04-violations-regression-models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Models for Violations Data\n",
    "We will use the same approach as the violation data, looking only at outside influences and see if we can model the number of daily intersection crashes in Chicago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from modules.myfuncs import *\n",
    "import warnings\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import metrics\n",
    "\n",
    "# import libraries\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import scipy.stats as stats\n",
    "import statsmodels.stats.api as sms\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.max_rows = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val(model, X, y):\n",
    "    # return a cross val score for my models\n",
    "    pred = cross_val_score(model, X, y, cv=5)\n",
    "    return pred.mean()\n",
    "\n",
    "def print_evaluate(true, predicted):  \n",
    "    # print out all of my stats for the model\n",
    "    mae = metrics.mean_absolute_error(true, predicted)\n",
    "    mse = metrics.mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(metrics.mean_squared_error(true, predicted))\n",
    "    r2_square = metrics.r2_score(true, predicted)\n",
    "    print('MAE:', mae)\n",
    "    print('MSE:', mse)\n",
    "    print('RMSE:', rmse)\n",
    "    print('R2 Square', r2_square)\n",
    "    \n",
    "def evaluate(true, predicted):\n",
    "    # get multiple stats on my model predictions\n",
    "    mae = metrics.mean_absolute_error(true, predicted)\n",
    "    mse = metrics.mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(metrics.mean_squared_error(true, predicted))\n",
    "    r2_square = metrics.r2_score(true, predicted)\n",
    "    return mae, mse, rmse, r2_square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqlite3 version: 2.6.0\n",
      "connected to database/rlc.db\n"
     ]
    }
   ],
   "source": [
    "conn = create_connection('database/rlc.db')  # function from myfuncs file\n",
    "c = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('cam_locations',), ('cam_startend',), ('daily_violations',), ('all_crashes',), ('hourly_congestion',), ('hourly_weather',), ('region_data',), ('intersection_cams',), ('all_hours',), ('int_startend',), ('intersection_chars',), ('signal_crashes',), ('daily_covid',), ('holidays',), ('rlc_all_hours',)]\n",
      "cam_locations\n",
      "\t-camera_id\n",
      "\t-intersection\n",
      "\t-address\n",
      "\t-violation_date\n",
      "\t-violations\n",
      "\t-latitude\n",
      "\t-longitude\n",
      "\t-month\n",
      "\t-day\n",
      "\t-weekday\n",
      "\t-year\n",
      "\n",
      "cam_startend\n",
      "\t-camera_id\n",
      "\t-start\n",
      "\t-end\n",
      "\n",
      "daily_violations\n",
      "\t-intersection\n",
      "\t-camera_id\n",
      "\t-address\n",
      "\t-violation_date\n",
      "\t-violations\n",
      "\t-latitude\n",
      "\t-longitude\n",
      "\t-month\n",
      "\t-day\n",
      "\t-weekday\n",
      "\t-year\n",
      "\n",
      "all_crashes\n",
      "\t-crash_record_id\n",
      "\t-rd_no\n",
      "\t-crash_date\n",
      "\t-posted_speed_limit\n",
      "\t-traffic_control_device\n",
      "\t-device_condition\n",
      "\t-weather_condition\n",
      "\t-lighting_condition\n",
      "\t-first_crash_type\n",
      "\t-trafficway_type\n",
      "\t-alignment\n",
      "\t-roadway_surface_cond\n",
      "\t-road_defect\n",
      "\t-report_type\n",
      "\t-crash_type\n",
      "\t-damage\n",
      "\t-prim_contributory_cause\n",
      "\t-sec_contributory_cause\n",
      "\t-street_no\n",
      "\t-street_direction\n",
      "\t-street_name\n",
      "\t-beat_of_occurrence\n",
      "\t-num_units\n",
      "\t-most_severe_injury\n",
      "\t-injuries_total\n",
      "\t-injuries_fatal\n",
      "\t-injuries_incapacitating\n",
      "\t-injuries_non_incapacitating\n",
      "\t-injuries_reported_not_evident\n",
      "\t-injuries_no_indication\n",
      "\t-injuries_unknown\n",
      "\t-crash_hour\n",
      "\t-crash_day_of_week\n",
      "\t-crash_month\n",
      "\t-latitude\n",
      "\t-longitude\n",
      "\t-lane_cnt\n",
      "\t-intersection_related_i\n",
      "\t-hit_and_run_i\n",
      "\t-crash_date_est_i\n",
      "\t-work_zone_i\n",
      "\t-work_zone_type\n",
      "\t-workers_present_i\n",
      "\n",
      "hourly_congestion\n",
      "\t-year\n",
      "\t-month\n",
      "\t-day\n",
      "\t-hour\n",
      "\t-region_id\n",
      "\t-bus_count\n",
      "\t-num_reads\n",
      "\t-speed\n",
      "\t-weekday\n",
      "\t-quantile_speed\n",
      "\n",
      "hourly_weather\n",
      "\t-temp\n",
      "\t-rain_1h\n",
      "\t-rain_3h\n",
      "\t-snow_1h\n",
      "\t-snow_3h\n",
      "\t-weather_main\n",
      "\t-time\n",
      "\t-year\n",
      "\t-month\n",
      "\t-day\n",
      "\t-hour\n",
      "\t-weekday\n",
      "\n",
      "region_data\n",
      "\t-region_id\n",
      "\t-region\n",
      "\t-description\n",
      "\t-north\n",
      "\t-south\n",
      "\t-east\n",
      "\t-west\n",
      "\n",
      "intersection_cams\n",
      "\t-intersection\n",
      "\t-latitude\n",
      "\t-longitude\n",
      "\t-cam1\n",
      "\t-cam2\n",
      "\t-cam3\n",
      "\t-region_id\n",
      "\n",
      "all_hours\n",
      "\t-datetime\n",
      "\t-day\n",
      "\t-hour\n",
      "\t-intersection\n",
      "\t-month\n",
      "\t-year\n",
      "\n",
      "int_startend\n",
      "\t-intersection\n",
      "\t-start\n",
      "\t-end\n",
      "\n",
      "intersection_chars\n",
      "\t-protected_turn\n",
      "\t-total_lanes\n",
      "\t-medians\n",
      "\t-exit\n",
      "\t-split\n",
      "\t-way\n",
      "\t-underpass\n",
      "\t-no_left\n",
      "\t-angled\n",
      "\t-triangle\n",
      "\t-one_way\n",
      "\t-turn_lanes\n",
      "\t-lat\n",
      "\t-long\n",
      "\t-rlc\n",
      "\t-intersection\n",
      "\t-daily_traffic\n",
      "\t-speed\n",
      "\n",
      "signal_crashes\n",
      "\t-crash_record_id\n",
      "\t-rd_no\n",
      "\t-crash_date\n",
      "\t-posted_speed_limit\n",
      "\t-traffic_control_device\n",
      "\t-device_condition\n",
      "\t-weather_condition\n",
      "\t-lighting_condition\n",
      "\t-first_crash_type\n",
      "\t-trafficway_type\n",
      "\t-alignment\n",
      "\t-roadway_surface_cond\n",
      "\t-road_defect\n",
      "\t-report_type\n",
      "\t-crash_type\n",
      "\t-damage\n",
      "\t-prim_contributory_cause\n",
      "\t-sec_contributory_cause\n",
      "\t-street_no\n",
      "\t-street_direction\n",
      "\t-street_name\n",
      "\t-beat_of_occurrence\n",
      "\t-num_units\n",
      "\t-most_severe_injury\n",
      "\t-injuries_total\n",
      "\t-injuries_fatal\n",
      "\t-injuries_incapacitating\n",
      "\t-injuries_non_incapacitating\n",
      "\t-injuries_reported_not_evident\n",
      "\t-injuries_no_indication\n",
      "\t-injuries_unknown\n",
      "\t-crash_hour\n",
      "\t-crash_day_of_week\n",
      "\t-crash_month\n",
      "\t-latitude\n",
      "\t-longitude\n",
      "\t-lane_cnt\n",
      "\t-intersection_related_i\n",
      "\t-hit_and_run_i\n",
      "\t-crash_date_est_i\n",
      "\t-work_zone_i\n",
      "\t-work_zone_type\n",
      "\t-workers_present_i\n",
      "\t-intersection\n",
