 - 1099 BLOCK OF EASTERN AVENUE NE</td>\n",
       "      <td>406683.0</td>\n",
       "      <td>137294.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Cluster 31</td>\n",
       "      <td>007807 2</td>\n",
       "      <td>7807.0</td>\n",
       "      <td>Precinct 95</td>\n",
       "      <td>38.903476</td>\n",
       "      <td>-76.922953</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>38.904648</td>\n",
       "      <td>17111445</td>\n",
       "      <td>2017-06-30T10:15:18.000Z</td>\n",
       "      <td>DAY</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>4200 - 4299 BLOCK OF LANE PLACE NE</td>\n",
       "      <td>404992.0</td>\n",
       "      <td>137422.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Cluster 29</td>\n",
       "      <td>009601 1</td>\n",
       "      <td>9601.0</td>\n",
       "      <td>Precinct 92</td>\n",
       "      <td>38.904640</td>\n",
       "      <td>-76.942448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-30T09:26:43.000Z</td>\n",
       "      <td>2017-06-30T10:15:16.000Z</td>\n",
       "      <td>165452239</td>\n",
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       "<p>3 rows × 24 columns</p>\n",
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      "text/plain": [
       "           X          Y       CCN                REPORT_DAT SHIFT  METHOD  \\\n",
       "0 -77.013517  38.920967  17111418  2017-06-30T08:28:54.000Z   DAY  OTHERS   \n",
       "1 -76.922956  38.903484  17111426  2017-06-30T08:46:47.000Z   DAY  OTHERS   \n",
       "2 -76.942450  38.904648  17111445  2017-06-30T10:15:18.000Z   DAY  OTHERS   \n",
       "\n",
       "        OFFENSE                                  BLOCK    XBLOCK    YBLOCK  \\\n",
       "0   THEFT/OTHER    100 - 199 BLOCK OF BRYANT STREET NW  398828.0  139232.0   \n",
       "1   THEFT/OTHER  934 - 1099 BLOCK OF EASTERN AVENUE NE  406683.0  137294.0   \n",
       "2  THEFT F/AUTO     4200 - 4299 BLOCK OF LANE PLACE NE  404992.0  137422.0   \n",
       "\n",
       "     ...      NEIGHBORHOOD_CLUSTER BLOCK_GROUP  CENSUS_TRACT  VOTING_PRECINCT  \\\n",
       "0    ...                Cluster 21    003301 3        3301.0     Precinct 135   \n",
       "1    ...                Cluster 31    007807 2        7807.0      Precinct 95   \n",
       "2    ...                Cluster 29    009601 1        9601.0      Precinct 92   \n",
       "\n",
       "    LATITUDE  LONGITUDE  BID                START_DATE  \\\n",
       "0  38.920959 -77.013515  NaN  2017-06-22T18:00:49.000Z   \n",
       "1  38.903476 -76.922953  NaN  2017-06-30T08:02:00.000Z   \n",
       "2  38.904640 -76.942448  NaN  2017-06-30T09:26:43.000Z   \n",
       "\n",
       "                   END_DATE   OBJECTID  \n",
       "0  2017-06-30T07:30:47.000Z  165452237  \n",
       "1  2017-06-30T08:46:45.000Z  165452238  \n",
       "2  2017-06-30T10:15:16.000Z  165452239  \n",
       "\n",
       "[3 rows x 24 columns]"
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     "execution_count": 76,
     "metadata": {},
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   ],
   "source": [
    "df17.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y', 'CCN', 'REPORT_DAT', 'SHIFT', 'METHOD', 'OFFENSE', 'BLOCK',\n",
       "       'XBLOCK', 'YBLOCK', 'WARD', 'ANC', 'DISTRICT', 'PSA',\n",
       "       'NEIGHBORHOOD_CLUSTER', 'BLOCK_GROUP', 'CENSUS_TRACT',\n",
       "       'VOTING_PRECINCT', 'LATITUDE', 'LONGITUDE', 'BID', 'START_DATE',\n",
       "       'END_DATE', 'OBJECTID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df17.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y', 'CCN', 'REPORT_DAT', 'SHIFT', 'METHOD', 'OFFENSE', 'BLOCK',\n",
       "       'XBLOCK', 'YBLOCK', 'WARD', 'ANC', 'DISTRICT', 'PSA',\n",
       "       'NEIGHBORHOOD_CLUSTER', 'BLOCK_GROUP', 'CENSUS_TRACT',\n",
       "       'VOTING_PRECINCT', 'LATITUDE', 'LONGITUDE', 'BID', 'START_DATE',\n",
       "       'END_DATE', 'OBJECTID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- Unused code, saved for reference as to how to combine multiple dataframes\n",
    "\n",
    "dataframes = [df17, df16, df15, df14, df13, df12]\n",
    "\n",
    "df_merged = reduce(lambda  left,right: pd.merge(left,right,on=['OBJECTID'],\n",
    "                                            how='outer'), dataframes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Pandas to identify crime by year and ward/cluster, used to export to a CSV to do further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df17_ncluster = df17.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEIGHBORHOOD_CLUSTER  OFFENSE                   \n",
       "Cluster 1             THEFT/OTHER                   305\n",
       "                      THEFT F/AUTO                  282\n",
       "                      MOTOR VEHICLE THEFT            40\n",
       "                      ROBBERY                        34\n",
       "                      ASSAULT W/DANGEROUS WEAPON     24\n",
       "Name: OFFENSE, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df17_ncluster.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df17_ward = df17.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# exported all df_ward groupby's to a csv, and then manually combined the csv's\n",
    "\n",
    "df12_ward.to_csv('./wards12.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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     "execution_count": 82,
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      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df17.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df16_ncluster = df16.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df16_ward = df16.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fa6672438>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df16.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df15_ncluster = df15.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df15_ward = df15.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WARD  OFFENSE                   \n",
       "1     THEFT F/AUTO                  2223\n",
       "      THEFT/OTHER                   1863\n",
       "      ROBBERY                        481\n",
       "      MOTOR VEHICLE THEFT            226\n",
       "      BURGLARY                       219\n",
       "      ASSAULT W/DANGEROUS WEAPON     192\n",
       "      SEX ABUSE                       42\n",
       "      HOMICIDE                        11\n",
       "      ARSON                            1\n",
       "2     THEFT/OTHER                   4015\n",
       "      THEFT F/AUTO                  1789\n",
       "      ROBBERY                        283\n",
       "      MOTOR VEHICLE THEFT            227\n",
       "      BURGLARY                       218\n",
       "      ASSAULT W/DANGEROUS WEAPON     186\n",
       "      SEX ABUSE                       50\n",
       "      HOMICIDE                         4\n",
       "3     THEFT/OTHER                    854\n",
       "      THEFT F/AUTO                   760\n",
       "      BURGLARY                       116\n",
       "      MOTOR VEHICLE THEFT            101\n",
       "      ROBBERY                         55\n",
       "      ASSAULT W/DANGEROUS WEAPON      14\n",
       "      SEX ABUSE                       12\n",
       "      HOMICIDE                         5\n",
       "4     THEFT F/AUTO                  1418\n",
       "      THEFT/OTHER                    982\n",
       "      ROBBERY                        394\n",
       "      MOTOR VEHICLE THEFT            294\n",
       "      BURGLARY                       252\n",
       "                                    ... \n",
