Module 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='table_of_contents'></a>\n",
    "\n",
    "0. [Import libraries](#imports)\n",
    "1. [Import data](#import_data)\n",
    "2. [Data exploration](#data_exploration)\n",
    "3. [Data cleaning](#data_cleaning)\n",
    "4. [Data preparation](#data_preparation)\n",
    "5. [Benchmark model](#benchmark_model)<br>\n",
    "    5.1. [Train-test split](#train_test_split)<br>\n",
    "    5.2. [Fit a model to the data](#fit_model)<br>\n",
    "    5.3. [Model evaluation](#model_evaluation)<br>\n",
    "    5.4. [Fix problems](#fix_problems)\n",
    "6. [Feature engineering](#feature_engineering)\n",
    "7. [Model training](#model_training)<br>\n",
    "    7.1. [Decision Tree](#decision_tree)<br>\n",
    "    7.2. [Random Forest](#random_forest) -\n",
    "      [Overfit](#overfit) - \n",
    "      [Underfit](#underfit)<br>\n",
    "    7.3. [Gradient boosting](#gradient_boosting)<br>\n",
    "    7.4. [Comparing algorithm performances](#comparing_algorithm_performances)\n",
    "9. [Tuning](#tuning)<br>\n",
    "    8.1. [Find best parameters](#find_best_parameters)<br>\n",
    "    Bonus: [Classification](#classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Import libraries <a id='imports'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import data <a id='import_data'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_jan_2019 = pd.read_csv('data/yellow_tripdata_2019-01.csv')\n",
    "taxi_data = pd.concat([taxi_jan_2019])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7667792, 18)\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.95</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>55.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "0         1  2019-01-01 00:46:40   2019-01-01 00:53:20                1   \n",
       "1         1  2019-01-01 00:59:47   2019-01-01 01:18:59                1   \n",
       "2         2  2018-12-21 13:48:30   2018-12-21 13:52:40                3   \n",
       "3         2  2018-11-28 15:52:25   2018-11-28 15:55:45                5   \n",
       "4         2  2018-11-28 15:56:57   2018-11-28 15:58:33                5   \n",
       "\n",
       "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
       "0            1.5           1                  N           151           239   \n",
       "1            2.6           1                  N           239           246   \n",
       "2            0.0           1                  N           236           236   \n",
       "3            0.0           1                  N           193           193   \n",
       "4            0.0           2                  N           193           193   \n",