      "\t-year\n",
      "\t-month\n",
      "\t-day\n",
      "\t-hour\n",
      "\t-region_id\n",
      "\t-time\n",
      "\t-weekday\n",
      "\t-start\n",
      "\t-end\n",
      "\t-rlc_state\n",
      "\n",
      "daily_covid\n",
      "\t-lab_report_date\n",
      "\t-cases_total\n",
      "\t-deaths_total\n",
      "\t-cases_age_0_17\n",
      "\t-cases_age_18_29\n",
      "\t-cases_age_30_39\n",
      "\t-cases_age_40_49\n",
      "\t-cases_age_50_59\n",
      "\t-cases_age_60_69\n",
      "\t-cases_age_70_79\n",
      "\t-cases_age_80_\n",
      "\t-cases_age_unknown\n",
      "\t-cases_female\n",
      "\t-cases_male\n",
      "\t-cases_unknown_gender\n",
      "\t-cases_latinx\n",
      "\t-cases_asian_non_latinx\n",
      "\t-cases_black_non_latinx\n",
      "\t-cases_white_non_latinx\n",
      "\t-cases_other_non_latinx\n",
      "\t-cases_unknown_race_eth\n",
      "\t-deaths_0_17_yrs\n",
      "\t-deaths_18_29_yrs\n",
      "\t-deaths_30_39_yrs\n",
      "\t-deaths_40_49_yrs\n",
      "\t-deaths_50_59_yrs\n",
      "\t-deaths_60_69_yrs\n",
      "\t-deaths_70_79_yrs\n",
      "\t-deaths_80_yrs\n",
      "\t-deaths_unknown_age\n",
      "\t-deaths_female\n",
      "\t-deaths_male\n",
      "\t-deaths_unknown_gender\n",
      "\t-deaths_latinx\n",
      "\t-deaths_asian_non_latinx\n",
      "\t-deaths_black_non_latinx\n",
      "\t-deaths_white_non_latinx\n",
      "\t-deaths_other_non_latinx\n",
      "\t-deaths_unknown_race_eth\n",
      "\t-hospitalizations_total\n",
      "\t-hospitalizations_age_0_17\n",
      "\t-hospitalizations_age_18_29\n",
      "\t-hospitalizations_age_30_39\n",
      "\t-hospitalizations_age_40_49\n",
      "\t-hospitalizations_age_50_59\n",
      "\t-hospitalizations_age_60_69\n",
      "\t-hospitalizations_age_70_79\n",
      "\t-hospitalizations_age_80_\n",
      "\t-hospitalizations_age_unknown\n",
      "\t-hospitalizations_female\n",
      "\t-hospitalizations_male\n",
      "\t-hospitalizations_unknown_gender\n",
      "\t-hospitalizations_latinx\n",
      "\t-hospitalizations_asian_non_latinx\n",
      "\t-hospitalizations_black_non_latinx\n",
      "\t-hospitalizations_white_non_latinx\n",
      "\t-hospitalizations_other_race_non_latinx\n",
      "\t-hospitalizations_unknown_race_ethnicity\n",
      "\n",
      "holidays\n",
      "\t-index\n",
      "\t-date\n",
      "\t-holiday\n",
      "\t-year\n",
      "\t-month\n",
      "\t-day\n",
      "\n",
      "rlc_all_hours\n",
      "\t-datetime\n",
      "\t-day\n",
      "\t-hour\n",
      "\t-intersection\n",
      "\t-month\n",
      "\t-year\n",
      "\t-rlc_state\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sql_fetch_tables(c, conn))\n",
    "\n",
    "\n",
    "\n",
    "def table_info(c, conn):\n",
    "    '''\n",
    "    prints out all of the columns of every table in db\n",
    "    c : cursor object\n",
    "    conn : database connection object\n",
    "    '''\n",
    "    tables = c.execute(\"SELECT name FROM sqlite_master WHERE type='table';\").fetchall()\n",
    "    for table_name in tables:\n",
    "        table_name = table_name[0]\n",
    "        table = pd.read_sql_query(\"SELECT * from {} LIMIT 0\".format(table_name), conn)\n",
    "        print(table_name)\n",
    "        for col in table.columns:\n",
    "            print('\\t-' + col)\n",
    "        print()\n",
    "\n",
    "table_info(c, conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query our data\n",
    "We will start by making a model to measure changing outside effects on our crashes.  These are weather, traffic, and temporal.  I will do d separate and simpler study of intersection characteristics and how they affect crashes.\n",
    "\n",
    "\n",
    "I would like to get daily red light camera intersection data with the following columns:\n",
    "- violations\n",
    "- weekday\n",
    "- month\n",
    "- year\n",
    "- region   (can add this post query with lambda function from intersection_cams and region_data tables, ugh!)\n",
    "- congestion (try peak, rush hour, average, etc)\n",
    "- precipitation\n",
    "- max_temp\n",
    "\n",
    "Each intersection should have one entry per day.  Weather will use aggregate daily values.\n",
    "Congestion will use aggragate also, although I am look for congestion at rush hour as a factor.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1217 entries, 0 to 1216\n",
      "Data columns (total 19 columns):\n",
      "violations                   1217 non-null int64\n",
      "year                         1217 non-null int64\n",
      "month                        1217 non-null int64\n",
      "day                          1217 non-null int64\n",
      "speed                        1202 non-null float64\n",
      "quantile_speed               1202 non-null float64\n",
      "min_speed                    1202 non-null float64\n",
      "avg_congestion               1202 non-null float64\n",
      "morning_rush_quant           1187 non-null float64\n",
      "morning_rush_congestion      1187 non-null float64\n",
      "afternoon_rush_speed         1192 non-null float64\n",
      "afternoon_rush_quant         1192 non-null float64\n",
      "afternoon_rush_congestion    1192 non-null float64\n",
      "weekday                      1217 non-null int64\n",
      "precip                       1217 non-null float64\n",
      "temp                         1217 non-null float64\n",
      "cases_total                  305 non-null float64\n",
      "morning_rush_speed           1187 non-null float64\n",
      "holiday                      35 non-null object\n",
      "dtypes: float64(13), int64(5), object(1)\n",
      "memory usage: 180.8+ KB\n",
      "None\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>violations</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
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       "      <th>quantile_speed</th>\n",
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       "      <th>morning_rush_speed</th>\n",
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       "      <td>27.088303</td>\n",
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       "      <td>3</td>\n",
       "      <td>1258</td>\n",
       "      <td>2017</td>\n",
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       "      <td>300.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.764421</td>\n",
       "      <td>Labor Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1366</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>24.121770</td>\n",
       "      <td>39.726954</td>\n",
       "      <td>19.216897</td>\n",
       "      <td>60.273046</td>\n",
       "      <td>11.733877</td>\n",
       "      <td>0.882661</td>\n",
       "      <td>20.264483</td>\n",
       "      <td>5.044710</td>\n",
       "      <td>0.949553</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>297.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.672759</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   violations  year  month  day      speed  quantile_speed  min_speed  \\\n",
       "0        1704  2017      9    1  24.150062       39.502084  19.306782   \n",
       "1        1623  2017      9    2  25.115236       49.359656  21.337644   \n",
       "2        1494  2017      9    3  26.001970       60.686873  22.096494   \n",