       "5     SEX ABUSE                       50\n",
       "      HOMICIDE                        28\n",
       "      ARSON                            2\n",
       "6     THEFT/OTHER                   2385\n",
       "      THEFT F/AUTO                  2053\n",
       "      ROBBERY                        552\n",
       "      MOTOR VEHICLE THEFT            467\n",
       "      BURGLARY                       306\n",
       "      ASSAULT W/DANGEROUS WEAPON     278\n",
       "      SEX ABUSE                       35\n",
       "      HOMICIDE                        17\n",
       "      ARSON                            2\n",
       "7     THEFT/OTHER                   1282\n",
       "      THEFT F/AUTO                   934\n",
       "      MOTOR VEHICLE THEFT            604\n",
       "      ASSAULT W/DANGEROUS WEAPON     586\n",
       "      ROBBERY                        549\n",
       "      BURGLARY                       420\n",
       "      SEX ABUSE                       52\n",
       "      HOMICIDE                        32\n",
       "      ARSON                            4\n",
       "8     THEFT/OTHER                   1119\n",
       "      THEFT F/AUTO                   683\n",
       "      ASSAULT W/DANGEROUS WEAPON     610\n",
       "      MOTOR VEHICLE THEFT            541\n",
       "      ROBBERY                        529\n",
       "      BURGLARY                       439\n",
       "      HOMICIDE                        56\n",
       "      SEX ABUSE                       54\n",
       "      ARSON                            7\n",
       "Name: OFFENSE, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df15_ward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fa4d44208>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df15.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df14_ncluster = df14.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df14_ward = df14.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fa4dab5c0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df14.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df13_ncluster = df13.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df13_ward = df13.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fa585e860>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df13.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df12_ncluster = df12.groupby('NEIGHBORHOOD_CLUSTER')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df12_ward = df12.groupby('WARD')['OFFENSE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fa5d9e9e8>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Ward vs Number of Crimes to identify annual trends\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "plt.ylabel('Ward', size =15)\n",
    "plt.xlabel('Number of Crimes', size =15)\n",
    "plt.title('Ward by Number of Crimes', size=15)\n",
    "df12.groupby(['WARD']).count()['OFFENSE'].sort_values().plot(kind = 'barh',ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./crime_by_wards.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>1933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Ward              Offense  Count\n",
       "0  2017     1         THEFT F/AUTO   1933\n",
       "1  2017     1          THEFT/OTHER   1853\n",
       "2  2017     1              ROBBERY    272\n",
       "3  2017     1  MOTOR VEHICLE THEFT    218\n",
       "4  2017     1             BURGLARY    162"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['THEFT F/AUTO', 'THEFT/OTHER', 'ROBBERY', 'MOTOR VEHICLE THEFT',\n",
       "       'BURGLARY', 'ASSAULT W/DANGEROUS WEAPON', 'SEX ABUSE', 'HOMICIDE',\n",
       "       'ARSON'], dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Offense.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT F/AUTO</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward       Offense  Count\n",
       "59   2017     8  THEFT F/AUTO    677\n",
       "126  2016     8  THEFT F/AUTO    777\n",
       "196  2015     8  THEFT F/AUTO    683\n",
       "267  2014     8  THEFT F/AUTO    691\n",
       "339  2013     8  THEFT F/AUTO    593\n",
       "409  2012     8  THEFT F/AUTO    613"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'THEFT F/AUTO')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>THEFT/OTHER</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward      Offense  Count\n",
       "58   2017     8  THEFT/OTHER   1157\n",
       "125  2016     8  THEFT/OTHER   1200\n",
       "195  2015     8  THEFT/OTHER   1119\n",
       "266  2014     8  THEFT/OTHER   1060\n",
       "335  2013     8  THEFT/OTHER   1020\n",
       "406  2012     8  THEFT/OTHER    830"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'THEFT/OTHER')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>ROBBERY</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward  Offense  Count\n",
       "61   2017     8  ROBBERY    362\n",
       "128  2016     8  ROBBERY    504\n",
       "199  2015     8  ROBBERY    529\n",
       "270  2014     8  ROBBERY    549\n",
       "337  2013     8  ROBBERY    714\n",
       "407  2012     8  ROBBERY    796"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'ROBBERY')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>MOTOR VEHICLE THEFT</td>\n",
       "      <td>552</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward              Offense  Count\n",
       "62   2017     8  MOTOR VEHICLE THEFT    343\n",
       "129  2016     8  MOTOR VEHICLE THEFT    405\n",
       "198  2015     8  MOTOR VEHICLE THEFT    541\n",
       "271  2014     8  MOTOR VEHICLE THEFT    459\n",
       "340  2013     8  MOTOR VEHICLE THEFT    579\n",
       "410  2012     8  MOTOR VEHICLE THEFT    552"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'MOTOR VEHICLE THEFT')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward   Offense  Count\n",
       "63   2017     8  BURGLARY    315\n",
       "130  2016     8  BURGLARY    308\n",
       "200  2015     8  BURGLARY    439\n",
       "269  2014     8  BURGLARY    597\n",
       "336  2013     8  BURGLARY    752\n",
       "405  2012     8  BURGLARY    842"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'BURGLARY')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
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       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>ASSAULT W/DANGEROUS WEAPON</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward                     Offense  Count\n",
       "60   2017     8  ASSAULT W/DANGEROUS WEAPON    470\n",
       "127  2016     8  ASSAULT W/DANGEROUS WEAPON    591\n",
       "197  2015     8  ASSAULT W/DANGEROUS WEAPON    610\n",
       "268  2014     8  ASSAULT W/DANGEROUS WEAPON    620\n",
       "338  2013     8  ASSAULT W/DANGEROUS WEAPON    668\n",
       "408  2012     8  ASSAULT W/DANGEROUS WEAPON    656"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'ASSAULT W/DANGEROUS WEAPON')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
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       "      <td>SEX ABUSE</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward    Offense  Count\n",
       "64   2017     8  SEX ABUSE     48\n",
       "131  2016     8  SEX ABUSE     57\n",
       "202  2015     8  SEX ABUSE     54\n",
       "272  2014     8  SEX ABUSE     65\n",
       "341  2013     8  SEX ABUSE     62\n",
       "411  2012     8  SEX ABUSE     42"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'SEX ABUSE')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward   Offense  Count\n",
       "65   2017     8  HOMICIDE     46\n",
       "132  2016     8  HOMICIDE     46\n",
       "201  2015     8  HOMICIDE     56\n",