       "\n",
       "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
       "0             1          7.0    0.5      0.5        1.65           0.0   \n",
       "1             1         14.0    0.5      0.5        1.00           0.0   \n",
       "2             1          4.5    0.5      0.5        0.00           0.0   \n",
       "3             2          3.5    0.5      0.5        0.00           0.0   \n",
       "4             2         52.0    0.0      0.5        0.00           0.0   \n",
       "\n",
       "   improvement_surcharge  total_amount  congestion_surcharge  \n",
       "0                    0.3          9.95                   NaN  \n",
       "1                    0.3         16.30                   NaN  \n",
       "2                    0.3          5.80                   NaN  \n",
       "3                    0.3          7.55                   NaN  \n",
       "4                    0.3         55.55                   NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(taxi_data.shape)\n",
    "taxi_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data exploration <a id='data_exploration'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VendorID', 'tpep_pickup_datetime', 'tpep_dropoff_datetime',\n",
       "       'passenger_count', 'trip_distance', 'RatecodeID', 'store_and_fwd_flag',\n",
       "       'PULocationID', 'DOLocationID', 'payment_type', 'fare_amount', 'extra',\n",
       "       'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge',\n",
       "       'total_amount', 'congestion_surcharge'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data = taxi_data[['tpep_pickup_datetime', 'tpep_dropoff_datetime',\n",
    "       'passenger_count', 'trip_distance', 'RatecodeID','PULocationID',\n",
    "       'DOLocationID', 'payment_type',  'total_amount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0  2019-01-01 00:46:40   2019-01-01 00:53:20                1            1.5   \n",
       "1  2019-01-01 00:59:47   2019-01-01 01:18:59                1            2.6   \n",
       "2  2018-12-21 13:48:30   2018-12-21 13:52:40                3            0.0   \n",
       "3  2018-11-28 15:52:25   2018-11-28 15:55:45                5            0.0   \n",
       "4  2018-11-28 15:56:57   2018-11-28 15:58:33                5            0.0   \n",
       "\n",
       "   RatecodeID  PULocationID  DOLocationID  payment_type  total_amount  \n",
       "0           1           151           239             1          9.95  \n",
       "1           1           239           246             1         16.30  \n",
       "2           1           236           236             1          5.80  \n",