       "3        1258  2017      9    4  25.515012       54.571942  23.222586   \n",
       "4        1366  2017      9    5  24.121770       39.726954  19.216897   \n",
       "\n",
       "   avg_congestion  morning_rush_quant  morning_rush_congestion  \\\n",
       "0       60.497916           12.722263                 0.872777   \n",
       "1       50.640344           46.531120                 0.534689   \n",
       "2       39.313127           57.948611                 0.420514   \n",
       "3       45.428058           58.340201                 0.416598   \n",
       "4       60.273046           11.733877                 0.882661   \n",
       "\n",
       "   afternoon_rush_speed  afternoon_rush_quant  afternoon_rush_congestion  \\\n",
       "0             20.869598             10.028837                   0.899712   \n",
       "1             22.993506             23.487764                   0.765122   \n",
       "2             24.317101             41.049373                   0.589506   \n",
       "3             24.023103             36.275404                   0.637246   \n",
       "4             20.264483              5.044710                   0.949553   \n",
       "\n",
       "   weekday  precip    temp  cases_total  morning_rush_speed    holiday  \n",
       "0        4    0.00  294.15          NaN           23.075891       None  \n",
       "1        5    0.00  292.15          NaN           26.324984       None  \n",
       "2        6    6.60  295.15          NaN           27.088303       None  \n",
       "3        0    1.12  300.15          NaN           26.764421  Labor Day  \n",
       "4        1    0.00  297.15          NaN           22.672759       None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a df with all relevant data.\n",
    "\n",
    "int_df = pd.read_sql_query('''\n",
    "WITH \n",
    "    ah AS(\n",
    "        SELECT *\n",
    "        FROM rlc_all_hours\n",
    "        GROUP by year, month, day\n",
    "        ),\n",
    "\n",
    "    v as\n",
    "    (SELECT intersection,\n",
    "            SUM(violations) AS violations,\n",
    "            month,\n",
    "            day,\n",
    "            year,\n",
    "            violation_date\n",
    "    FROM daily_violations\n",
    "    GROUP BY year, month, day, intersection\n",
    "    ),\n",
    "\n",
    "ic as\n",
    "    (SELECT region_id,\n",
    "            intersection\n",
    "     FROM intersection_cams\n",
    "    ),\n",
    "\n",
    "\n",
    "\n",
    "c as\n",
    "    (\n",
    "        WITH allcg as \n",
    "             (SELECT AVG(speed) as speed,\n",
    "                     AVG(quantile_speed) as quantile_speed,\n",
    "                     100-AVG(quantile_speed) as avg_congestion,\n",
    "                     MIN(speed) as min_speed,\n",
    "                    region_id,\n",
    "                    month,\n",
    "                    day,\n",
    "                    weekday,\n",
    "                    year\n",
    "             FROM hourly_congestion\n",
    "             GROUP BY year, month, day, region_id\n",
    "            ),\n",
    "\n",
    "            rh1 as \n",
    "             (SELECT \n",
    "                    AVG(speed) as morning_rush_speed,\n",
    "                    MIN(quantile_speed) as morning_rush_quant,\n",
    "                    100 - MIN(quantile_speed) as morning_rush_congestion,\n",
    "                    region_id,\n",
    "                    month,\n",
    "                    day,\n",
    "                    weekday,\n",
    "                    year\n",
    "             FROM hourly_congestion\n",
    "             WHERE hour > 5 AND hour < 10\n",
    "             GROUP BY year, month, day, region_id\n",
    "            ),\n",
    "\n",
    "            rh2 as \n",
    "             (SELECT MIN(speed) as afternoon_rush_speed,\n",
    "                    MIN(quantile_speed) as afternoon_rush_quant,\n",
    "                    100 - MIN(quantile_speed) as afternoon_rush_congestion,\n",
    "                    region_id,\n",
    "                    month,\n",
    "                    day,\n",
    "                    year\n",
    "             FROM hourly_congestion\n",
    "             WHERE hour > 16 AND hour < 20\n",
    "             GROUP BY year, month, day, region_id\n",
    "            )\n",
    "\n",
    "    SELECT AVG(allcg.min_speed) as min_speed,\n",
    "            AVG(allcg.speed) as speed,\n",
    "            allcg.month,\n",
    "            allcg.day,\n",
    "            allcg.weekday,\n",
    "            allcg.year,\n",
    "            AVG(allcg.quantile_speed) as quantile_speed,\n",
    "            AVG(allcg.avg_congestion) as avg_congestion,\n",
    "\n",
    "            AVG(rh1.morning_rush_speed) as morning_rush_hour,\n",
    "            AVG(rh1.morning_rush_quant) as morning_rush_quant ,\n",
    "            AVG(rh1.morning_rush_congestion) as morning_rush_congestion,\n",
    "            AVG(rh1.morning_rush_speed) as morning_rush_speed,\n",
    "\n",
    "            AVG(rh2.afternoon_rush_speed) as afternoon_rush_speed,\n",
    "            AVG(rh2.afternoon_rush_quant) as afternoon_rush_quant,\n",
    "            AVG(rh2.afternoon_rush_congestion) as afternoon_rush_congestion\n",
    "\n",
    "     FROM allcg\n",
    "     LEFT JOIN rh1\n",
    "         ON rh1.year = allcg.year\n",
    "         AND rh1.month = allcg.month\n",
    "         AND rh1.day = allcg.day\n",
    "         AND rh1.region_id = allcg.region_id\n",
    "     LEFT JOIN rh2\n",
    "         ON rh2.year = allcg.year\n",
    "         AND rh2.month = allcg.month\n",
    "         AND rh2.day = allcg.day\n",
    "         AND rh2.region_id = allcg.region_id\n",
    "     \n",
    "     GROUP BY allcg.year, allcg.month, allcg.day\n",
    "     ORDER BY allcg.year, allcg.month, allcg.day\n",
    "),\n",
    "\n",
    "\n",
    " wx as\n",
    "    (SELECT temp,\n",
    "            SUM(snow_1h)+SUM(rain_1h) as precip,\n",
    "            month,\n",
    "            day,\n",
    "            weekday,\n",
    "            year    \n",
    "     FROM hourly_weather\n",
    "     GROUP BY year, month, day\n",
    "    ),\n",
    "cov as\n",
    "    (SELECT CAST(cases_total as INT) as cases_total,\n",
    "            lab_report_date\n",
    "    FROM daily_covid\n",
    "            \n",
    "    ),\n",
    "hol as\n",
    "    (SELECT year, month, day, holiday FROM holidays\n",
    "    )\n",
    "\n",
    "SELECT  SUM(v.violations) as violations,\n",
    "        v.year,\n",
    "        CAST(v.month as INT) as month,\n",
    "        v.day,\n",
    "\n",
    "        AVG(c.speed) as speed, \n",
    "        CAST(c.quantile_speed as REAL) as quantile_speed,\n",
    "        CAST(c.min_speed as REAL) as min_speed,\n",
    "        AVG(c.avg_congestion) as avg_congestion,\n",
    "        AVG(c.morning_rush_quant) as morning_rush_quant,\n",
    "        (AVG(c.morning_rush_congestion))/100 as morning_rush_congestion,\n",
    "\n",
    "        AVG(c.afternoon_rush_speed) as afternoon_rush_speed,\n",
    "        AVG(c.afternoon_rush_quant) as afternoon_rush_quant,\n",
    "        (AVG(c.afternoon_rush_congestion))/100 as afternoon_rush_congestion,\n",
    "\n",
    "        wx.weekday,\n",
    "        wx.precip,\n",
    "        wx.temp,\n",
    "        cov.cases_total,\n",