       "273  2014     8  HOMICIDE     35\n",
       "342  2013     8  HOMICIDE     27\n",
       "412  2012     8  HOMICIDE     23"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'HOMICIDE')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Offense</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>ARSON</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Ward Offense  Count\n",
       "66   2017     8   ARSON      1\n",
       "133  2016     8   ARSON      3\n",
       "203  2015     8   ARSON      7\n",
       "274  2014     8   ARSON      6\n",
       "343  2013     8   ARSON     15\n",
       "413  2012     8   ARSON      8"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.Ward == 8) & (df.Offense == 'ARSON')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing DC3 data, which has been through data cleaning/EDA already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dc3 = pd.read_csv('./dc_crime3.csv')\n",
    "ward = pd.read_csv('./warddata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'Unnamed: 0.1.1', 'Unnamed: 0.1.1.1', 'X',\n",
       "       'Y', 'CCN', 'REPORT_DAT', 'SHIFT', 'METHOD', 'OFFENSE', 'XBLOCK',\n",
       "       'YBLOCK', 'WARD', 'ANC', 'DISTRICT', 'PSA', 'NEIGHBORHOOD_CLUSTER',\n",
       "       'CENSUS_TRACT', 'VOTING_PRECINCT', 'LATITUDE', 'LONGITUDE', 'BID',\n",
       "       'START_DATE', 'END_DATE', 'OBJECTID', 'PSA_ID', 'DistrictID',\n",
       "       'SHIFT_Code', 'OFFENSE_Code', 'METHOD_Code', 'CRIME_TYPE', 'AGE',\n",
       "       'TIME_TO_REPORT', 'DATE', 'Year', 'Month', 'Day', 'hour', 'dayofyear',\n",
       "       'week', 'weekofyear', 'dayofweek', 'weekday', 'quarter'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc3.head().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dc3' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-cc4e12d8714f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Dropping unnecessary columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdc3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Unnamed: 0'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Unnamed: 0.1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Unnamed: 0.1.1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Unnamed: 0.1.1.1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'dc3' is not defined"
     ]
    }
   ],
   "source": [
    "# Dropping unnecessary columns\n",
    "\n",
    "dc3.drop(columns=['Unnamed: 0', 'Unnamed: 0.1', 'Unnamed: 0.1.1', 'Unnamed: 0.1.1.1'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'ANC', 'Housing_Prices', 'Unemployment'], dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ward.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(231502, 41)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 4)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ward.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>ANC</th>\n",
       "      <th>Housing_Prices</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997</td>\n",
       "      <td>1</td>\n",
       "      <td>323000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>8</td>\n",
       "      <td>289000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>583700</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>634800</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>834500</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  ANC  Housing_Prices  Unemployment\n",
       "0  1997    1          323000           3.6\n",
       "1  2006    8          289000          17.0\n",
       "2  2008    1          583700           5.6\n",
       "3  2008    2          634800           3.1\n",
       "4  2008    3          834500           1.6"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ward.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>X</th>\n",
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       "      <th>CCN</th>\n",
       "      <th>REPORT_DAT</th>\n",
       "      <th>SHIFT</th>\n",
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       "      <th>XBLOCK</th>\n",
       "      <th>YBLOCK</th>\n",
       "      <th>WARD</th>\n",
       "      <th>...</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
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       "      <th>dayofyear</th>\n",
       "      <th>week</th>\n",
       "      <th>weekofyear</th>\n",
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       "      <td>-76.999518</td>\n",
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       "      <td>2012-04-25 00:00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>400042.0</td>\n",
       "      <td>137118.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-76.995541</td>\n",
       "      <td>38.905032</td>\n",
       "      <td>10123633</td>\n",
       "      <td>2012-02-29 00:00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>SEX ABUSE</td>\n",
       "      <td>400387.0</td>\n",
       "      <td>137463.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-76.948897</td>\n",
       "      <td>38.885680</td>\n",
       "      <td>11102619</td>\n",
       "      <td>2012-05-14 00:00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>GUN</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>404434.0</td>\n",
       "      <td>135316.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-76.967571</td>\n",
       "      <td>38.855724</td>\n",
       "      <td>11141272</td>\n",
       "      <td>2012-06-25 00:00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>HOMICIDE</td>\n",
       "      <td>402815.0</td>\n",
       "      <td>131990.0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-76.939620</td>\n",
       "      <td>38.910718</td>\n",
       "      <td>11158196</td>\n",
       "      <td>2012-01-05 00:00:00</td>\n",
       "      <td>MIDNIGHT</td>\n",
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       "      <td>405237.0</td>\n",
       "      <td>138096.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           X          Y       CCN           REPORT_DAT     SHIFT  METHOD  \\\n",
       "0 -76.999518  38.901924   9074624  2012-04-25 00:00:00  MIDNIGHT  OTHERS   \n",
       "1 -76.995541  38.905032  10123633  2012-02-29 00:00:00  MIDNIGHT  OTHERS   \n",
       "2 -76.948897  38.885680  11102619  2012-05-14 00:00:00  MIDNIGHT     GUN   \n",
       "3 -76.967571  38.855724  11141272  2012-06-25 00:00:00  MIDNIGHT  OTHERS   \n",
       "4 -76.939620  38.910718  11158196  2012-01-05 00:00:00  MIDNIGHT  OTHERS   \n",
       "\n",
       "     OFFENSE    XBLOCK    YBLOCK  WARD   ...      Year  Month   Day  hour  \\\n",
       "0  SEX ABUSE  400042.0  137118.0     6   ...    2009.0    6.0   1.0   6.0   \n",
       "1  SEX ABUSE  400387.0  137463.0     6   ...    2010.0    8.0  27.0   7.0   \n",
       "2   HOMICIDE  404434.0  135316.0     7   ...    2011.0    7.0  17.0   2.0   \n",
       "3   HOMICIDE  402815.0  131990.0     8   ...    2011.0    9.0  26.0   6.0   \n",
       "4   HOMICIDE  405237.0  138096.0     7   ...    2011.0   10.0  27.0   0.0   \n",
       "\n",
       "   dayofyear  week  weekofyear  dayofweek weekday quarter  \n",
       "0      152.0  23.0        23.0        0.0     0.0     2.0  \n",
       "1      239.0  34.0        34.0        4.0     4.0     3.0  \n",
       "2      198.0  28.0        28.0        6.0     6.0     3.0  \n",
       "3      269.0  39.0        39.0        0.0     0.0     3.0  \n",
       "4      300.0  43.0        43.0        3.0     3.0     4.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using get_dummies on the SHIFT column so it can be used as a feature for modeling\n",
    "\n",
    "dc3 = pd.get_dummies(dc3, columns=['SHIFT'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y', 'CCN', 'REPORT_DAT', 'METHOD', 'OFFENSE', 'XBLOCK', 'YBLOCK',\n",