       "3           1           193           193             2          7.55  \n",
       "4           2           193           193             2         55.55  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb60f3d6c70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb60ef2e490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb60ef5abb0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fb60f023b80>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb5f6199490>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fb60ef1a790>,\n",
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       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb4ec4d5d90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data.hist(figsize=(20,10), bins=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     7430139\n",
       "2      166090\n",
       "5       54569\n",
       "3       11801\n",
       "4        4895\n",
       "99        252\n",
       "6          46\n",
       "Name: RatecodeID, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data['RatecodeID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4ec52c430>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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twNKq2ldVnwLePJN+SZYm2QJsoxe47gKOq6on2rhPAK9qm68BHu3rPtba1rTH+7dP6lNVe4EfAsdMM9b+9W1MMppkdPv27TP5SJIkSQvaTAPe/05yGLAlydVJfhk4YiYdWyA8HVhLbzbuJ6fZPFMNMU37bPv013dtVY1U1cjq1aunKU2SJOnQMNOA98627S8CPwJOAC56KW9UVT8A/pTeYdIn22FX2v22ttlYG3vCWuDx1r52ivZJfZIsA14B7JhmLEmSpE6b6Sra71XVc1X1dFV9oKp+pR2yBSDJLVP1S7I6ySvb45XAPwS+CdwGTKxq3QDc2h7fBqxvK2NPpLeY4u52GPeZttgjwLv26zMx1tuAO9t5ercD5yRZ1RZXnNPaJEmSOm2m18E7kNe+SPvxwA1tJewSYFNV/WGSrwKbklwKPAJcDFBVDyTZBDwI7AXeU1X72ljvBq6nt8jji+0GcB3w6SRb6c3crW9j7UjyIeCett0Hq2rHHH1eSZKkBSu9ya6DHCT5WlX91BzUM1QjIyM1Ojo67DIkSZIOKMm9VTUy1WszXkUrSZKkQ8NcBbypVqxKkiRpCOYq4F0+R+NIkiTpIE27yCLJ/Uxx7Th6M3ZVVafRe/ClAdQmSZKkWTjQKtqfm5cqJEmSNGemDXhV9b35KkSSJElzY0bn4LULDN+TZFeS55PsS/L0oIuTJEnSSzfTRRb/GbgEeJjehYb/JfDbgypKkiRJszfjb7Koqq1JlrZvlvhUkr8YYF2SJEmapZkGvP+d5DBgS5KrgSeAIwZXliRJkmZrpodo39m2/UXgR8AJwM8PqihJkiTN3kwD3oVV9VxVPV1VH6iqX8FLqEiSJC1IMw14G6Zo++dzWIckSZLmyIG+yeIS4J8CJya5re+lo4CnBlmYJEmSZudAiyz+gt6CimOB3+hrfwa4b1BFSZIkafZm8k0W3wPelOQ44KfbSw9V1d5BFydJkqSXbqbfZHExcDdwMfB24K4kbxtkYZIkSZqdmV4H798BP11V2wCSrAb+GLh5UIVJkiRpdma6inbJRLhrnnoJfSVJkjSPZjqD98UktwOfbc//X+ALgylJkiRJB2Oms3AF/BfgNOB1wLUDq0iSJEkHZaYzeP+oqi4Hfn+iIckHgMsHUpUkSZJm7UAXOn438G+A1ybpv+7dy4GvDLIwSZIkzc6BZvB+D/gi8B+B9/e1P1NVOwZWlSRJkmbtQBc6/iHwQ+CS+SlHkiRJB8tLnUiSJHWMAU+SJKljDHiSJEkdY8CTJEnqGAOeJElSxww04CU5IcmfJHkoyQNJfqm1H51kc5KH2/2qvj5XJNma5FtJzu1rf0OS+9tr1yRJa1+R5HOt/a4k6/r6bGjv8XCSDYP8rJIkSQvFoGfw9gK/WlX/N3Am8J4kp9C7pt4dVXUycEd7TnttPXAqcB7wsSRL21gfBzYCJ7fbea39UmBnVZ0EfAS4qo11NHAl8EbgDODK/iApSZLUVQMNeFX1RFV9rT1+BngIWANcANzQNrsBuLA9vgC4qap2V9V3gK3AGUmOB46qqq9WVQE37tdnYqybgbPb7N65wOaq2lFVO4HNvBAKJUmSOmvezsFrh05fD9wFHFdVT0AvBAKvaputAR7t6zbW2ta0x/u3T+pTVXvpXZj5mGnGkiRJ6rR5CXhJjgRuAd5XVU9Pt+kUbTVN+2z79Ne2McloktHt27dPU5okSdKhYeABL8lyeuHuM1X1+635yXbYlXa/rbWPASf0dV8LPN7a107RPqlPkmXAK4Ad04w1SVVdW1UjVTWyevXq2X5MSZKkBWPQq2gDXAc8VFW/2ffSbcDEqtYNwK197evbytgT6S2muLsdxn0myZltzHft12dirLcBd7bz9G4Hzkmyqi2uOKe1SZIkddqyAY9/FvBO4P4kW1rbrwMfBjYluRR4BLgYoKoeSLIJeJDeCtz3VNW+1u/dwPXASuCL7Qa9APnpJFvpzdytb2PtSPIh4J623QerasegPqgkSdJCkd5klwBGRkZqdHR02GVIkiQdUJJ7q2pkqtf8JgtJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHWPAkyRJ6hgDniRJUscY8CRJkjrGgCdJktQxBjxJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHWPAkyRJ6hgDniRJUscY8CRJkjrGgCdJktQxBjxJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHWPAkyRJ6hgDniRJUscY8CRJkjrGgCdJktQxBjxJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHWPAkyRJ6hgDniRJUscY8CRJkjpmoAEvySeTbEvyjb62o5NsTvJwu1/V99oVSbYm+VaSc/va35Dk/vbaNUnS2lck+VxrvyvJur4+G9p7PJxkwyA/pyRJ0kIy6Bm864Hz9mt7P3BHVZ0M3NGek+QUYD1wauvzsSRLW5+PAxuBk9ttYsxLgZ1VdRLwEeCqNtbRwJXAG4EzgCv7g6QkSVKXDTTgVdWXgR37NV8A3NAe3wBc2Nd+U1XtrqrvAFuBM5IcDxxVVV+tqgJu3K/PxFg3A2e32b1zgc1VtaOqdgKb+dtBU5IkqZOGcQ7ecVX1BEC7f1VrXwM82rfdWGtb0x7v3z6pT1XtBX4IHDPNWJIkSZ23kBZZZIq2mqZ9tn0mv2myMcloktHt27fPqFBJkqSFbBgB78l22JV2v621jwEn9G23Fni8ta+don1SnyTLgFfQOyT8YmP9LVV1bVWNVNXI6tWrD+JjSZIkLQzDCHi3AROrWjcAt/a1r28rY0+kt5ji7nYY95kkZ7bz6961X5+Jsd4G3NnO07sdOCfJqra44pzWJkmS1HnLBjl4ks8CbwaOTTJGb2Xrh4FNSS4FHgEuBqiqB5JsAh4E9gLvqap9bah301uRuxL4YrsBXAd8OslWejN369tYO5J8CLinbffBqtp/sYckSVInpTfhJYCRkZEaHR0ddhmSJEkHlOTeqhqZ6rWFtMhCkiRJc8CAJ0mS1DEGPEmSpI4x4EmSJHWMAU+SJKljDHiSJEkdY8CTJEnqGAOeJElSxxjwJEmSOsaAJ0mS1DEGPEmSpI4x4EmSJHWMAU+SJKljDHiSJEkdY8CTJEnqGAOeJElSxxjwJEmSOsaAJx0intq1m68/+gOe2rV72KVIkha4ZcMuQFrontq1m7Gdz7J21UqOOXLFvPcHuHXLY1x+y30sX7KEPePjXH3RaZx/+ppZjSVJ6j4Dnl7UVMFkLsLKoeRgg9VcBLOndu3m8lvu47k94zzHOACX3XIfZ5107NACpyRpYTPgaUpTBZOCRTWLdLDBaq6C2djOZ1m+ZMnfjAGwfMkSxnY++5IDmjOBkrQ4eA7eIW4Q52X1B5Nndu/luT3j/Nubv85lN09uu+yW+w6Z88Fms58mglW/iWA1H/0nrF21kj3j45Pa9oyPs3bVypc0zlT/XQ+l/4aSpJlzBu8QNqjZmKlmjJZmCWTydrOdRZpvs91PBxus5iqYHXPkCq6+6DQu2+8zvNT9PpczgQuFh5slaWoGvEPUXJ+X1W+qYLKvxqEmJ7zZhJX5MvGL/4jDls56Px1ssJqrYAZw/ulrOOukYw8qzMxV4Bym/kD351u/7+FmSXoRBrxDyNYnn2HLoz/g9BNeyY+e3zew2ZgXCybAnISVQbt1y2NcdvPXWZol7Bnfx9IXOUw6k9oPNljNRTCbcMyRKw66/1wFzmHon4l9ft8+xgv27Ks5/wNHkrrAgHeI+Pd/cD83/uUjf/P87SNrBjob82LBZK7CyqA8tWs3v7ppC3vHAfYBsGffwe2nuQhWC2VfzWXgnE9TzVjv71A/3CxJc8mAdwjY+uQzk8IdwKbRx/j1t/4Ev/nH/3NgszFTBZOFFFam8sDjT7dwN9lhS5ewYtmhN2s1CAv9v+FUxnY+S43XtNs8t3ffIXW4WZIGyYB3CNjy6A+mbD/6iMP4yuVvOeRmYwZr6hDwm28/jROOPsL9dIg64rCl7N43fcCrmv51SVpMDHiHgNNPeOWLth+KszGDdOqrX8HypWFPXxhYvjS86cc8N2sY5mqV64+e38fhy5fw3J6pD88CrFy