    "        AVG(c.morning_rush_speed) as morning_rush_speed,\n",
    "        hol.holiday\n",
    "\n",
    "        \n",
    "\n",
    "FROM v\n",
    "LEFT JOIN ah\n",
    "    ON v.year = ah.year\n",
    "    AND v.month = ah.month\n",
    "    AND v.day = ah.day\n",
    "LEFT JOIN ic\n",
    "  ON v.intersection = ic.intersection\n",
    "LEFT JOIN c\n",
    "  ON c.year = v.year\n",
    "      AND c.month = v.month\n",
    "      AND c.day = v.day\n",
    "LEFT JOIN wx\n",
    "  ON wx.year = v.year\n",
    "      AND wx.month = v.month\n",
    "      AND wx.day = v.day\n",
    "LEFT JOIN cov\n",
    "    ON DATE(cov.lab_report_date) = DATE(v.violation_date)\n",
    "LEFT JOIN hol\n",
    "    ON hol.year = v.year\n",
    "    AND hol.month = v.month\n",
    "    AND hol.day = v.day\n",
    "\n",
    "    \n",
    "WHERE v.violation_date >= \\'2017-09-01\\' AND v.violation_date <= \\'2020-12-31\\' \n",
    "\n",
    "GROUP BY v.year, v.month, v.day\n",
    "\n",
    "ORDER BY v.year, v.month, v.day\n",
    "\n",
    "                                    ''', conn)\n",
    "\n",
    "# LOOKS GOOD!!\n",
    "print(int_df.info())\n",
    "int_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a pretty solid dataframe to do analysis on the red light intersections.\n",
    "\n",
    "We would like to know what factors affect the target (violations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1217 entries, 0 to 1216\n",
      "Data columns (total 19 columns):\n",
      "violations                   1217 non-null int64\n",
      "year                         1217 non-null int64\n",
      "month                        1217 non-null int64\n",
      "day                          1217 non-null int64\n",
      "speed                        1202 non-null float64\n",
      "quantile_speed               1202 non-null float64\n",
      "min_speed                    1202 non-null float64\n",
      "avg_congestion               1202 non-null float64\n",
      "morning_rush_quant           1187 non-null float64\n",
      "morning_rush_congestion      1187 non-null float64\n",
      "afternoon_rush_speed         1192 non-null float64\n",
      "afternoon_rush_quant         1192 non-null float64\n",
      "afternoon_rush_congestion    1192 non-null float64\n",
      "weekday                      1217 non-null int64\n",
      "precip                       1217 non-null float64\n",
      "temp                         1217 non-null float64\n",
      "cases_total                  1217 non-null float64\n",
      "morning_rush_speed           1187 non-null float64\n",
      "holiday                      1217 non-null bool\n",
      "dtypes: bool(1), float64(13), int64(5)\n",
      "memory usage: 172.5 KB\n"
     ]
    }
   ],
   "source": [
    "int_df['holiday'] = int_df.holiday.notnull()\n",
    "int_df.cases_total.fillna(0, inplace=True)\n",
    "int_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1186.25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rough number of expected output.  We are very close. Could be more or fewer intersections for parts\n",
    "365*3.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change weekday to string\n",
    "days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "int_df['weekday'] = int_df['weekday'].apply(lambda x: days_of_week[x])\n",
    "\n",
    "months = ['January', 'February', 'March', 'April', 'May', 'June',\n",
    "         'July', 'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "int_df['month'] = int_df['month'].apply(lambda x: months[x-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['violations', 'year', 'month', 'day', 'speed', 'quantile_speed',\n",
       "       'min_speed', 'avg_congestion', 'morning_rush_quant',\n",
       "       'morning_rush_congestion', 'afternoon_rush_speed',\n",
       "       'afternoon_rush_quant', 'afternoon_rush_congestion', 'weekday',\n",
       "       'precip', 'temp', 'cases_total', 'morning_rush_speed', 'holiday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "****************************************************************************************************\n",
      "*                                            VIOLATIONS                                            *\n",
      "****************************************************************************************************\n",
      "Red Light Camera - Regression Analysis and Diagnostics Violations\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             violations   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.014e+32\n",
      "Date:                Sun, 14 Mar 2021   Prob (F-statistic):               0.00\n",
      "Time:                        15:57:05   Log-Likelihood:                 30846.\n",
      "No. Observations:                1182   AIC:                        -6.169e+04\n",
      "Df Residuals:                    1180   BIC:                        -6.168e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1.137e-13   1.52e-13      0.750      0.453   -1.84e-13    4.11e-13\n",
      "violations     1.0000   9.93e-17   1.01e+16      0.000       1.000       1.000\n",
      "==============================================================================\n",
      "Omnibus:                     1050.810   Durbin-Watson:                   0.031\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               83.450\n",
      "Skew:                          -0.229   Prob(JB):                     7.57e-19\n",
      "Kurtosis:                       1.782   Cond. No.                     7.08e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.08e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "\n",
      "\n",
      "****************************************************************************************************\n",
      "*                                              PRECIP                                              *\n",
      "****************************************************************************************************\n",
      "Red Light Camera - Regression Analysis and Diagnostics Violations\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             violations   R-squared:                       0.016\n",
      "Model:                            OLS   Adj. R-squared:                  0.016\n",
      "Method:                 Least Squares   F-statistic:                     19.66\n",
      "Date:                Sun, 14 Mar 2021   Prob (F-statistic):           1.01e-05\n",
      "Time:                        15:57:05   Log-Likelihood:                -8518.5\n",
      "No. Observations:                1182   AIC:                         1.704e+04\n",
      "Df Residuals:                    1180   BIC:                         1.705e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1507.7253     10.239    147.249      0.000    1487.636    1527.815\n",
      "precip        -3.3156      0.748     -4.434      0.000      -4.783      -1.849\n",
      "==============================================================================\n",
      "Omnibus:                       17.362   Durbin-Watson:                   0.355\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               17.878\n",
      "Skew:                           0.301   Prob(JB):                     0.000131\n",