       "       'WARD', 'ANC', 'DISTRICT', 'PSA', 'NEIGHBORHOOD_CLUSTER',\n",
       "       'CENSUS_TRACT', 'VOTING_PRECINCT', 'LATITUDE', 'LONGITUDE', 'BID',\n",
       "       'START_DATE', 'END_DATE', 'OBJECTID', 'PSA_ID', 'DistrictID',\n",
       "       'SHIFT_Code', 'OFFENSE_Code', 'METHOD_Code', 'CRIME_TYPE', 'AGE',\n",
       "       'TIME_TO_REPORT', 'DATE', 'Year', 'Month', 'Day', 'hour', 'dayofyear',\n",
       "       'week', 'weekofyear', 'dayofweek', 'weekday', 'quarter',\n",
       "       'SHIFT_EVENING', 'SHIFT_MIDNIGHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'ANC', 'Housing_Prices', 'Unemployment'], dtype='object')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ward.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining features and modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Defined features for modeling as X variable\n",
    "\n",
    "features = ['LONGITUDE','LATITUDE','PSA','AGE','SHIFT_EVENING', 'SHIFT_MIDNIGHT','METHOD_Code','Year','Month','Day','hour','dayofyear','WARD','week','quarter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['X', 'Y', 'CCN', 'REPORT_DAT', 'METHOD', 'OFFENSE', 'XBLOCK', 'YBLOCK',\n",
       "       'WARD', 'ANC', 'DISTRICT', 'PSA', 'NEIGHBORHOOD_CLUSTER',\n",
       "       'CENSUS_TRACT', 'VOTING_PRECINCT', 'LATITUDE', 'LONGITUDE', 'BID',\n",
       "       'START_DATE', 'END_DATE', 'OBJECTID', 'PSA_ID', 'DistrictID',\n",
       "       'SHIFT_Code', 'OFFENSE_Code', 'METHOD_Code', 'CRIME_TYPE', 'AGE',\n",
       "       'TIME_TO_REPORT', 'DATE', 'Year', 'Month', 'Day', 'hour', 'dayofyear',\n",
       "       'week', 'weekofyear', 'dayofweek', 'weekday', 'quarter',\n",
       "       'SHIFT_EVENING', 'SHIFT_MIDNIGHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using fillna to get rid of null values in the dataframe\n",
    "\n",
    "dc3.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Defining X and y variables\n",
    "\n",
    "X = dc3[features]\n",
    "y = dc3['OFFENSE_Code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train, test split with a random state of 42\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# Dropping null values\n",
    "\n",
    "X_train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# Dropping null values\n",
    "\n",
    "X_test.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying no null values in data\n",
    "\n",
    "X_train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying no null values in data\n",
    "\n",
    "X_test.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Scaling data in preparation for modeling\n",
    "\n",
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173626, 15)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying shapes are the same \n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173626,)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying shapes are the same \n",
    "\n",
    "\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57876, 15)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying shapes are the same \n",
    "\n",
    "\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57876,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying shapes are the same \n",
    "\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TPOT First Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_optimizer = TPOTClassifier(generations=2, population_size=20,cv=5, verbosity=2,\n",
    "                      config_dict='TPOT light')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.5910404395573121\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                               \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.5910404395573121\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                            \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best pipeline: DecisionTreeClassifier(input_matrix, criterion=gini, max_depth=9, min_samples_leaf=13, min_samples_split=12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TPOTClassifier(config_dict={'sklearn.naive_bayes.GaussianNB': {}, 'sklearn.naive_bayes.BernoulliNB': {'alpha': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0], 'fit_prior': [True, False]}, 'sklearn.naive_bayes.MultinomialNB': {'alpha': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0], 'fit_prior': [True, False]}, 'sklearn.tree.DecisionT...e_selection.VarianceThreshold': {'threshold': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}},\n",
       "        crossover_rate=0.1, cv=5, disable_update_check=False,\n",
       "        early_stop=None, generations=2, max_eval_time_mins=5,\n",
       "        max_time_mins=None, memory=None, mutation_rate=0.9, n_jobs=1,\n",
       "        offspring_size=20, periodic_checkpoint_folder=None,\n",
       "        population_size=20, random_state=None, scoring=None, subsample=1.0,\n",
       "        verbosity=2, warm_start=False)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_optimizer.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5934065934065934\n"
     ]
    }
   ],
   "source": [
    "print(pipeline_optimizer.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(criterion='gini', max_depth=9, min_samples_leaf=13, min_samples_split=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=9,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=13, min_samples_split=12,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5468132652943684"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5402239270163799"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4823424295181299"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(dt, X, y,scoring='accuracy',cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12721787, 0.12640386, 0.06321824, 0.14629497, 0.01068265,\n",
       "       0.00917052, 0.06820908, 0.05335156, 0.04251621, 0.08744546,\n",
       "       0.08154455, 0.09403749, 0.0192574 , 0.07065013])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "important_features = pd.Series(data=dt.feature_importances_,index=X.columns)\n",
    "important_features.sort_values(ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "METHOD_Code       0.395665\n",
       "AGE               0.243054\n",
       "PSA               0.120549\n",
       "hour              0.082480\n",
       "LONGITUDE         0.045136\n",
       "LATITUDE          0.044909\n",
       "WARD              0.020519\n",
       "Year              0.017460\n",
       "SHIFT_MIDNIGHT    0.013668\n",
       "SHIFT_EVENING     0.010839\n",
       "dayofyear         0.003062\n",
       "Day               0.002004\n",
       "week              0.000645\n",
       "Month             0.000010\n",
       "quarter           0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest - Best Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Even though TPOT stated DecisionTreeClassifier was the best model, we manually tested both models and found the RF model to have a better cross_val_score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(criterion='gini', max_depth=9, min_samples_leaf=13, min_samples_split=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=9, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=13, min_samples_split=12,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.55037264004239"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5429884580827977"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5264876245847796"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(rf, X, y,scoring='accuracy',cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "important_features = pd.Series(data=rf.feature_importances_,index=X.columns)\n",