      "Kurtosis:                       3.001   Cond. No.                         14.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n",
      "\n",
      "****************************************************************************************************\n",
      "*                                               TEMP                                               *\n",
      "****************************************************************************************************\n",
      "Red Light Camera - Regression Analysis and Diagnostics Violations\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             violations   R-squared:                       0.441\n",
      "Model:                            OLS   Adj. R-squared:                  0.440\n",
      "Method:                 Least Squares   F-statistic:                     929.8\n",
      "Date:                Sun, 14 Mar 2021   Prob (F-statistic):          4.46e-151\n",
      "Time:                        15:57:05   Log-Likelihood:                -8184.9\n",
      "No. Observations:                1182   AIC:                         1.637e+04\n",
      "Df Residuals:                    1180   BIC:                         1.638e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept  -3974.1513    179.363    -22.157      0.000   -4326.057   -3622.245\n",
      "temp          19.0751      0.626     30.493      0.000      17.848      20.302\n",
      "==============================================================================\n",
      "Omnibus:                       48.303   Durbin-Watson:                   0.838\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               86.979\n",
      "Skew:                           0.303   Prob(JB):                     1.30e-19\n",
      "Kurtosis:                       4.182   Cond. No.                     7.18e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.18e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "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>ind_var</th>\n",
       "      <th>r_squared</th>\n",
       "      <th>intercept</th>\n",
       "      <th>slope</th>\n",
       "      <th>p-value</th>\n",
       "      <th>normality (JB)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>precip</td>\n",
       "      <td>0.016391</td>\n",
       "      <td>1.507725e+03</td>\n",
       "      <td>-3.315640</td>\n",
       "      <td>1.009767e-05</td>\n",
       "      <td>17.878072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>temp</td>\n",
       "      <td>0.440710</td>\n",
       "      <td>-3.974151e+03</td>\n",
       "      <td>19.075055</td>\n",
       "      <td>4.459325e-151</td>\n",
       "      <td>86.978550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>violations</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.136868e-13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>83.449980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ind_var  r_squared     intercept      slope        p-value  \\\n",
       "1      precip   0.016391  1.507725e+03  -3.315640   1.009767e-05   \n",
       "2        temp   0.440710 -3.974151e+03  19.075055  4.459325e-151   \n",
       "0  violations   1.000000  1.136868e-13   1.000000   0.000000e+00   \n",
       "\n",
       "   normality (JB)  \n",
       "1       17.878072  \n",
       "2       86.978550  \n",
       "0       83.449980  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets look at some regressions for all of my chosen columns\n",
    "\n",
    "\n",
    "\n",
    "# Eliminated and useful features\n",
    "target = 'violations'\n",
    "drop_me = ['day', 'min_speed', 'speed', 'quantile_speed', 'month',\n",
    "          'afternoon_rush_speed', 'morning_rush_speed', 'year', 'afternoon_rush_congestion',\n",
    "          'morning_rush_congestion', 'morning_rush_quant', 'afternoon_rush_quant', 'cases_total',\n",
    "          'avg_congestion']\n",
    "categoricals = ['weekday', 'holiday']\n",
    "\n",
    "df_cont = int_df.copy()\n",
    "df_cont.drop(columns=drop_me+categoricals, inplace=True)\n",
    "\n",
    "\n",
    "\n",
    "results = []\n",
    "\n",
    "for idx, column in enumerate(df_cont.columns):\n",
    "    \n",
    "    print('\\n'*2)\n",
    "    print('*'*100)\n",
    "    print('*' + (49-len(column)//2)*' ' + column.upper() + (49-len(column)//2 - len(column)%2)*' ' + '*')\n",
    "    print('*'*100)\n",
    "\n",
    "    print (\"Red Light Camera - Regression Analysis and Diagnostics Violations\")\n",
    "    \n",
    "    f = '{}~{}'.format(target, column)  # create my model formula\n",
    "    model = smf.ols(formula=f, data=df_cont).fit()  # fit the model\n",
    "    print(model.summary())\n",
    "    \n",
    "    #fig, axes = plt.subplots(figsize=(15,12))\n",
    "    #fig = sm.graphics.plot_regress_exog(model, column, fig=fig)\n",
    "    #fig = sm.graphics.qqplot(model.resid, dist=stats.norm, line='45', fit=True)\n",
    "    #fig.tight_layout()\n",
    "    #plt.show()\n",
    "    \n",
    "    results.append([column, model.rsquared, model.params[0], model.params[1], model.pvalues[1], sms.jarque_bera(model.resid)[0]])\n",
    "\n",
    "result_df = pd.DataFrame(results, columns=['ind_var', 'r_squared', 'intercept', 'slope', 'p-value', 'normality (JB)' ])\n",
    "result_df.sort_values(by='r_squared')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot takes a few min\n",
    "pd.plotting.scatter_matrix(df_cont,figsize  = [20, 20]);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>violations</th>\n",
       "      <th>precip</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>violations</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128026</td>\n",
       "      <td>0.66386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>precip</td>\n",
       "      <td>-0.128026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.06380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>temp</td>\n",
       "      <td>0.663860</td>\n",
       "      <td>0.063800</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            violations    precip     temp\n",
       "violations    1.000000 -0.128026  0.66386\n",
       "precip       -0.128026  1.000000  0.06380\n",
       "temp          0.663860  0.063800  1.00000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cont.corr()  # create a correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiYAAAHWCAYAAABDtELCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAdMElEQVR4nO3de7RkZ1km8OclBhkhkDUCKkm4iAEmYgDJBMSMBgcwoEOWTCCJoNyGVgRBBTSMroiw1qDgMEsUkEa5KiaYEWw1mkFEkEsk7QAJCWaMQUgTMMolBFEw8s4fpxoqh+5z6jRn96m96/dbq1bX3rXrq+9kVU4//Xy7dlV3BwBgGdxspycAALCfYAIALA3BBABYGoIJALA0BBMAYGkIJgDA0hBMAIAtq6pXVtV1VfWBgzxeVfXiqrqqqi6tqm9fZFzBBAA4FK9OctoGjz80yfGz264kL1tkUMEEANiy7n57kk9ucMjpSV7bay5OcnRVfdNm4womAMAQjklyzdz2vtm+DX3NYNPZr+Ka92yroz+90zNgSs4/c6dnwBR975+kDusLDvB3baV+JGtLMPvt7u7dWxriK206z+GDCQAwOrMQspUgst6+JMfNbR+b5NrNnmQpBwAYwp4kPzz7dM79k1zf3R/b7EkaEwBgy6rqd5KcmuS2VbUvyc8nOTJJuvvXk1yY5GFJrkryuSSPX2RcwQQARq4HOKNlsyG7++xNHu8kT9nq61rKAQCWhsYEAEZuJxqToQgmADByQwSTnWIpBwBYGhoTABg5jQkAwAA0JgAwclNqTAQTABi5KQUTSzkAwNLQmADAyGlMAAAGoDEBgJGbUmMimADAyE0pmFjKAQCWhsYEAEZOYwIAMACNCQCMnMYEAGAAGhMAGLkpNSaCCQCM3JSCiaUcAGBpaEwAYOQ0JgAAA9CYAMDITakxEUwAYOSmFEws5QAAS0MwAQCWhmACACwN55gAwMhN6RwTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5KbUmAgmADByUwomlnIAgKWhMQGAkdOYAAAMQGMCACOnMQEAGIDGBABGbkqNiWACACM3pWBiKQcAWBoaEwAYOY0JAMAANCYAMHJTakwEEwAYuSkFE0s5AMDS0JgAwMhpTAAABqAxAYCRm1JjIpgAwMhNKZhYygEAlobGBABGTmMCADAAjQkAjJzGBABgAIIJALA0LOUAwMhZygEAGIDGBABGTmMCADAAjQkAjNyUGhPBBABGbkrBxFIOALA0NCYAMHIr1ZhU1XdW1S1n9x9TVS+qqjsNPzUAYNUsspTzsiSfq6p7JfnpJB9O8tqNnlBVu6pqb1Xt3Z3d2zBNAOBgurb/tlMWCSY3dncnOT3Jr3T3ryQ5aqMndPfu7j6pu0/alV3bMU8A4CB2KphU1WlVdWVVXVVV5xzg8TtW1Vur6r1VdWlVPWyzMRcJJjdU1bOTPCbJH1XVEUmOXGzKAMAUzfLAS5I8NMkJSc6uqhPWHfZzSd7Q3fdJclaSl2427iLB5Mwkn0/yxO7+eJJjkrxwC3MHAAa0Q43JyUmu6u6ru/sLSc7L2urKTaaW5Naz+7dJcu1mg276qZxZGHnR3PZHssk5JgDA5B2T5Jq57X1J7rfumOck+T9V9eNJbpnkQZsNusinch5RVX9TVddX1Weq6oaq+szi8wYAhjREYzL/QZbZbf1JowfqVXrd9tlJXt3dxyZ5WJLXVdWG2WOR65i8IMl/6e4PLnAsADAB3b072fCjtfuSHDe3fWy+cqnmiUlOm4337qq6RZLbJrnuYIMuco7J3wslALC8dugck0uSHF9Vd6mqm2ft5NY96475SJL/nCRV9R+S3CLJP2w06CKNyd6qOj/Jm7J2Euzaf4Tu31to2gDAoHbiuiPdfWNVPTXJRUmOSPLK7r68qp6bZG9370nyjCSvqKqfzNoyz+NmlyA5qEWCya2TfC7JQ+bnk0QwAYAV1t0XJrlw3b5z5+5fkeQ7tzLmIp/KefxWBgQADq9V+66cY6vqjVV1XVX9fVX976o69nBMDgBYLYuc/PqqrJ3McoesfWb5D2b7AIAlsGrflXO77n5Vd984u706ye0GnhcAsKBVCyb/WFWPqaojZrfHJPnE0BMDAFbPIsHkCUkeleTjST6W5IzZPgBgCUypMVnkUzkfSfLwwzAXAGDFHTSYVNVPd/cLqupX85XXvk93P23QmQEAC5nSx4U3akz2X4Z+7+GYCABwaFYimHT3H8zufq67f3f+sap65KCzAgBW0iInvz57wX0AAF+Vjc4xeWiShyU5pqpePPfQrZPcOPTEAIDVs9E5Jtdm7fyShyf5q7n9NyT5ySEnBQAsblXOMXl/kvdX1eu7+18P45wAgBW16XVMkty5qp6f5IQkt9i/s7u/ebBZAQALm1JjsuiX+L0sa+eVPDDJa5O8bshJAQCLm9KVXxcJJv+uu9+SpLr7w939nCTfM+y0AIBVtMhSzr9U1c2S/E1VPTXJR5PcfthpAQCLWrWlnJ9I8nVJnpbkvkl+KMljh5wUALCaFvkSv0tmdz+b5PHDTgcA2KopNSYbXWDtD3KAL+/br7t94zAALIGVCCZJfvmwzQIAIBtfYO1t++9X1c2T3G22eaULrgHA8liVxiRJUlWnJnlNkr9LUkmOq6rHdvfbh50aALBqFvm48P9M8pDuvjJJqupuSX4na5/QAQB22Eo1JkmO3B9KkqS7/19VHTngnACALVi1YLK3qn4zX74M/aNz028bBgDYFosEkycneUrWLrBWSd6e5KVDTgoAWNxKNSbd/fkkL5rdAAAGs9EF1t7Q3Y+qqstygAutdfeJg84MAFjIqjQmT5/9+aok70lyzfDTAQBW2UYXWPvY7O5RSV6e5JNJzktyQXf//WGYGwCwgCk1Jpt+u3B3/0J3f2vWToC9Q5K3VdWfDj4zAGAhXdt/2ymbBpM51yX5eJJPJLn9MNMBAFbZIpekf3KSM5PcLskFSZ7U3VcMPTEAYDFTWspZ5Domd0ryE939vqEnAwCstkWuY3LO4ZgIAHBoVq0xAQCW2JSCyVZOfgUAGJTGBABGTmMCADAAwQQAWBqWcgBg5CzlAAAMQGMCACOnMQEAGIDGBABGTmMCADAAjQkAjNyUGhPBBABGbkrBxFIOALA0NCYAMHIaEwCAAWhMAGDkptSYCCYAMHJTCiaWcgCApaExAYCR05gAAAxAYwIAIzelxkQwAYCRm1IwsZQDACwNjQkAjNyUGpPBg8nRnx76FVg1nz56p2fAlNz/4p2eAVP0vTs9gRHTmADAyE2pMXGOCQCwNDQmADByU2pMBBMAGLkpBRNLOQDA0hBMAGDkurb/toiqOq2qrqyqq6rqnIMc86iquqKqLq+q1282pqUcAGDLquqIJC9J8uAk+5JcUlV7uvuKuWOOT/LsJN/Z3Z+qqttvNq7GBAA4FCcnuaq7r+7uLyQ5L8np6455UpKXdPenkqS7r9tsUMEEAEZuh5Zyjklyzdz2vtm+eXdLcreqemdVXVxVp202qKUcAOArVNWuJLvmdu3u7t3zhxzgab1u+2uSHJ/k1CTHJvmLqrpndx/0uvCCCQCM3BAfF56FkN0bHLIvyXFz28cmufYAx1zc3f+a5ENVdWXWgsolBxvUUg4AcCguSXJ8Vd2lqm6e5Kwke9Yd86YkD0ySqrpt1pZ2rt5oUI0JAIzcTlxgrbtvrKqnJrkoyRFJXtndl1fVc5Ps7e49s8ceUlVXJPm3JM/q7k9sNK5gAgAjt1NXfu3uC5NcuG7fuXP3O8lPzW4LsZQDACwNjQkAjJzvygEAGIDGBABGbkqNiWACACM3pWBiKQcAWBoaEwAYOY0JAMAANCYAMHIaEwCAAWhMAGDkptSYCCYAMHJTCiaWcgCApaExAYCR05gAAAxAYwIAIzelxkQwAYCRm1IwsZQDACwNjQkAjJzGBABgABoTABi5KTUmggkAjNyUgomlHABgaQgmAMDSEEwAgKXhHBMAGDnnmAAADEBjAgAjN6XGRDABgJGbUjCxlAMALA2NCQCMnMYEAGAAGhMAGLkpNSaCCQCM3JSCiaUcAGBpaEwAYOQ0JgAAA9CYAMDITakxEUwAYOSmFEws5QAAS0NjAgAjpzEBABiAxgQARk5jAgAwAI0JAIzclBoTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5KbUmAgmADByUwomlnIAgKWhMQGAkdOYAAAMQDABAJaGpRwAGDlLOQAAA1i4MamqRyQ5JUkneUd3v3GwWQEAC1u5xqSqXprkR5NcluQDSX6kql6ywfG7qmpvVe39wqt3b89MAYDJW7Qx+e4k9+zuTpKqek3WQsoBdffuJLuT5Ojr01/tJAGAg1u5xiTJlUnuOLd9XJJLt386AMAqW7Qx+fokH6yq98y2/2OSd1fVniTp7ocPMTkAYHNTakwWDSbnDjoLAOCQrVww6e63DT0RAIANg0lVvaO7T6mqG5KbnMRaSbq7bz3o7ACATa1MY9Ldp8z+POrwTAcAWGWLXsfk/lV11Nz2rarqfsNNCwBYVNf233bKoh8XflmSz85tf262DwDYYasYTGr/xdWSpLu/GF8ACABss0WDydVV9bSqOnJ2e3qSq4ecGACwmJ1qTKrqtKq6sqquqqpzNjjujKrqqjppszEXDSY/muQBST6aZF+S+yXZteBzAYCJqaojkrwkyUOTnJDk7Ko64QDHHZXkaUn+cpFxF72OyXVJzlp4tgDAYbND54ScnOSq7r46SarqvCSnJ7li3XHPS/KCJM9cZNBFP5Vzt6p6S1V9YLZ9YlX93KIzBwCGM8RSTlXtqqq9c7f1KyXHJLlmbnvfbN+XVNV9khzX3X+46M+y6FLOK5I8O8m/Jkl3XxoNCgBMVnfv7u6T5m671x1yoJ7mSx+UqaqbJflfSZ6xlddd9JM1X9fd76m6yRxu3MoLAQDD2KGlnH1JjpvbPjbJtXPbRyW5Z5I/n+WHb0yyp6oe3t17Dzbooo3JP1bVXTNLQlV1RpKPLT53AGBiLklyfFXdpapunrWVlD37H+zu67v7tt195+6+c5KLk2wYSpLFG5OnJNmd5B5V9dEkH0ry6EP4IQCAbbYTjUl331hVT01yUZIjkryyuy+vqucm2dvdezYe4cA2DSazNaKTuvtBVXXLJDfr7hsO5cUAgOno7guTXLhu37kHOfbURcbcdClndpXXp87u/5NQAgDLZUqXpF90KefNVfXMJOcn+af9O7v7k4PMCgBY2E4Gie22aDB5QtZOfP2xdfu/eXunAwCsskWDyQlZCyWnZC2g/EWSXx9qUgDA4laxMXlNks8kefFs++zZvkcNMSkAYDUtGkzu3t33mtt+a1W9f4gJAQBbM6XGZNELrL23qu6/f6Oq7pfkncNMCQBYVYs2JvdL8sNV9ZHZ9h2TfLCqLkvS3X3iILMDAFbKosHktEFnAQAcsikt5SwUTLr7w0NPBABg0cYEAFhSK9eYAADLa0rBZNFP5QAADE5jAgAjpzEBABiAxgQARk5jAgAwAI0JAIzclBoTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5KbUmAgmADByUwomlnIAgKWhMQGAkdOYAAAMQGMCACM3pcZEMAGAkZtSMLGUAwAsDY0JAIycxgQAYAAaEwAYOY0JAMAANCYAMHIaEwCAAQgmAMDSsJQDACNnKQcAYAAaEwAYuSk1JoIJAIzclIKJpRwAYGloTABg5DQmAAAD0JgAwMhNqTERTABg5KYUTCzlAABLQ2MCACOnMQEAGIDGBABGTmMCADAAjQkAjNyUGpPBg8n5Zw79Cqya+1+80zNgSi6+/07PgEnqw/xyEwomlnIAgKVhKQcARk5jAgAwAI0JAIzclBoTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5KbUmAgmADByUwomlnIAgKWhMQGAkdOYAAAMQDABAJaGpRwAGDlLOQDAyquq06rqyqq6qqrOOcDjP1VVV1TVpVX1lqq602ZjCiYAMHJd23/bTFUdkeQlSR6a5IQkZ1fVCesOe2+Sk7r7xCQXJHnBZuMKJgDAoTg5yVXdfXV3fyHJeUlOnz+gu9/a3Z+bbV6c5NjNBhVMAGDkhmhMqmpXVe2du+1a97LHJLlmbnvfbN/BPDHJH2/2szj5FQD4Ct29O8nuDQ450IJPH/DAqsckOSnJd2/2uoIJAIzcDn0qZ1+S4+a2j01y7fqDqupBSX42yXd39+c3G1QwAYCR26FgckmS46vqLkk+muSsJD84f0BV3SfJy5Oc1t3XLTKoc0wAgC3r7huTPDXJRUk+mOQN3X15VT23qh4+O+yFSW6V5Her6n1VtWezcTUmADByO3WBte6+MMmF6/adO3f/QVsdU2MCACwNjQkAjNyULkkvmADAyE0pmFjKAQCWhsYEAEZOYwIAMACNCQCM3JQaE8EEAEZuSsHEUg4AsDQ0JgAwchoTAIABaEwAYOQ0JgAAA9CYAMDITakxEUwAYOSmFEws5QAAS0NjAgAjpzEBABiAxgQARk5jAgAwAMEEAFgalnIAYOQs5QAADEBjAgAjN6XGRDABgJGbUjCxlAMALA2NCQCMnMYEAGAAGhMAGDmNCQDAADQmADByU2pMBBMAGLkpBRNLOQDA0tCYAMDIaUwAAAagMQGAkZtSYyKYAMDITSmYWMoBAJaGxgQARk5jAgAwAI0JAIzclBoTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5KbUmCwUTKrqG5OcnKSTXNLdHx90VgDAStp0Kaeq/luS9yR5RJIzklxcVU8YemIAwGK6tv+2UxY5x+RZSe7T3Y/r7scmuW+Sn9noCVW1q6r2VtXeC6/ZvR3zBAAOYkrBZJGlnH1JbpjbviHJNRs9obt3J9mdJBedlj7k2QEAK2WRYPLRJH9ZVb+ftXNMTk/ynqr6qSTp7hcNOD8AYIUsEkz+dnbb7/dnfx61/dMBAFbZpsGku3/hcEwEADg0K/Vx4ao6KcnPJrnT/PHdfeKA8wIAFrRSwSTJb2ftkzmXJfnisNMBAFbZIsHkH7p7z+AzAQAOyao1Jj9fVb+R5C1JPr9/Z3f/3mCzAgBW0iLB5PFJ7pHkyHx5KaeTCCYAsARWrTG5V3d/2+AzAQAOyZSCySKXpL+4qk4YfCYAwMpbpDE5Jcljq+pDWTvHpJK0jwsDwHKYUmOySDA5bfBZAABkgaWc7v5wkuOSfM/s/ucWeR4AcHhM6duFNw0YVfXzSX4mybNnu45M8ltDTgoAWE2LLOX8QJL7JPm/SdLd11aVL/ADgCWxaueYfKG7u6o6SarqlgPPCQDYgikFk0XOFXlDVb08ydFV9aQkf5rkFcNOCwBYRYs0JrdLckGSzyS5e5JzkzxoyEkBAItbtcbkwd395u5+Vnc/s7vfnOShQ08MAFhuVXVaVV1ZVVdV1TkHePxrq+r82eN/WVV33mzMgzYmVfXkJD+W5Jur6tK5h45K8s6tTx8AGMJONCZVdUSSlyR5cJJ9SS6pqj3dfcXcYU9M8qnu/paqOivJLyU5c6NxN1rKeX2SP07y/CTzKeiG7v7kIfwMAMAAdmgp5+QkV3X31UlSVeclOT3JfDA5PclzZvcvSPJrVVXd3Qcb9KDBpLuvT3J9krO/unkDAGNTVbuS7Jrbtbu7d89tH5PkmrntfUnut26YLx3T3TdW1fVJvj7JPx7sdRc5+RUAWGJDNCazELJ7g0MO9Krrm5BFjrkJl5YHAA7Fvqx9Zc1+xya59mDHVNXXJLlNkg1PBxFMAGDkdui7ci5JcnxV3aWqbp7krCR71h2zJ8ljZ/fPSPJnG51fkljKAYDR24mTX2fnjDw1yUVJjkjyyu6+vKqem2Rvd+9J8ptJXldVV2WtKTlrs3EFEwDgkHT3hUkuXLfv3Ln7/5LkkVsZUzABgJFbtSu/AgAcFoIJALA0BBMAYGk4xwQARm5K55gIJgAwclMKJpZyAICloTEBgJHTmAAADEBjAgAjN6XGRDABgJGbUjCxlAMALA2NCQCMnMYEAGAAGhMAGLkpNSaCCQCM3JSCiaUcAGBpaEwAYOQ0JgAAA9CYAMDIaUwAAAagMQGAkZtSYyKYAMDITSmYWMoBAJaGxgQARk5jAgAwAI0JAIzclBoTwQQARm5KwcRSDgCwNDQmADByGhMAgAFoTABg5DQmAAADEEwAgKVhKQcARs5SDgDAADQmADByGhMAgAFoTABg5KbUmAgmADByUwomlnIAgKWhMQGAkdOYAAAMQGMCACM3pcakunun58BMVe3q7t07PQ+mwfuJ7eY9xeFgKWe57NrpCTAp3k9sN+8pBieYAABLQzABAJaGYLJcrN2ynbyf2G7eUwzOya8AwNLQmAAAS0Mw+SpV1R2q6oJNjjm1qv5wk2PuXVUPm9t+eFWds13zhPWq6l07PQd2TlUdXVU/ttPzgPUEk69Sd1/b3Wdsw1D3TvKlYNLde7r7F7dhXFZAVR2x1ed09wOGmAujcXQSwYSlI5hsQVX90vy/MKrqOVX1jKr6wGz7FlX1qqq6rKreW1UPPMAYJ1fVu2aPv6uq7l5VN0/y3CRnVtX7qurMqnpcVf3a7Dl3qqq3VNWlsz/vONv/6qp68Wycq6vqjNn+b6qqt8/G+kBV/afD8d+HYVTVnavqr6vqNbP3wAVV9XVV9XdVdW5VvSPJI6vqrlX1J1X1V1X1F1V1j9nzv6Gq3lhV75/dHjDb/9nZn6fO3i9vrKorqurXq8rvhun7xSR3nf2eeGFVPauqLpm9x34hucl77zdmv0t+u6oeVFXvrKq/qaqTZ8c9p6peV1V/Ntv/pB39yRg1v3y25rwkZ85tPyrJJXPbT0mS7v62JGcneU1V3WLdGH+d5Lu6+z5Jzk3yP7r7C7P753f3vbv7/HXP+bUkr+3uE5P8dpIXzz32TUlOSfL9WftFkyQ/mOSi7r53knsled+h/LAslbsn2T17D3wmX/6X7r909yndfV7WPjHx49193yTPTPLS2TEvTvK27r5Xkm9PcvkBxj85yTOSfFuSuyZ5xGA/CcvinCR/O/s98eYkx2ftfXDvJPetqu+aHfctSX4lyYlJ7pG13y+nZO099t/nxjsxyfcl+Y4k51bVHQ7HD8H0+K6cLeju91bV7Wf/w90uyaeSfGTukFOS/Ors2L+uqg8nudu6YW6TtcByfJJOcuQCL/0d+fJfFK9L8oK5x97U3V9MckVVfcNs3yVJXllVR84eF0zG75rufufs/m8ledrs/vlJUlW3SvKAJL9b9aUvzfja2Z/fk+SHk6S7/y3J9QcY/z3dffVsrN/J2nt5w3OnmJSHzG7vnW3fKmtB5SNJPtTdlyVJVV2e5C3d3VV1WZI7z43x+939z0n+uaremrWQ86bDNH8mRDDZuguSnJHkG7PWoMxb5GuUnpfkrd39A1V15yR/fghzmP+M9+fXv353v332r53vS/K6qnphd7/2EF6H5bH+c/37t/9p9ufNknx69q/f7Ryf1VBJnt/dL7/JzrXfUfO/Y744t/3F3PTvEO8htoWlnK07L8lZWQsn6/9F+fYkj06SqrpbkjsmuXLdMbdJ8tHZ/cfN7b8hyVEHec13zV4zs/HfsdEEq+pOSa7r7lck+c2s1feM2x2r6jtm98/OuvdAd38myYeq6pFJUmvuNXv4LUmePNt/RFXd+gDjn1xVd5mdW3Lm+vGZpPnfORclecKseUtVHVNVt9/ieKfPzrP7+iSn5qbL3LAwwWSLuvvyrP3P/NHu/ti6h1+a5IhZxXl+ksd19+fXHfOCJM+vqncmmf8kxVuTnLD/5Nd1z3laksdX1aVJfijJ0zeZ5qlJ3ldV703yX7O2Psy4fTDJY2fvgX+f5GUHOObRSZ5YVe/P2nkkp8/2Pz3JA2fvy79K8q0HeO67s3aO0geSfCjJG7d3+iyb7v5EknfW2sn7D07y+iTvnr1PLsjB/6F0MO9J8kdJLk7yvO6+djvny+pw5VdYcrM6/Q+7+54DjX9qkmd29/cPMT7TV1XPSfLZ7v7lnZ4L46cxAQCWhsYEAFgaGhMAYGkIJgDA0hBMAIClIZgAAEtDMAEAloZgAgAsjf8PK+tvLbK14zYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize = (10,8))\n",
    "sns.heatmap(df_cont.corr(), center=0.4, cmap = \"cool\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get rid of categories that have high correlation and others.\n",
    "df_continuous = df_cont.copy()\n",
    "df_ohe = int_df.drop(columns=list(df_continuous.columns) + drop_me +[target])\n",
    "\n",
    "\n",
    "# One hot encoding   \n",
    "df_ohe = pd.get_dummies(df_ohe, drop_first=True)\n",
    "df_final = pd.concat([df_continuous, df_ohe], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>violations</th>\n",
       "      <th>precip</th>\n",
       "      <th>temp</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday_Monday</th>\n",
       "      <th>weekday_Saturday</th>\n",
       "      <th>weekday_Sunday</th>\n",
       "      <th>weekday_Thursday</th>\n",
       "      <th>weekday_Tuesday</th>\n",
       "      <th>weekday_Wednesday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1704</td>\n",
       "      <td>0.00</td>\n",
       "      <td>294.15</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1623</td>\n",
       "      <td>0.00</td>\n",
       "      <td>292.15</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1494</td>\n",
       "      <td>6.60</td>\n",
       "      <td>295.15</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1258</td>\n",
       "      <td>1.12</td>\n",
       "      <td>300.15</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1366</td>\n",
       "      <td>0.00</td>\n",
       "      <td>297.15</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   violations  precip    temp  holiday  weekday_Monday  weekday_Saturday  \\\n",
       "0        1704    0.00  294.15    False               0                 0   \n",
       "1        1623    0.00  292.15    False               0                 1   \n",
       "2        1494    6.60  295.15    False               0                 0   \n",
       "3        1258    1.12  300.15     True               1                 0   \n",
       "4        1366    0.00  297.15    False               0                 0   \n",
       "\n",
       "   weekday_Sunday  weekday_Thursday  weekday_Tuesday  weekday_Wednesday  \n",
       "0               0                 0                0                  0  \n",
       "1               0                 0                0                  0  \n",
       "2               1                 0                0                  0  \n",
       "3               0                 0                0                  0  \n",
       "4               0                 0                1                  0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = 100\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_final.drop(['violations'], axis=1)\n",
    "y = df_final['violations']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pipeline = Pipeline([\n",
    "#     ('std_scalar', StandardScaler())\n",
    "# ])\n",
    "\n",
    "# X_train = pipeline.fit_transform(X_train)\n",
    "# X_test = pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(X, y)  # my regression object\n",
    "\n",
    "pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9a505d1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test - pred), bins=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Results:\n",
      "=========================\n",
      "Test set:\n",
      "MAE: 146.5066932069357\n",
      "MSE: 35267.1787303342\n",
      "RMSE: 187.79557697223382\n",
      "R2 Square 0.6573243592823195\n",
      "\n",
      "Train set:\n",
      "MAE: 160.66561473348423\n",
      "MSE: 42783.54117672398\n",
      "RMSE: 206.84182646825565\n",
      "R2 Square 0.6094400446825982\n"
     ]
    }
   ],
   "source": [
    "test_pred = reg.predict(X_test)\n",
    "train_pred = reg.predict(X_train)\n",
    "\n",
    "print(\"Linear Regression Results:\")\n",
    "print('=========================')\n",
    "print('Test set:')\n",
    "print_evaluate(y_test, test_pred)\n",
    "\n",
    "print()\n",
    "print('Train set:')\n",
    "print_evaluate(y_train, train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "    .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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Square</th>\n",
       "      <th>Cross Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>146.506693</td>\n",
       "      <td>35267.17873</td>\n",
       "      <td>187.795577</td>\n",
       "      <td>0.657324</td>\n",
       "      <td>0.593255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model         MAE          MSE        RMSE  R2 Square  \\\n",
       "0  Linear Regression  146.506693  35267.17873  187.795577   0.657324   \n",
       "\n",
       "   Cross Validation  \n",
       "0          0.593255  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(data=[[\"Linear Regression\", *evaluate(y_test, test_pred) , cross_val(reg, X_train, y_train)]], \n",
    "                          columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', \"Cross Validation\"])\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try Ridge Regression\n",
    "Would like to try this since we likely have collinearity in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression Results:\n",
      "=========================\n",
      "Test set:\n",
      "MAE: 147.2075004936809\n",
      "MSE: 35681.803639719896\n",
      "RMSE: 188.89627746390317\n",
      "R2 Square 0.6532956316778897\n",
      "\n",
      "Train set:\n",
      "MAE: 160.510047935772\n",
      "MSE: 42746.771435774666\n",