    "important_features.sort_values(ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "METHOD_Code       0.313403\n",
       "AGE               0.245524\n",
       "PSA               0.124617\n",
       "hour              0.075233\n",
       "LATITUDE          0.057188\n",
       "LONGITUDE         0.050762\n",
       "WARD              0.046789\n",
       "SHIFT_MIDNIGHT    0.031808\n",
       "Year              0.018312\n",
       "SHIFT_EVENING     0.016622\n",
       "dayofyear         0.007197\n",
       "week              0.005195\n",
       "Day               0.003418\n",
       "Month             0.002727\n",
       "quarter           0.001206\n",
       "dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20fbbdfeb00>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\matplotlib\\figure.py:2267: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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K5pkoKEv6OoCdgO2B6zPz/4ZxzJIkSRJgiNIIi4gJlAMkZgH/r7r8bFWu1tL2Q8AW1cclWrq6DngE+Cwu5ZMkSdII8mAJDZddImJs9XsHsCKwPuVAiNnA3pn5YFU/DdgbuDQifgQ8DWxECVB/o5zM95rmzjPzxYg4n7J3ahZw6bB+G0mSJKniTJSGy87AcdXPscAhlCB0BvD2zLyq0bD6fW/gT5Sjyg8ElgY+DmxbNdu+zTMuqMpLqhf4SpIkScPOmSgNqcycBEwawH3Tgek9VLfuh2oYV5Xn1n1eb7onrjuU3UmSJGkh40yUFkgRsSIwkTJ7deMoD0eSJEmLEGeitECJiC2AycCbKPusPpSZ3aM7KkmSJC1KnInSguavwOso74U6NjM9lU+SJEkjypkoLVAy8z5g9dEehyRJkhZdzkRJkiRJUg2GKEmSJEmqwRAlSZIkSTUYoiRJkiSpBkOUJEmSJNVgiJIkSZKkGgxRkiRJklSDIUqSJEmSajBESZIkSVINhihJkiRJqsEQJUmSJEk1GKIkSZIkqYbFR3sA0vymY/LdfbbpnrjuCIxEkiRJ8yNnoiRJkiSpBkOUFigRMTMinhztcUiSJGnRZYiSJEmSpBoMUZIkSZJUgyFKkiRJkmrwdD71KCJuA94GrJSZzzZd7wLWB7bMzBubrk8GPgGslZkPRMQE4HPARpS/a78DvpWZF7V51vrAscBmwLJAAmcC38vM7j7GeSDwX1X/EzLz8QF/aUmSJKkPzkSpN1cDSwHvaVyIiDHAuOrjFi3ttwXuqQLUwcD1wDuA6cD3gFWBCyPi8803RcR2wC+ACcBPgNMpfze/W93Xo4jYBTgbuBvY2gAlSZKk4WaIUm+ursotm66Np/y9mQVs3rgYEW8EArgqIl4PnAHcC6ybmYdm5icps1q/AL4SEW+r7lsWmAo8DbwjMw/MzM9QgtpFwCERsX27wVUzXT8G/ghslZmPDcm3liRJknphiFJvfg38nVeGqAnAP4BLgY0jYsnq+rZVeRWwP2UG69jM/EfjxsycDRxH+Xt3QHV5J2AV4MTMnNnUdi5lKSDAh1sHFhEbAZcDD1GWFT4y4G8pSZIk1eCeKPUoM+dGxLXA3hGxYmY+RQlRPwP+B/ggsCHwc0qIegq4Ffh41cWWjRmnJstVZWNJYGejjIhJbYbxYlPbhmUos2TLAddm5sMD+HqSJEnSgBii1Jergf2A8RHxS2Bdyj6lm6v6zSPiV5RwdU1mzomIlaq6j/XS78pV2Wi7dz/aNiwJPA/8Btg9InbIzCv79W0kSZKkQTJEqS/XAnMpS/qWrq7NyMy7I+JvlH1RPwdWoCzlg7JfCuDNmXl/H/032r7ipL8+PF+NpwO4E/h2RMzIzFm93yZJkiQNnnui1KtqT9OvKTNNmwKPA3dV1TMoJ/e9nxK0/ru6/ruq3KC1v4j494g4KSJ27EfblSNickTs31I1OzPvycy7gVOANwBfGcDXkyRJkmozRKk/rgb+g3IIxM+a3ts0A1ge+Cjw66bT8aZR9jKdEBGva3QSEYtTji8/GnhNdflSysl8n42IdVqeeyLlvVNr9zK2LwN/AY6MiM5e2kmSJElDwhCl/mgcdf4GSnBqaPy+Ii8v5SMz7wM+Qwk/f4iI/4qIb1FmnbYBrqQELTLzSeBgynLAOyJiWkScGBG/Bg6i7Hs6qaeBZea/gInAYsBZEbHYoL6pJEmS1AdDlPrjdqBxhPiMxsXMvKfp+lXNN2TmyZRlfncCu1Nmq16gzELtkZlzmtpeSNlbdQOwHXAkZYbrK5T3P/W61ykzL6EsJXwnJVBJkiRJw6aju7u771bSIqCrq6sboLPTVYGSJEkLu66uLgA6Ozs76t7rTJQkSZIk1WCIkiRJkqQaDFGSJEmSVIMhSpIkSZJqMERJkiRJUg2GKEmSJEmqwRAlSZIkSTUYoiRJkiSpBkOUJEmSJNVgiJIkSZKkGgxRkiRJklSDIUqSJEmSajBESZIkSVINhihJkiRJqsEQJUmSJEk1LD7aA5DmNx2T7659T/fEdYdhJJIkSZofORMlSZIkSTU4EzWfiohxwB3A1Mw8cJSHA0BE/DtwJrAxMBf4emZ+bXRHJUmSJI0sQ5TqOBfYBLgUuBe4ZXSHI0mSJI08Q5TqWB94KDN3G+2BSJIkSaPFPVGqY0ngH6M9CEmSJGk0ORM1H4iIdwBfATYDOijL5aa1afdG4BjgfcCawBwggbMy88yqzWbAz4DzM3P/Nn38CVgCGJuZcyNiKeBoYH/gzcAs4FbgK5l5W3XPJOC4qov1IqIbeBA4B/gycEhmnt1mrA8AP2yMIyJWAD4P7Am8Hvg7cAVwXGb+re53bRnbVsDXgHHATKAzM2e1fn9JkiRpsJyJGmXVARK3AtsB1wA/pASH81vajQVuAw4A/gc4BbgEeCvw3Yg4omp6K3A/sHNELNvSx7uBtSgBa25ELA1cD5xACSnfBX4KbAP8IiJ2rm6dARxf/f5o9ftk4DygG9i3zVfbjxIIz6uevSLwc+CzlHB1avU9DgV+HRGrD+C7NjsfmA2cDswwQEmSJGm4OBM1+k4FlgXel5k3wkuzKzOA1zW1OwZ4LbB1Zl7fuBgRZwC/ogSZMzKzOyKmAccCOwLTm/rYryrPq8pPA5sCUyizSXOqPjsph0ZMiYg3ZuYMYEZEHAc8kpmTmp5/C7BFRKyRmX9tedYjlJAGZZbobcDHM/M7TffvBFxe/Xf4QJ3v2vLf8c/AhMyciyRJkjSMnIkaRRGxBrA5cE0jQAFk5mOUZXLNpgEHNYeKqu2vKTMwqzZdPrcqX5ohioglKCHl9sxsvE32QOAZ4KhGgKr67AK+DawE9HWIxFTK36O9mp71TmBdylK+FyNiceBDwB+aA1T1rCsoM1S7Vcv96n7XhksNUJIkSRoJzkSNrvWq8rY2db9o/pCZtwK3RsTKlH0/awNBOXJ8aWCxprZ/ioifA9tGxJjMfIKyRO+1lKV7RMTylKV9P8/Mf7Z5/q3Ap5rG2JMLKbNC+1KW3cG8M14BLAcsVs2ytWqM/+3VePr9XZvM7GOckiRJ0pAwRI2uMVXZLsQ83vwhIsZQQsq+lIMhuinB4UbK0eMdLfefC7wH2B04m3JwxBzgR1V9Y9bnqR7G1liat2wP9QBk5j8j4jJgn+plvH8C9gF+n5l3Vs1Wqsq38PIBFe2sDAP6rlBmqCRJkqRhZ4gaXU9U5Ypt6pZr+TwN2B44kzLDc1djBiki9mNe06n2GVV7pHYErsvMR6v6RnBbo4exNQJef440n0oJTntSZrDWoBw80dA45OG8zPxQP/qr+10lSZKkEWOIGl13UGZZ3tOmboPGLxGxEiVU3JaZhzU3qk6yW5qW2ZnMfCoirgB2qX6W5eXldWTm0xHxQOkiVqn2YTXbvCr/0I/vcT1l5mpHyqzTXMopgy89DngO6IyIjszsbvkOEymh8bvAi3W/qyRJkjSSPFhiFGXmI5RjzSdExO6N69UBC83L3p6nBJMxEbFkU7tlePmUuiXaPOJcygtyv06Zebq8pX4KsAxwSnX4Q6Pf9YEjgSeBn/Tje7xICU0bAXsDN2bmw031z1JmxtYFPtl8b0SMB04CPkKZmRvod5UkSZJGhDNRo+8IyiESF1R7ix6izOi8dNJcZj4TEZcAe1DeqXQdZeZmR8ox6E8AK0XEq1pOqLuW8l6nNwI/yMzWfUMnUg6c2A94R0TcCKxGmbnqAPbKzKf7+T2mUg6i+Dfgi23qPwW8Gzipev/Urygv3N0NeAH4SDX2gX5XSZIkaUQ4EzXKMvN+yqlzP6YsofsIZZnfTi1ND6LsM1qJMku0LfAbSjCZSplRem9L33MoL6mFpqV8TfXPAltR3im1JHAYMIEy+/SuzGyduerte/weuIdyZPolbeofAzYGvgWsCRwFbFY9a5PqXVQD/q6SJEnSSOno7u7uu5UWWBHxC0poGdu6F2mIn7Mi5eW6F2XmB4frOcOpq6urG6Czs3O0hyJJkqRh1tXVBUBnZ2ft/fbORC3EIuJ9wLuAc4YzQFU+Szn04axhfo4kSZI0qtwTtRCKiMmUZXnrAn/n5QMZhuNZP6PMdK1FOVDiZ8P1LEmSJGl+4EzUwumvlFBzL7BTZj7eR/vBeBxYHfgp5eW4kiRJ0kLNmaiFUGaeSDl5bySetctIPEeSJEmaXzgTJUmSJEk1GKIkSZIkqQZDlCRJkiTVYIiSJEmSpBoMUZIkSZJUgyFKkiRJkmowREmSJElSDYYoSZIkSarBECVJkiRJNRiiJEmSJKkGQ5QkSZIk1WCIkiRJkqQaFh/tAUjzm47Jd4/as7snrjtqz5YkSVL/jGiIioidgEOBjYAVgceBXwPnZOYVTe3GAg8AN2fm+F76mwFsAbwpM2dW1w4EfgAcn5mTWvrry6mZOTEiumt8rQ9n5pQa7YmIScBx/Wj6YGaOjYjPAV8DJmfmf/bR9yXArsD4zLy56b9RX6Zm5oEt45sBTMjMef57RMRKwBO0/Bn18GcyBTig+Rlt+hsH3NFTm4h4NbAXsC+wDrAa8BTwW2A6MCUz5/Tje0qSJEmDMmIhKiJOB44AZgKXA38H1gTeD+wUEWdl5qHDPIwHgSm91P+yKo9vuT6WEgJ+C1zWUnfnIMZzeR/3P1mV5wFfBfaMiE+2CzUAEbEisD0lMP6spfrUpv7aaTeO8cBHgHN6ua+OAyJiWmZeX+emiHgbcDElPP0F+CnwKPBaYFvgLOBjEbFtZv59iMYqSZIktTUiISoixlMC1MXA3s0zBtU//G8CDomIqzLz8mEcyszG7FRvWttU4z8AuLM/99dwWX9msTLzoYi4CdgS2Ix5A1LDHsBSwLltgtbkxsxQTd+MiCsz89EB3NvO9yLibZk5uz+NI2JVyozYSsB/Aqdn5otN9UsAJwCfBr4H7D5E45QkSZLaGqmDJXaoyjNal1xl5lPAMdXH3UZoPAuiqVW5Vy9t9gO6m9oO1h3AGOC0IexvLWBSjXtOBV4DfCMzJzcHKIDMfCEzPwP8HNgtIv59iMYqSZIktTVSIWqJqnx7D/W3AB8AThmZ4SyQLgFmAXtExGKtlRGxBmUv0i2Z2Z/9X/3xDSCBD0TEDn017ofPUJZxfrLaA9WriHgNZXbtMcpyxt58AzgDmDvYQUqSJEm9GakQ9dOqPCkiTo+IdzUHgcycnZkXZuZg9hct1DLzX5TlkKsC723TZB/Kn+eUIXzsc8AhlNmt70TE8oPs7++UJXmLA2e3C4MtdqjaXtPX8r/M/ElmHpmZfxrkGCVJkqRejcieqMy8MiK+CxxG2Rt1BPB0RNxKCVgXZeZDbW4dW50U15OxNYfSW38zMnNGzf4Ga5fq5MCe/Dgz7236PJWyN2svoPVwhv2AfwEX9tDXxIjo7WCJr2fms60XM/OWiDiLcqriCcBRvfTRp8ycFhEfBN4HfAI4uZfma1fl7wfzTEmSJGkojdjpfJl5eERcRQlQWwIrUE6S2x44MSK+BXwhM5uXY72R/h0F3l999TdjCJ/VHztXPz25E2gOUTMoJwzuFhGHZ+YLABHxFuCdlAMlZvXQ1yf6GMtkYJ4QVfkMsCPw8Yj4YWb+sod2/fUxSjD6ckRc0suBF6tW5ROtFRGxPrBTm3vuzMzWExQlSZKkITOi74nKzKuAqyJiOWBzSpjaiTLjcAxlOdpnm27p73ui+qvX/kZBrXdMZWZ3REwDvgBsDVxdVe1Xlb0dKPGmXsJKX899KiKOBC4CzqoCzIBl5gMRcSxwEnAm5Zjydh6vyjFt6tanfSCeyrzH0EuSJElDZqT2RL1CZs7KzKsz82jKu38a+26OjIhlR2NMC5BGUNq76dq+lBmqm4broZl5MeW9Vm+jzEwN1mTgdmCbiNivhzaNAzLWbq3IzLMzs6PxQ5mJkyRJkobdsIeoiFghIu6LiCvb1Wdmd2aeTdkbtQzw+uEe04IsM+8D/gfYOSKWiohNKMeGt3s31FD7OPA08CVK+B2w6qjyg4EXKacyvqZNsyspp+3t1I9DKCRJkqQRMewhKjOfBlYEtoqI1fpoPhd4ZLjHtBA4l7KnbCtgz+raUL0bqkeZ+TDwOcoLfc8cgv7uoBy18jBiAAAgAElEQVQssQrwzTb1f6UszVutem5vRmVWVZIkSYuekfqH5xmUf3hfFBGrt1ZGxE6UQHBpFbrUux9Tjh/fCdgVuHUEj/b+LvALhm753CTg/l76Owx4FDg+Io6NiCVbG0TEeEqwBN8TJUmSpGE2UgdLnEB50e4ewB8j4lrgfykv4d0YeA/lFLrDRmg884u+jjgHODMzXzE7l5lPRsQVwP7AspT/vn3p64jzZzPz6311Uh1ucQhwBzBPoKkrM5+JiI8B1/VQ/7eI2BSYDhwPHBUR1wB/AVYCxgNvoeypm8bQ7NeSJEmSejRS74l6EdgzInal/MN/I2A74HngPspSrVP7eqHqQqivI86hLGdrt8RxKmUp3zPABf14Vl9HnD8F9BmiADLz7oj4OnBsf9r3o7+fRsS5wId6qP9jtfdrV8pJhO8G1gBmU2axTgLOzswcivFIkiRJveno7h7uswikBUNXV1c3QGdn52gPRZIkScOsq6sLgM7Ozo6697oZX5IkSZJqGNGX7S6MqkMNxte4ZXJm9rY3SZIkSdJ8zBA1eOOB42q0nwIYoiRJkqQFlCFqkDJzEuWYbkmSJEmLAPdESZIkSVINhihJkiRJqsEQJUmSJEk1GKIkSZIkqQZDlCRJkiTVYIiSJEmSpBoMUZIkSZJUgyFKkiRJkmowREmSJElSDYYoSZIkSarBECVJkiRJNRiiJEmSJKmGxUd7ANL8pmPy3aM9hJd0T1x3tIcgSZKkFoYo1RIRpwNHANdk5na9tNsFuBT4X2BcZs4eoSFKkiRJw8rlfKrrc8BfgG0jYu92DSJieeB0YC7wEQOUJEmSFiaGKNWSmbOAw6uPp0TESm2anQC8Hjg9M38+YoOTJEmSRoAhSrVl5pXAdOB1wNeb6yJiQ+DjwJ+Az4/86CRJkqTh5Z4oDdQngK2BQyNiSmb+MiIWA74HdAAHZeYzzTdExF7AROAdwIvAr4CvZubNLe2WoASxfYC3AEsDjwBXAcdm5t+rdmsD9wGTgFWBA4FngI9m5iXD8J0lSZIkZ6I0MJn5KPApSmCaHBEdwGHAO4FvtwlGXwN+DKwG/BdwLiVM3RAR+7R0fyFwCvAs8H3gLOC5qv8r2wznMGBX4LuUYPbLIfiKkiRJUlvORGnAMvMHEbE/MIFyYt/xwAPAMc3tIuJdlAMpbgB2bBw0ERGTKKHn+xFxbWY+HhGbAjsDUzPzwKY+lgDuBDaOiLUy8/6mR6wKvD0z/zA831SSJEl6mTNRGqyPArOB04CVgIMz818tbQ6qyk81n9RXLcv7JrAcsGd1+c+UZXmTmjvIzBeAxiEVq7b0f48BSpIkSSPFmSgNSmb+MSK+CRwLTMvMG9s066zKPav3RzV7Q1WOq/r7MzA1IpaIiPWBANau6req2i7W0sfMwX0LSZIkqf8MURoKD1Tl/T3UN45B7+20vpUbv0TEYcCXgNWrS09Q9jklsCFlH1Yz30MlSZKkEWOI0kiYBbwALJOZL/bWsDpk4juU/U8fA+7IzL9UdWdRQpQkSZI0agxRGgm/A94GrAfc3lwREe8BdgSuzMxbgX2rqr0zM1v6eWtVts5ESZIkSSPGgyU0EqZU5eSIWL5xMSJWoBxL/tmmts9W5WrNHUTER4D3VB+XGJ5hSpIkSX1zJkrDLjN/GhHfAQ4H/hARVwHPA7sBrwfOqGahAKYBewBXRMSPgaeBTYDNgL9RTuZ7zQh/BUmSJOklzkRpRGTmxylHlz8MfAg4APhrde2opnaXA/tRDqvYv2q3OOWFuu+vmm0/QsOWJEmS5tHR3d092mOQ5gtdXV3dABvcssxoD+Ul3RPXHe0hSJIkLZS6uroA6OzsrL3f3uV8UguDiyRJknrjcj5JkiRJqsEQJUmSJEk1GKIkSZIkqQZDlCRJkiTVYIiSJEmSpBoMUZIkSZJUgyFKkiRJkmowREmSJElSDYYoSZIkSarBECVJkiRJNRiiJEmSJKkGQ5QkSZIk1WCIkiRJkqQaDFGSJEmSVIMhSpIkSZJqWHy0ByDNbzom3z3aQxhx3RPXHe0hSJIkLTCciZIkSZKkGoZsJioidgIOBTYCVgQeB34NnJOZVzS1Gws8ANycmeN76W8GsAXwpsycWV07EPgBcHxmTmrpry+nZubEiOiu8bU+nJlTarQnIqYAB1QfP5iZ03ppewWwI0BmdrTp472ZOaO6Ngk4rqWLbmA28FfgJuDkzLy3zXMa33lCZt7Uw1gmA59oeeb4qt+pmXlgm3uWAHYFPgj8B7A68AxwD3AJcGZmPtNyz4G0/Bn2MJ5u4MHMHNvyHfqj9p+bJEmS1F9DEqIi4nTgCGAmcDnwd2BN4P3AThFxVmYeOhTP6sWDwJRe6n9Zlce3XB9LCSy/BS5rqbtzkGPaDWgboiJieeB9A+jzcl4e16uAFYD1gEOA/SNiz8y8qod7vx8Rb8/MZwfw3FeIiH8DLgQ2Bv4GXAc8VI1nS+BbwBERsVVm3j/Y5zGyf26SJElSjwYdoqrZiiOAi4G9M3NOU92KlJmMQyLiqsy8fLDP68XM3mY2GlrbVOM/ALizP/fX8AiwTUQs2zobU9kRWAqYBSxXo9/L2s2yRMT2wKXA9IgYl5l/bHPv2sCxwOdrPG8eEbEMcD2wDvANYFJzMIuIDuAo4JRqPBtn5tzBPHME/9wkSZKkXg3FnqgdqvKM5gAFkJlPAcdUH3cbgmctSC4DlgW26aF+D8qytz8NxcMy82rgS8Crq7LVn4F/AJ+OiHcM8nHHUgLUDzPzmNaZrczszsxTgR8CGwATBvk8SZIkab4xFCFqiap8ew/1twAfoMxKLEouA+bSJjxGxKuBbYGLhviZZwDPArtHROss4xPA0ZTZx7MiYkB/9hGxGHAQMKfqrzenAudQwpskSZK0UBiKEPXTqjwpIk6PiHdV/9AGIDNnZ+aFmbmo7VN5FLgV2KE6gKHZ+4FlKHuKhky1bPB2ymzUuDb1UynL8DYCjhzgYzYFVgH+JzMf6WM8v8nMgzPzjgE+S5IkSZrvDHpPVGZeGRHfBQ6j7I06Ang6Im6lBKyLMvOhNreOrU6c68nYmkPprb8ZjRPnRtjFwObAeykHLzTsAWRm3hURQ/3Mh6ty9R7qPwbcBXw1Ii7NzD/X7H/tqvz9QAbXZHwff/6SJEnSfGlITufLzMMj4ipKgNqSckLb9tXPiRHxLeALLYcLvJF5j+wejL76mzGEz+qvS4DJlCV918FLhzJsz/Atb3yuKldoV5mZf6rCyzeA71JmxepYtSqfaK2IiLWAD7W5Z2abwzC2qH4kSZKkBcqQvSeqOlb7qohYjjL7siWwE2Xm4hjK0sHPNt3S3/dE9Vev/Y2GzHwoIn4N7BwRh1chcjvKcrshXcrXZPmqnNVLm5OBfYDtI2LvzPxxjf4fr8oxberWon2QvZl5j5/vz3uiJEmSpPnOUOyJeoXMnJWZV2fm0ZQT3A6hvBT2yIhYdqiftwC4GHgd8K7q8x7AfZn5u2F63tiq7PHdTNUpigcDLwKnRsTKNfpvvNh47daKzLw+MzsaP7QPWpIkSdICbVAhKiJWiIj7IuLKdvXVUddnU/ZGLQO8fjDPW0BdXJW7RcRSlOVzwzILFRFjgP8AngTu7q1tZnZRTs9bFTipxmNurvofXz1PkiRJWqQMKkRl5tPAisBWEbFaH83nUl5Au0jJzPuBO4FdgK0pe5WGaynfRylLNKdn5ov9aH8sMBP4MGUJZp8y8zngvyhH23+zj+ZDPtMpSZIkjbah+EfuGcBSwEURMc+JcBGxE7AVcGkVuhZFF1P2C30B+ONwHPceERMooWgW8P/6c09m/otyqiLAO2s87ouUma6DIuI71XuvWsfzTuCK6uPc1npJkiRpQTUUB0ucQHnR7h7AHyPiWuB/KTMVGwPvAe7l5X+sL4ouBr4CbEI/A04vdomIsdXvHZSZwPWBzYDZwN6Z+WB/O8vMayLifGC/GvfMrkLbeZQ/1w9GxDWUfVjLUvZ/dVbN/xs4vL99S5IkSfO7oXhP1IvAnhGxK7A/5UWu2wHPA/cBnwNOzczZg33Wgioz74mIe4C3AhcNsrudq5+GZyhL8s4AJmfmnwbQ538C2wKv6e8NmfloRGxD+bP+ECXIvR+YAzwIfBuYkpm3DWA8kiRJ0nyro7vbk6QlgK6urm6Azs7OvppKkiRpAdfV1QVAZ2dnR9173fgvSZIkSTUM2ct2F0YRMR4YX+OWyZn55PCMRpIkSdL8wBDVu/HAcTXaT6G8Q0mSJEnSQsoQ1YvMnARMGuVhSJIkSZqPuCdKkiRJkmowREmSJElSDYYoSZIkSarBECVJkiRJNRiiJEmSJKkGQ5QkSZIk1WCIkiRJkqQaDFGSJEmSVIMhSpIkSZJqMERJkiRJUg2GKEmSJEmqwRAlSZIkSTUsPtoDkOY3HZPvHu0hLBK6J6472kOQJEkaEEPUIiAipgAHADtk5lVt6n8DbAD8LDO3aFP/IWAq8P8y8/MtdfcAbwG+k5kf7+H5Y4EHehjeHOBJ4A/AOZl5Xsu9M4DWMb0I/Kvq8wrg5Mx8sof+JUmSpCFliFo03EQJUe8CXhGiImJlYH1gLvCuiFguM2e13L9pVd7Qcu+GlAD1DLBvRHwqM2f3Mo4HgSkt15YG3grsAGwREWtk5jfa3HsqJWwBLAGsDGwOfAk4MCI2z8yZvTxbkiRJGhKGqEXDTVW5SZu6LSl74y4E9gTGA1e2tNkUeBb4ecv1DwHdwDeB44A9gPPo2czMnNSuIiK2Aq4Djo2I77WZWZrcGpIi4lXAJEqQujIixmXmnF6eL0mSJA2aB0ssAjLzz8D9wEZV8Gi2NWVJ3VeaPr+kmql6C/CLzHy26foSwN7AXcD3KDNZBw9ijNcDtwDLUmbM+nPP3Mw8Fvhv4D+ADw70+ZIkSVJ/GaIWHTOA5Slho9nWwK8y8y5K0Nq6pf49QActS/mA7YHXAtdm5v9RAtDmEbHOIMb4t6pcquZ9J1Xl3oN4tiRJktQvhqhFR2NJ30uzPBGxNjAWuL66dD3w1ohYs+m+tvuhKEv5AKZX5Y+r8qCBDC4ilqXscQL4Xc3bf0GZCXvPQJ4tSZIk1WGIWnS02xfVmHVqhKgbWq5DCVFPA7c1LkTEGOD9wH2Z2VVdvhB4ATggIvq91y4ilo2IDYBLgVWB8zLz/v7eD1AtM/wH8OqIWL7OvZIkSVJdhqhFRGY+DNzHK/cbbQ3MAn5Vfb6RclDEVgARsRTQCczIzBeb7tuLsuTuR039/4NyMMRqwI49DGOLiOhu/qEcVf4bYAJwDvCxAX7F56pyhQHeL0mSJPWLp/MtWm4CDqlmkp4G3gvcnJkvAGTm3yPit5QT+gA2pISlnpby/ajl+vmUGaqDKTNLrZqPOF+SEuI2AO4AdqkOwBioxgxU6/HskiRJ0pAyRC1aZgCHAhtT3rm0EvMGpOuBT0XEm3l5j9FLbap9VI3ZrHsiot1ztomI12fmQy3XW484/3xEfBP4FHBBRGzV5h1VfapC4YrA45n5VN37JUmSpDoMUYuWxr6oDSgHMcDL+6EabqCEmndTQtSjmfmHpvrGLNSNlOWBrTagLAH8MC8fm96bz1Je9jsBOAvYpx/3tGocfvGLAdwrSZIk1WKIWoRk5iMRcS8l5CxFCUh3tTS7BXgeWI9yCMVPGxUR0QHsT9k39eF2y+8iYtOqj49ExFczs7uPMc2NiA8Dvwf2johLM/OCml/t41V5fs37JEmSpNo8WGLRcxNlr9O7mHcpH5n5L+CXwA7AKi1tNgPeBNzS0/6lzLyVMkM1luqAir5UfX2h+nhqRKzUn/sioiMijgG2AX5LOSFQkiRJGlbORC16bgIOq35vXcrXcANwfNPvDY2lfNP6eMYU4ATKARM/7b3pS74NfJAS8L4BfLSlfmJEPFn9vjjlRb9bAG8BZgK7tZwgKEmSJA0LZ6IWPTMoy/Gg5xDVuH5/Zj4IEBFLA3tQjhLva8ZnKmXP1S4R8dr+DCoz51IOvZhDOUFw05YmnwCOq34+D+xNOWHwi8B6dd8tJUmSJA1UR3d3r1tWpEVGV1dXN0BnZ+doD0WSJEnDrKurC4DOzs6Ouvc6EyVJkiRJNRiiJEmSJKkGQ5QkSZIk1WCIkiRJkqQaDFGSJEmSVIMhSpIkSZJqMERJkiRJUg2GKEmSJEmqwRAlSZIkSTUYoiRJkiSpBkOUJEmSJNVgiJIkSZKkGgxRkiRJklSDIUqSJEmSajBESZIkSVINhihJkiRJqmHx0R6ANL/pmHz3aA9BQPfEdUd7CJIkSW0ZoppExCTgOODDmTmln/f8GzAR2AZ4IzAHeAC4AjgzMx9pc88MYAvg+Myc1EO/uwCX9tQmIlYG9gf2BNYCXgs8DvwGOA+4KDO7W+45EPhBo8+IGA/c1J/vWXlTZs6MiJnAGzOzo6eG7f5bNl1r1g3MBv5ajeXkzLy3TX8zKf99+9Ljf1NJkiRpKBiiBiEi9gHOAZYBbgGuAZYANqSEhf+MiP0z8yc9dPG5iLggM2tNfUTE5sB04HVAAj+hBKg1gO2BHYFrImK3zJzdS1czgeNbro2nBLzLgTtb6p6sM85eNPf9KmAFYD3gEGD/iNgzM6/q4d7W8baaMSQjlCRJknpgiBqgiNgBOJ8yg/KBzPxFS/2WwIXA5RGxTWb+tE03SwJnRcSmrbNGvTx3XeA64EVgv8z8YUv9q4HvAfsBXwWO7qmvzJwJTGq5fxIlRF3W39m4AWjbd0RsT5l9mx4R4zLzj61tnGWSJEnSaPNgiQGIiKWBs4Hnge1aAxRAZt4A7Ax0AGdX97S6A3g38LEajz8HWAqY2Bqgquf+C/gIZUnh4RGxfI2+R1VmXg18CXh1VUqSJEnzHUPUwOwNrAZckJl39dQoM2+hLLV7A/D+Nk0OA54Dvh4Ra/b10IhYD9gEuIsS4np67vPACcBpQLvwNj87A3gW2D0inCmVJEnSfMcQNTBbVeV/96PtJVW5S5u6pCy5W4ESHvrS6OOyvpb/ZeY5mfnZzHysH/3ONzLzGeB2ymzUuFEejiRJkjQP/0//wERVZj/a3lOVa/VQ/w1gL2CX6iCIS3poB7B2Vf6+H89dkD1clau3VlR7tnrybGZ+fVhGJEmSJFUMUQOzYlU+3Y+2j1fla9tVZuYLEXEI8HPgjIi4ITOf6qGvVavyidaKiJgAbN7mnhmZOaMf45yfPFeVK7Spaz0ivdlTgCFKkiRJw8oQNTCNELNMP9q+uip7XFaXmb+MiO8AR1Bmpno6aKIRyMa0qZsAfKGH+2b0Pcxa5g5T24bGYRizWit6ezeVJEmSNBLcEzUwf6rKdfrRdt2qfLCPdp8HHgIOjYjNemjzQFWu3VqRmV/MzI7GD7BrP8Y2UE/D/2/v3uOtq+bFj3+2Cj3dhJQjJOmrHF3sRHRRh5Mc6X45UqJwDqGfRI4kilCSFE4cPehOnfREUZxu5GArt/J1qSenCCndntJt//4Yc2m1nrn23nOvtde+fd6v13qNZ80x5phj7vGMtfd3jTnHhIhYbowyy7eXbWitKr1uEvtKkiRJU8oganLOrdK6xSI6vapjn1qZeSfwZsqS6CdRljHv9LUq3XkCx51Kt1Tp6mOUaa02eMsYZZYSEasCz6E82LfRQ4glSZKkQTCImpzzKDNLe0bE87sViohNgV0oD+RdNF6lmbmI8oDeZwPvrsn/X8rKdcMR8ZpxqpvKvv1+ldbdg0VEDAEvBh6gtLeJN1EuMz0zMx+cdAslSZKkKWIQNQmZeS+wH+Xnd35EbN5ZprokbxElIHh9tc9EvI0yC7Nxl/zXAEuAz0XEmyLiEX0YEUMRsRPwyWrTZO5JGs8ZlADpwxGxVk3+wZRL8k6vliyfkGpxjMMo90Id1XszJUmSpP5zYYl6h0TEvl3yTsjMr2bmtyNiB+A04LKIuAz4IeVyvOcDW1CCgd0z85sTPXBm3hwR76Jc0leXf21EbAWcCXwWODQiLgZuBlYDXkZ5uO/9lEDq4xM9doM2/jwiDgGOBn4REYso9y+tQJmd2gi4Gnhnlyp2bAu+hiirHT6P8jO7B9gzM2vvIRtniXOAmzPzsxM/G0mSJKkZg6h6wcPPgur093ubMvP8iFgX+Hdge+ANVdb1wBHASZl509JVjOvzwF7AVnWZmfmjiNiA8nypPSkr860B3An8ClgIfC4zb5zEsSckMz8eESOUlQRfCOwA3A0spgRP/5mZS62uV9mherUsqfY7ATguM39bt1NlrCXOAX5CCS4lSZKkKTE0Ojo63W2QZoSRkZFRgE0un8jK9ZpqoweuP34hSZKkSRoZGQFgeHi48SN0nImSOvjHuyRJksbiwhKSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0YREmSJElSAwZRkiRJktSAQZQkSZIkNWAQJUmSJEkNGERJkiRJUgMGUZIkSZLUgEGUJEmSJDVgECVJkiRJDRhESZIkSVIDBlGSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0sO90NkGaaoeOume4maBJGD1x/upsgSZLmCWeiJEmSJKkBZ6KmUEQcDrwfeF1mLmyw32eAfwOuyczn1OQvC9zfoCl7Z+YpEbE/8DngfZl5ZES8FLioQT1PzcwbI+JGYI3M7Pr/JyKOBN7bOnbHtnajwD3ATcB3gGMz81c19d0IPGUCbXxfZh45obORJEmSJsEgaoaJiEcDuwNLgPUjYrPMvLKj2EPABzq2rQ3sDVwFnNeR99Muh7uupp5tgC2A/67Z745xT2Bi2uteBlgZ2Ah4E7B3ROySmRfW7DcKfHCcui/rUxslSZKkWgZRM8/2wOOBw6vX/sAjgqjMfKjK+7tqVmlv4MeZ+Yi8bjLzupp6lqUEUee0ZpCmQG3dEbE9cDbwlYjYsGpfu4cmem6SJEnSVPGeqJlnH8pM0wnAL4E9ImKl6W3SYGTmIkpQtyJLX/YnSZIkzQgGUTNIRDwR2A4Yycy/AGcCKwB7TmvDBut44D5g14hYZrobI0mSJHUyiJpZ/hVYjhI8AZxepftPT3MGLzPvAq6m3Cf13GlujiRJkrQU74maWfahLJ5wJkBmZkRcBWwaEc/NzJ9Na+sG56YqfTIloGp5VLXiYTdLMvNjU9YqSZIkCYOoGSMing1sAlyWmTe2ZZ0GbEyZjXr7dLRtGvytSlfu2D5EWTK+m78ABlGSJEmaUgZRM8c+VXp6x/bTgY8Cr4mId2Xm35h+D01R2ZbWQhp3dWx/cKxnU0mSJEmD4D1RM0BEDAF7VW8/ExGjrRdwI6WfHg/sPF1t7HAHsExEjPX/Z/m2sk2tVaWdS5xLkiRJ085v9WeGrYGnUZY0v7Qm/x8oz4/an6VnqqbDLVX6JODmLmWe0lF2QqoVCtcDbgVyUq2TJEmSppBB1MzQupTvQ10eQrsA+AOwdUSsXfMQ2kH7PrAVsCVwVmdmNbP2IspS5Vd35o/j3ygzb6dXDxWWJEmSZhQv55tmVYC0M7AEOLeuTGYuoQQrQ8B+g2tdV6cDDwIfiYin1eS/B3gqcGpm3jvRSiPiZcChwJ3AR/rRUEmSJKnfnIkajEMiYt8ueZ+nLKRwWvWMpG5OplzOt29EHJaZD/a5jROWmT+JiPcCRwHXRMQi4HpgRcrs1IbAj4F3dali54hYp/r3o4BVgGHgxZRgcveOFQpbxlviHOD3mXlSk/ORJEmSmjCIGoyoXnU2q9KlLuNrl5nfi4is6nkFsKh/zWsuMz8aET+gXH73ImAnymp61wHvAE7KzLu77L5T9WpZAiwGPgV8IjOv77LfeEucA4wABlGSJEmaMkOjo6PT3QZpRhgZGRkFGB4enu6mSJIkaYqNjIwAMDw8PNR0X++JkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIkSZKkBgyiJEmSJKkBgyhJkiRJasAgSpIkSZIaMIiSJEmSpAYMoiRJkiSpAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhpYdrobIM00Q8ddM91N0Cw3euD6090ESZI0hZyJ0qRFxMKIGI2Ijaa7LZIkSdKgGERJkiRJUgMGUZIkSZLUgEGUJEmSJDXgwhLqh8dFxKeAXYHHAdcCR2XmV1oFIuIxwEHAa4BnAncBVwBHZOaP2srtC5wM/L/MPK79IBFxCbAVsGpm/jUiXgL8D/BmYEtgR+CvwK6Z+d0pOVNJkiTNe85EqR/OBF4FnAGcCqwHnBURrwKIiMcCFwMfAh4APgNcBGwLfC8idujx+O8Hng98CvgxcFWP9UmSJEldOROlfrgR2Coz7wKIiPOB/wb2A84DDgY2BxYCb8jMB6pyw8DlwMKIeHpm3jHJ468EbJSZN/d0FpIkSdIEOBOlfjiuFUBVvg48BKxdvd8XWAK8rRVAAWTmCHAi5RLAnXs4/hUGUJIkSRoUgyj1w6/b32Tm/cCdwIoRsRIlmLoqM++s2feKKt2wh+Mv7mFfSZIkqRGDKPXDvV22DwErV/++vUuZ31fpgh6Of08P+0qSJEmNGERpqrVmn/6hS/6qVfqXKh2t0qGasr0EWpIkSVJfGERpSlWLRVwPRESsVlNkyyr9RZXeV6UrtheKiCEevsdKkiRJmjYGURqEhcDywCci4u8rQkbE84C3Up7ttKja/Msq3S4ilmmr49+BJ0x9UyVJkqSxucS5BuFjlGdC7QVsEBHfAVanPBx3CNijtbx5Zl4VESPAZsAVEXEp8Fzgn4D/BV4wDe2XJEmS/s6ZKE25zLwXeClwGPBoyqzSNpTZp80y82sdu7wS+CLwLMpM1YpV+e8Pqs2SJElSN0Ojo6Pjl5LmgZGRkVGA4eHh6W6KJEmSptjIyAgAw8PDdQuajcmZKEmSJElqwCBKkiRJkhowiJIkSZKkBgyiJEmSJKkBgyhJknQKdkMAABn3SURBVCRJasAgSpIkSZIaMIiSJEmSpAYMoiRJkiSpAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIkSZKkBgyiJEmSJKmBZae7AdJMM3TcNdPdBEk9GD1w/elugiRpjjOIUk8i4nDg/TVZDwB3AD8DPp+Zp3TstxZwMLAtsCZwL/Br4Fzgk5l51xjHfDJwI2UmdffM/ErPJyJJkiRNkEGU+uVrwNVt75cFngTsDnw5Ip6dmYcCRMQWwIXAMsAi4BxgReCFwJHA/hGxRWbe2OVYr6EEUEuA/QGDKEmSJA2MQZT65dzMXNi5MSKOBq4CDomIzwH/ByykzDy9IDN/01H+UOAI4FPATl2OtTdwLWWWa9eIeHpm3tCn85AkSZLG5MISmlKZ2bpEbxnKpXvrA2sDizoDqMqHgJuA7SNiuc7MiNgYeC5wEWUG61HA66em9ZIkSdLSDKI0CDdV6ROAVmC0fkQs9f8vM0cpl+tt36Wufar0m8D5lEv6XldXlyRJkjQV/MNTg7BOld4E/Bz4A/B84JKI2CMiHtdeODMvycwLMvP+9u0RsSzwr8BtwEWZeTflnqqnUma5JEmSpClnEKUpFRGbAK8C7gFagdHewF3AFsAZwF8iYiQijomIzceobltgdeCrbQHWaVW6/5ScgCRJktTBhSXULztWy5a3LAesC7yS8v/s7Zn5Z4DM/HZE/CNlifPdKKv4Pa96HRQRlwOvzczrO46xd5We3rbtAuBWyj1UT8rMP/X3tCRJkqRHciZK/bID5XlRrdfBwIuBbwHbZuan2wtn5g2ZeQCwBiV4eidwMfAgZYbq2xGxoFU+IlamzGj9Abi0rZ77ga9SgrbXTtXJSZIkSS3ORKlfXle3xPl4qoUkrqpeH4+I9YDzKPdR7QGcXBXdHVi+ej0YEXXV7Qcc3bjlkiRJUgMGURqoiDiBEhA9v+7ZTpl5bUQcRrnX6VltWa1V+U4B7q6p+lWl+tgiMy/vc7MlSZKkvzOI0qAtAVYDdgQ+OU7Z3wNU91ptDvw2M/euKxgRN1Ie0rs/YBAlSZKkKeM9URq0k4D7gCMj4uWdmRGxOvBeymp+Z1Wb9wGGgFPHqPeLwEPAbhGxSl9bLEmSJLVxJkoDlZm/iYh9KEHPBRHxQ+BKyiV6zwReATwaeHXbSnuvqdJTxqj3/yLi28DLgFcDn5miU5AkSdI850yUBi4zzwSeAxwPLKCsqncQ8ALgTGCjzDwbICI2o9wb9YPM/PU4VbcWofCZUZIkSZoyQ6Ojo9PdBmlGGBkZGQXY5PLlp7spknoweuD6090ESdIsMDIyAsDw8PBQ0329nE/q4B9gkiRJGouX80mSJElSAwZRkiRJktSAQZQkSZIkNWAQJUmSJEkNGERJkiRJUgMGUZIkSZLUgEGUJEmSJDVgECVJkiRJDRhESZIkSVIDBlGSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0YREmSJElSAwZRkiRJktSAQZQkSZIkNbDsdDdAmmmGjrtmupsgSdKcN3rg+tPdBGnSnImSJEmSpAaciVJfRMQFwMuBb2Tmv4xTdjlgJ2Bv4DnAk4ElwLXAOcBnM3NJxz77AidPpC2ZOdS0/ZIkSdJEGUSpZxGxBvAySiC0bUSsmZk3din7VOArwAuAPwHfAm4EVgb+Cfg4cEBEvDQzr6up4lLgkr6fhCRJkjRBBlHqh72AZYCPAYcDrwOO6CwUEcsDFwPrAh8FDs/Me9vyh4C3AZ8AzoyIF2TmQx3VXJKZh0/BOUiSJEkT4j1R6od9gNsoQdTtwOurgKjTYZQA6rTMPKQ9gALIzNHM/CRwGrAJsM3UNluSJElqziBKPYmIDYANgIsz8x7gXGAt4KUd5ZYB9gMeAA4ap9pPAv8F/KXf7ZUkSZJ65eV86tVrq/TMKj2j2rY/cFFbuc2B1YDLM/PmsSrMzB8CP+xzOyVJkqS+MIjSpFWzS68G7gS+Xm2+iLJgxI4R8cTMvKXavk6V/rzHw74kIg4fI//CzPx+j8eQJEmSujKIUi9eBqwBfKl1f1NmPhgRXwHeQlnC/BNV2SdV6W2dlUTE2pT7qjotzsyFHdu2ql7d/BUwiJIkSdKUMYhSL1qBz+kd20+lBFH783AQdWuVrlpTz9rA+2u2Xwos7Nj2AVfnkyRJ0nQyiNKkRMRKwI7V2wsioq7Y+hGxWWZeCVxfbVuns1BmXgz8fTW/iHgcNTNWkiRJ0kxgEKXJ2g1YnrIAxI9r8gN4CWU26krKrNJfKfc0rZqZBkmSJEmalQyiNFmtS/nekZlXdGZGxNMos097RMSBmXlnRHwBeAdwNCW46sal9yVJkjRj+ceqGouIpwNbAouB79aVyczfAd8BVgD2rDYfClwD7BcRn46IFWrq3hg4r3r7UH9bLkmSJPXOmShNxt6Ue5hOy8zRMcqdTHno7v7A5zLznojYBvgy8O/A3hFxIXAdsADYDBiu9r0AeHNNneMtcQ5wRmb+cqInI0mSJDVhEKXJeE2VnjJOuXMo90FtGhHPzcyfZeYfI2JbYDvKJYHPA/4FeAC4ATgRWJiZP+pS53hLnANcDRhESZIkaUoMjY6ONZEgzR8jIyOjAMPDw+MVlSRJ0iw3MjICwPDw8NA4RZfiPVGSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0YREmSJElSAwZRkiRJktSAQZQkSZIkNWAQJUmSJEkNGERJkiRJUgMGUZIkSZLUgEGUJEmSJDVgECVJkiRJDRhESZIkSVIDBlGSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0YREmSJElSA8tOdwOkmWbouGumuwmSJEnzyuiB6093ExqZsUFURBwOvL96+77MPHKMsscDb63ePiMzF0fEJcBWEzjUFzNz347jjeeGzFwrItYCrgcuzcyXjNG+VluekZmLa/K3BfYHNgNWA/4EXAv8F3B2Zj7QUb513E73AbcBVwNfyMyzJng+44qI5wFvqs7jacADwM+BU4H/7Gxjn455HPB2YOvMvKTf9UuSJEmTMWODqA47A7VBVEQMVfndfBL46xj5V1fpJTV5+wJPr6ljrPomLCIeC5wM7AncAXwDWAysDrwcOAO4MiJ2ycw/1FRxA7Cw7f0KwFOBbYFtI+JfgH0zc7SHNj4KOBw4lBKkXQAsAh5XHecEYLeI2C4z75nscSRJkqTZYjYEUTcDG0fEWnWzOJTZm6cAdwEr1uQf12W/R6hmOi5p3xYRL6EEUROqYxK+BOwGnA28MTNvbTv2Y4AjgIOBSyJio5ogZXFmHt5ZaUSsQQl09gF+CRzVQxv/A3gf8H1g18y8qaON/wXsRQnm9ujhOJIkSdKsMBsWlji3Snfqkr8rcDtw+WCa0x8RsRMlgLoC2KM9gALIzL9l5rsoQcq6wAcmWndm3lzVfQ/w7oh43CTbuC5wGPBnYLv2AKrVRuB1lBmx3SJivckcR5IkSZpNZsNM1HcoMxw7A5+oyd8ZOA9YeZCN6oO3V+n7M/PBMcr9B/BaYL+IeG9m3j+Ryqv7wr4K7A1sB5w+iTbuAywHnJCZtZcwZub9EXEA8ETglva8iNgDeBuwETAK/BQ4PjPP6KwnIl5P+Zk8C7gJOKZboyJiHcolhi+jXFZ4HWVW75iJ/nwkSZKkyZoNM1H3Uy5Ne1FErN6eERGbUi63+8p0NGyyImJ54EXAEuDSscpm5p+A7wKPB7ZseKjW7NyLm7axsl2VfnOsQpl5fmYuzMw/t7ZFxDGUe7rWBk6jBHHPAE6PiI+27x8RR1Bm3Fap0quBE6m5PLBa4OJHlJm271AC61uBDwPnRcQyzU9TkiRJmrjZMBMF5Z6hfYAdgJPatu9KWZDhW8B+XfY9MCLGWgjiI5l5b4/tW6ta3a9rfsf7tSkzPL8cZxaq5VrKqnhrA99u0K7W5XdPbrBPuzWr9FdNdoqILYCDgKuAbVvBVUSsRgl83hURX8/MyyLiWcAhlMBp69aMV0S8kjLD2F7vEPBF4DHAizJzpC3vWOD/UVYQ/HTTE5UkSZImarYEUd+iLByxM48MonYBzsvMv0VEt33f3i2jchzQaxD1dCa+PDqUGRcoAeBEtO6XemKDYwD8rUone6lj616qOxvut2+VvrN9dioz/xwRhwDnA68HLqPMKC0LfKj9ksHMPD8ivklZpbDlBcA/Aie2B1CV9wFvodyjZRAlSZKkKTMrgqjMvDcivg7sHBGrZObt1WVda1NmH8ZS+2ymPpvoc6JabqvS5SdY/wpV+ucxSy1tpSq9q+F+LX+hzGKt2vDYGwEPURbN6NTatmFH+qOast/jkUHUcJU+s8vM353AhhEx1Muy7pIkSdJYZkUQVTmbco/MKykPeN2V8kfzmPfrzFC/ozysdp2IeFRmPjRO+dYjnG9oeJy1qvS6hvu1XEcJotZhjCAqIlYBFrQ9y2pl4N7MvK+zbBUALwEWVJtWrdK62a5bO963ZsZeziODq04rdqlPkiRJ6tlsWFii5RuUJbtbD9bdBVhULbM9q2Tm3cBFlGBj67HKRsSqwOaUB/yOuQhFjS2q9HtN21i5sEr/eZxybwR+Xy0QASWAWVAFV49QPWB4ecosFzw8K7dUWZZ+7ldrRm2/zBwa42UAJUmSpCkza4KoKvD4JvDyiHg+5dlJs2pVvg7HV+lHImK5Mcp9gBJ0nFw3s9NNRDyNMmv3V0oAOhmnAfcBB9QFRNVxFgBvqN5eVKVXV+kWS+/B5sAQ8IvqfeveproVBDfpeP/TLtuJiOUi4uMR8da6dkqSJEn9MmuCqMrZlMvAjqfMSlw4dvGZKzMvpKw0twlwTkQ8oT0/IpaNiA8CbwV+Q1k4YUKqus4AHg0cmZn3TLKN11GWEH8icGFEPGKVvyqwOpXybKdFmXlZlbWwSo+qVuRrlV8NOLp6++UqPZOysMf7ImKNtrJbUFZjbHcZcD3lmVmbdeQdAryDh++bkiRJkqbEbLonCsrzou4DXgicPsGlycdb4vzezPxIX1rX3BsoK+i9EfhttXjGDcATgG0pq/79ANitmonr1Lm0+vKU+6BeTrlU8IvAsT228b3Akyir3l1ftfE3wFMol/mtRnmO1T6tHaqly4+lBDU/jYhFVdYrKfdYfbQVcGXmDRHxTuAE4KqIOIdyad9ulHvHntlW74MRsQ8leL4sIr4G/JYSiG5DCbDe0+P5SpIkSWOaVUFUtSjBtykPgf3qBHcbb4nz24FpCaIy837gTRHxJeAAYFPKPV+3Aj+nBDBnVeXqdC6tfj9lAYhLKJf/nduHNj4IvD4izgD+jbKa3iuqY/2UMkP2+c7nXWXmQRHx4+q89qrKXw0ckJnndJQ9MSJuogRArwNuAQ6jBMzHdpS9onrI8qHAP1ECsxsps5Mfzsw/9nrOkiRJ0liGRkddCVoCGBkZGQUYHvaKQEmSpLluZKTcmj88PDzUdN/Zdk+UJEmSJE2rWXU5nyYvIjYCdmywy8IBPKRYkiRJmnUMouaPjXjk/VPjuQRYPCUtkSRJkmYxg6h5IjMX8vDS45IkSZImyXuiJEmSJKkBgyhJkiRJasAgSpIkSZIa8J4oqUPrmQGSJElSHWeiJEmSJKmBodHR0elugyRJkiTNGs5ESZIkSVIDBlGSJEmS1IBBlCRJkiQ1YBAlSZIkSQ0YREmSJElSAwZRkiRJktSAQZQkSZIkNWAQJUmSJEkNGERJkiRJUgMGUZIkSZLUgEGUJEmSJDVgECVJkiRJDSw73Q2Q+ikilgXeCrwBeAbwB+Bk4COZef8E9n888EHglcCTgGuBj2XmmTVlFwDvAf4VeApwPXAi8OnMHO3LCc1CA+6DU4C9ulT10cw8ZFInMcv12gcddb0SWARsnJlX1+Q7DmoMuA8cBzX68Fk0DLwP2AJYCfg/4CvAEZl5d0dZx0GNAfeB46BGH/rgOcARwGaUPrgaODYzz6kpO6/GgUGU5poTgTcCVwDnAS+m/EG+IbDrWDtGxArARcDGwFnA74BdgDMiYrXMPKGt7DKUD/JXAN8AvgpsB5xA+ZB6Z1/PanYZSB9UNgD+CHy2prorejiH2W7SfdAuItaj/LLtlu846G4gfVBxHNTr5bNoa+DC6u3ZwO+BLYF3A9tExJaZeW9V1nHQ3UD6oOI4qNdLH2wIfA8YAs4A7gB2AM6OiHdl5tFtZefdOBgaHZ1zgaHmqYh4EfBdysDdPTNHI2IIWAjsA2yfmeePsf9/AB8CDsjME6ttKwFXUj4AnpGZf6q2vxo4FTgmMw+uti1H+cDfGtgwM382JSc6gw24D5YD7gbOz8ydp+6sZpde+6Ctnq0pgewTq01LzYI4DuoNuA8cBzX68Fl0LbAO8OLM/EG1bQj4T8o3+gdl5rHVdsdBjQH3geOgRh/64HvAJsBmmTlSbVsRuApYE1gzM/9SbZ9348B7ojSXvKVKP9CaNq7S9wCjwP7j7P9mOr7Fysw7KX/ULwBe3XGsB4APt5W9HziU8o3Nfr2cyCw2yD5YD1gO+GlfWj539NQHEbF8RHweuJjyf/nH4xzLcbC0QfaB46DepPsgItYHng18rfXHe9v+H6zebtdxLMfB0gbZB46Der30wcrACpTAdKS1PTPvolxe/FjKVSPtx5pX48AgSnPJlsAtmfnz9o2Z+XvgV8BW3XaMiGdSrt+9PDMf7Mj+nyrdqir7GGBT4OrMvK2j7A+AJWMda44bSB9UNqhSf2k+0qT7oLI65Zfd1ymXe9R+c+g4GNNA+qDiOKjXSx/cQblk7As1eX+r0hXBcTCOgfRBxXFQb9J9kJl3ZOaGXWb2nl2lf4T5Ow4MojQnVAN4TeC3XYosBh4XEat1yX9mlS61f2beDNwLrFttejrlfsK6sg9SbnxdtzNvrhtwH8DDvzTXjYjvRsSdEfGniDg5Iv6h8QnMAX3oA4DbgM0z81WZedMY5RwHNQbcB+A4WEqvfZCZN2bmxzLzGzXZO1XpL6rUcVBjwH0AjoOl9OmzqL2+ZSJinYg4njILeH7b5XnzchwYRGmueHyV/rVL/u1VukqX/CeMs/8dbfuOV/Z2YEG1Is58Msg+gId/aR5GWQHoJMo3a/sCP4iINcdp71zUax+Qmbdn5ncncCzHQb1B9gE4Dur03Ad1ImJ1Hr6U7KQqdRzUG2QfgOOgTr/74BLg15SV/r4L7NmWNy/HwZw6Gc1ry1Xp37rkt7Y/tof9F0ziWHd1KTMXDbIPAO6hfKDvlJl//0YyIt4LHAkcD8y3G4x77YOpOpbj4GH97ANwHNTpex9ExCqUyytXB45vu0/HcVBvkH0AjoM6/e6D7wH/C7yIssLfdyJiu8y8teGx5sw4MIjSXHFPlT66S/5jqvTuLvkT2f/uBmVHKdcAzyeD7AMyc6cu5Y6i3E+yfUSsWN0EO1/02gf9PpbjYGn97APHQb2+9kF1udOFwPOA84GDGh7LcbC0fvaB46BeX/sgM9/d+ndEfAw4mPL8qLdM8Fhzbhx4OZ/mituBh+g+Lb1KW7k6t3WU67Ry277jlV0FuCszH+qSP1cNsg+6qn7uP6F8STTfLuHotQ+acBzUG2QfdOU46E8fVAveXEn54/08YNfMfKCtiOOg3iD7oCvHwZR9Fh1KCYh2qN7Py3FgEKU5ITPvA26gPEuozjMoK9Tc2iX/V23lHiEinkyZgs5q02Lgvi5llwGe2lZ23hhkH0TEgoh4YfUgwDrLV+m9XfLnpD70QROLcRwsZZB94Dio168+iIiNKJcwPRP4IrBLZnZerrQYx8FSBtkHjoN6vfZBRDw+IraPiA0686q6/8DDz7BbzDwcBwZRmkuuANaIiEesAFOtzPMsyjdZtTLzd8DvgM0jonNcvKRKr6zKPkC5Lnjj6kGw7Tal3LfT9Vhz3ED6AFij+veXO+uJiAWUbyz/TPkFMt9Mug+acByMaSB9gONgLD31QUSsA3wLeBJwLPC6utkPx8GYBtIHOA7G0ksfrEeZ+Xt/Z0Z1f9rTqVbjm6/jwCBKc8mXqvTDrT/CqydzH0V50NtJ3XasfJky3X9Aa0P1YfBeyvW+7R/QX6Jc4/uBtrLLUa4PBvjcpM9idhtIH2TmdZQHkD43IvZqKzsEfARYDfhM6+GC80yvfdD0WI6DpQ2kDxwHY5p0H1TlT6f8/D6ZmQeN8zN0HNQbSB84DsbUy2fR9ylfbO4QEZu3NlYr7J1IuUSy/Tle824cDI2Ozsf/U5qrIuIMYA/Kw93+h7KKzBbAV4HdWx+iEXE4QGYe3rbvysCPKN/OnEP5hmUXYG3grZl5QlvZZYDLqvovBkaAl1MejHlMZh48hac5ow2wD4YpS66uAJxLuZxgC2ATSt/8c82lN/NCL31QU9dC4LXAxpl5dUee46CLAfaB46CLyfZBROwMnE1ZUewYoG724+bM/GxV3nHQxQD7wHHQRY+/k19KWRER4CzgFuBlwHOq7Tu2Zgfn4zgwiNKcUn3rcQjl2RBPoXyL8mXgY+0foBExCpCZQx37rw58GNie8mH8S+DozDyj5lgrUb5x2Z3yjITfAp+hfOM1p26ebGLAffBsyjNDtgFWovziPKXzWPNNr33QUddCuvwBX+U7DmoMuA8cBzUm2wcRcRzw9nGq/0lmbtRWh+OgxoD7wHFQow+/k4eBwymB12MpS8l/gbLM/IMdZefVODCIkiRJkqQGvCdKkiRJkhowiJIkSZKkBgyiJEmSJKkBgyhJkiRJasAgSpIkSZIaMIiSJEmSpAYMoiRJkiSpAYMoSZIkSWrAIEqSJEmSGjCIkiRJkqQGDKIkSZIkqQGDKEmSJElqwCBKkiRJkhowiJIkSZKkBgyiJEmSJKkBgyhJkiRJasAgSpIkSZIa+P89m0+pcUNjRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 424
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(important_features).plot(kind = 'barh', grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing more packages for model evaluation\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.preprocessing import label_binarize, LabelBinarizer\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to allow us to obtain aucroc score for a multi-class y variable\n",
    "\n",
    "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
    "\n",
    "    lb = LabelBinarizer()\n",
    "    lb.fit(y_test)\n",
    "\n",
    "    y_test = lb.transform(y_test)\n",
    "    y_pred = lb.transform(y_pred)\n",
    "\n",
    "    return roc_auc_score(y_test, y_pred, average=average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5862626051654125"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiclass_roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5429884580827977"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(y_test, y_pred,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24935288480700654"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(y_test, y_pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to produce confusion matrix, precision, recall and f1-score\n",
    "\n",
    "def classifications(y_test, y_pred):\n",
    "    print('Mean accuracy score %s ' % metrics.accuracy_score(y_test, y_pred), '\\n')\n",
    "    print(confusion_matrix(y_test, y_pred), '\\n')\n",
    "    print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy score 0.5429884580827977  \n",
      "\n",
      "[[17665  4319   151    18    19    30     0     0     0]\n",
      " [ 7199 10165   107     2    13    63     0     0     0]\n",
      " [ 2275  1540   273    27    52    55     0     0     0]\n",
      " [ 1007   124     3  1761   735     4     0     0     0]\n",
      " [ 2619   303     6   752  1481     3     0     0     0]\n",
      " [ 2044  2186    95     0     3    78     0     0     0]\n",
      " [   15     6     0    92    77     0     3     0     0]\n",
      " [  285   116     2    40    20     4     0     0     0]\n",
      " [   34     3     0     0     0     2     0     0     0]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\plog3\\Anaconda3\\envs\\dsi\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.53      0.80      0.64     22202\n",
      "          2       0.54      0.58      0.56     17549\n",
      "          3       0.43      0.06      0.11      4222\n",
      "          4       0.65      0.48      0.56      3634\n",
      "          5       0.62      0.29      0.39      5164\n",
      "          6       0.33      0.02      0.03      4406\n",
      "          7       1.00      0.02      0.03       193\n",
      "          8       0.00      0.00      0.00       467\n",
      "          9       0.00      0.00      0.00        39\n",
      "\n",
      "avg / total       0.52      0.54      0.50     57876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classifications(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17985,  4025,   133,    16,    16,    27,     0,     0,     0],\n",
       "       [ 7630,  9760,   103,     1,     6,    49,     0,     0,     0],\n",
       "       [ 2358,  1494,   242,    32,    43,    53,     0,     0,     0],\n",
       "       [ 1027,   116,     1,  1738,   749,     3,     0,     0,     0],\n",
       "       [ 2685,   271,     7,   730,  1470,     1,     0,     0,     0],\n",
       "       [ 2093,  2142,    87,     0,     2,    82,     0,     0,     0],\n",
       "       [   15,     6,     0,    80,    87,     0,     5,     0,     0],\n",
       "       [  296,   108,     3,    32,    26,     2,     0,     0,     0],\n",
       "       [   35,     3,     0,     0,     0,     1,     0,     0,     0]],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  }
 ],
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