      "RMSE: 206.75292364504708\n",
      "R2 Square 0.6097757062007243\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "\n",
    "model = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10, 100])\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "test_pred = model.predict(X_test)\n",
    "train_pred = model.predict(X_train)\n",
    "\n",
    "print(\"Ridge Regression Results:\")\n",
    "print('=========================')\n",
    "print('Test set:')\n",
    "print_evaluate(y_test, test_pred)\n",
    "\n",
    "print()\n",
    "print('Train set:')\n",
    "print_evaluate(y_train, train_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge regression showed no improvement on original model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2 Square</th>\n",
       "      <th>Cross Validation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>146.506693</td>\n",
       "      <td>35267.17873</td>\n",
       "      <td>187.795577</td>\n",
       "      <td>0.657324</td>\n",
       "      <td>0.593255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>147.207500</td>\n",
       "      <td>35681.80364</td>\n",
       "      <td>188.896277</td>\n",
       "      <td>0.653296</td>\n",
       "      <td>0.593596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model         MAE          MSE        RMSE  R2 Square  \\\n",
       "0  Linear Regression  146.506693  35267.17873  187.795577   0.657324   \n",
       "1   Ridge Regression  147.207500  35681.80364  188.896277   0.653296   \n",
       "\n",
       "   Cross Validation  \n",
       "0          0.593255  \n",
       "1          0.593596  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = pd.DataFrame(data=[[\"Ridge Regression\", *evaluate(y_test, test_pred) , cross_val(RidgeCV(), X_train, y_train)]], \n",
    "                            columns=['Model', 'MAE', 'MSE', 'RMSE', 'R2 Square', \"Cross Validation\"])\n",
    "results_df = results_df.append(ridge, ignore_index=True)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      precip: -4.648070048246554\n",
      "        temp: 19.51906087135397\n",
      "     holiday: -15.387507936997403\n",
      "weekday_Monday: -218.51820386527157\n",
      "weekday_Saturday: 131.48113545036645\n",
      "weekday_Sunday: 9.65032277120526\n",
      "weekday_Thursday: -128.87791485204332\n",
      "weekday_Tuesday: -225.7357845381828\n",
      "weekday_Wednesday: -183.19924502379024\n",
      "r_squared val: 0.6119494369802788\n"
     ]
    }
   ],
   "source": [
    "#from sklearn.cross_validation import KFold\n",
    "\n",
    "\n",
    "\n",
    "# Good time to do a baseline r_squared calculation\n",
    "crossvalidation = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "baseline = np.mean(cross_val_score(reg, X, y, scoring='r2', cv=crossvalidation))\n",
    "\n",
    "# Do the actual regression\n",
    "coefficients = dict(zip(X.columns,list(reg.coef_)))\n",
    "for var in coefficients:\n",
    "    print('{:>12}: {}'.format(var, coefficients[var]))\n",
    "\n",
    "print('r_squared val:', baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need more information about the intersection other than just names.\n",
    "I need descriptive info, but it isn't available in violations table.  Crash data does have some stuff though"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model\n",
    "Would like to see what success we could have with a tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['violations', 'year', 'month', 'day', 'speed', 'quantile_speed',\n",
       "       'min_speed', 'avg_congestion', 'morning_rush_quant',\n",
       "       'morning_rush_congestion', 'afternoon_rush_speed',\n",
       "       'afternoon_rush_quant', 'afternoon_rush_congestion', 'weekday',\n",
       "       'precip', 'temp', 'cases_total', 'morning_rush_speed', 'holiday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = int_df['violations']\n",
    "X = int_df.drop(columns=['violations', 'day', 'speed', 'quantile_speed', 'afternoon_rush_speed',\n",
    "                        'morning_rush_speed'])\n",
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47558527104301995"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor() \n",
    "tree_reg.fit(X_train, y_train)\n",
    "tree_reg.score(X_test, y_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7488313759106089\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier, RandomForestRegressor\n",
    "\n",
    "forest = RandomForestRegressor(bootstrap=True, max_depth=20,\n",
    "                      max_features=15, max_leaf_nodes=None, criterion='mse', \n",
    "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                      min_samples_leaf=1, min_samples_split=2,\n",
    "                      min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
    "                      n_jobs=-1, oob_score=False, random_state=0,\n",
    "                      verbose=0, warm_start=False)\n",
    "\n",
    "forest.fit(X_train, y_train)\n",
    "print(forest.score(X_test, y_test))  # quick r2 score for tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1182.000000\n",
      "mean     1490.786802\n",
      "std       329.186782\n",
      "min       546.000000\n",
      "25%      1243.500000\n",
      "50%      1465.500000\n",
      "75%      1714.750000\n",
      "max      3173.000000\n",
      "Name: violations, dtype: float64\n",
      "\n",
      "Cross validation score: 0.74\n",
      "MAE: 123.22\n",
      "RMSE: 160.78\n",
      "R2 (test set): 0.75\n"
     ]
    }
   ],
   "source": [
    "print(y.describe())\n",
    "print()\n",
    "print('Cross validation score: {:.2f}'.format(cross_val_score(forest, X_train, y_train, cv=5).mean())) # 2min\n",
    "print('MAE: {:.2f}'.format(mean_absolute_error(forest.predict(X_test), y_test)))\n",
    "print('RMSE: {:.2f}'.format(np.sqrt(mean_squared_error(forest.predict(X_test), y_test))))\n",
    "print('R2 (test set): {:.2f}'.format(r2_score(y_test, forest.predict(X_test))))  # should match self scoring\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result\n",
    "\n",
    "Linear model looked at around 50k intersection related crashes at traffic signals.  Data is aggragated for each day. \n",
    "\n",
    "A linear regression was chosen, so we could better make sense of the results and see the affect of each variable.\n",
    "\n",
    "With violations as the target, the model is vastly improved over the more noisy crash data.\n",
    "\n",
    "Around 66% of the crashes could be explained by a model with only three variables.\n",
    "\n",
    "##### How my variables affect violations:\n",
    "- For every degree Celsius of temp change, expect an additional 19 violations.\n",
    "- For weekends (Sat/Sun) expect about up to 200 additional accidents citywide over a typical weekday.\n",
    "- For every 1/4\" of precipitation, we can expect an 30 fewer violations citywide.\n",
    "- Surprisingly, congestion has almost no affect, and is not included in this model\n",
    "- Expect 15 fewer violations on holidays\n",
    "\n",
    "\n",
    "###### Random Forest\n",
    "I also got good results from random forest (r2 of 0.75), but prefer the interpretability of linear regression.  Should a predictive model be necessary, this one works well."
   ]
  }
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