+zEO0ktR4HbxDwEnHvZx3vek1k9re9abXcNJxL39J4yyG7zI95sgV/MbFr2PFsiW87LClrFi2hN+4+HX+0h+CW7c8xllX3ck7PnEXZ111J7dteWzWY83k0OuhtiJYkgYpHtZ4wcjISI2Ojg67jBfVv4r2pYa7xfYNBl4fbbie2rWbs666c9KM2+HLl/CVy98y6/8et215bNIK4LePrGXT6Nii+Tc9G/5/oIXKf5tzI8m9VTUy5WtdD3hJzgM+CiwFPlFVH36xbecj4K17/38f6PiSJGnh+O6Hf3ZgY08X8Dp9iDbJUuB3gLcCpwCXJDllWPUY7iRJWlyG9bu/0wEPOAPYWlXfrqrngZuAC4ZRiOFOkqTFaRgZoOsBbw3waN/zsdb2N5JsTDKaZHT79u3zWpwkSdIgdD3gZYq2SScdVtW1VTVSVSOrV6+ep7IkSZIGp+sBbww4oe/5WuDxYRQyyJMsJUnSwjWMDND1gHcPcHKSE5McBqwHbhtWMYY8SZIWl2H97u/0N1lU1d4kvwjcTu8yKZ+sqgeGWZMhT5IkDVqnAx5AVX0B+MKw65AkSZovXT9EK0mStOgY8CRJkjrGgCdJktQxBjxJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHZOqOvBWi0SS7cD35uGtjgW+Pw/vs9C5H3rcDz3uhx73wwvcFz3uhx73Q0//fvg7VbV6qo0MeEOQZLSqRoZdx7C5H3rcDz3uhx73wwvcFz3uhx73Q89M94OHaCVJkjrGgCdJktQxBrzhuHbYBSwQ7oce90OP+6HH/fAC90WP+6HH/dAzo/3gOXiSJEkd4wyeJElSxxjw5lGS85J8K8nWJO8fdj3DkuSTSbYl+cawaxmmJCck+ZMkDyV5IMkvDbumYUhyeJK7k3y97YcPDLumYUqyNMlfJfnDYdcyLEm+m+T+JFuSjA67nmFJ8sokNyf5Zvs58aZh1zTfkvx4+3cwcXs6yfuGXdcwJPnl9jPyG0k+m+Twabf3EO38SLIU+J/APwLGgHuAS6rqwaEWNgRJfgbYBdxYVT857HqGJcnxwPFV9bUkLwfuBS5cbP8mkgQ4oqp2JVkO/DnwS1X1l0MubSiS/AowAhxVVT837HqGIcl3gZGqWtTXPEtyA/BnVfWJJIcBL6uqHwy7rmFpv0cfA95YVfNxzdoFI8kaej8bT6mqZ5NsAr5QVde/WB9n8ObPGcDWqvp2VT0P3ARcMOSahqKqvgzsGHYdw1ZVT1TV19rjZ4CHgDXDrWr+Vc+u9nR5uy3KvzyTrAV+FvjEsGvRcCU5CvgZ4DqAqnp+MYe75mzgfy22cNdnGbAyyTLgZcDj021swJs/a4BH+56PsQh/mWtqSdYBrwfuGm4lw9EOS24BtgGbq2pR7gfgt4DLgPFhFzJkBXwpyb1JNg67mCF5LbAd+FQ7ZP+JJEcMu6ghWw98dthFDENVPQb8J+AR4Angh1X1pen6GPDmT6ZoW5SzFJosyZHALcD7qurpYdczDFW1r6pOB9YCZyRZdIfuk/wcsK2q7h12LQvAWVX1U8Bbgfe00zoWm2XATwEfr6rXAz8CFvO524cB5wP/bdi1DEOSVfSO+p0IvBo4Isk7putjwJs/Y8AJfc/XcoDpVXVfO+fsFuAzVfX7w65n2NohqD8FzhtyKcNwFnB+O//sJuAtSX53uCUNR1U93u63AZ+nd4rLYjMGjPXNZt9ML/AtVm8FvlZVTw67kCH5h8B3qmp7Ve0Bfh/4+9N1MODNn3uAk5Oc2P4SWQ/cNuSaNERtccF1wENV9ZvDrmdYkqxO8sr2eCW9H2TfHG5V86+qrqiqtVW1jt7Phzuratq/0LsoyRFt0RHtkOQ5wKJbcV9Vfw08muTHW9PZwKJagLWfS1ikh2ebR4Azk7ys/e44m9552y9q2byUJapqb5JfBG4HlgKfrKoHhlzWUCT5LPBm4NgkY8CVVXXdcKsairOAdwL3t/PPAH69qr4wxJqG4XjghrZCbgmwqaoW7SVCxHHA53u/w1gG/CaqU0oAAALnSURBVF5V/dFwSxqa9wKfaZMC3wZ+Ycj1DEWSl9G7AsW/HnYtw1JVdyW5GfgasBf4Kw7wjRZeJkWSJKljPEQrSZLUMQY8SZKkjjHgSZIkdYwBT5IkqWMMeJIkSfMsySeTbEsyo8sAJXl7kgeTPJDk9w60vQFPkl6iJH/xErd/cxIv/SKp3/XM8KLuSU4GrqD3LS+nAu87UB8DniS9RFU17RXkJelAqurLwI7+tiQ/luSP2vcw/1mSn2gv/Svgd6pqZ+u77UDjG/Ak6SVKsqvdvznJnya5Ock3k3ymXWWeJOe1tj8Hfr6v7xHt0Mw97UvkL2jt1yT59+3xuUm+nMSf0dLici3w3qp6A/BrwMda+98F/m6SryT5yyQHnPnzmywk6eC8HjiV3ndLfwU4K8ko8F+BtwBbgc/1bf//0fsasn/RvqLt7iR/TO+L5O9J8mfANcA/rqrxefwckoYoyZH0vl/2v7W/EwFWtPtlwMn0vgVqLfBnSX6yfX/3lAx4knRw7q6qMYD2lXPrgF30vhj84db+u8DGtv05wPlJfq09Pxx4TVU9lORfAV8Gfrmq/tc8fgZJw7cE+EFVnT7Fa2PAX1bVHuA7Sb5FL/DdM91gkqTZ2933eB8v/OH8Yt8DGeCiqjq93V5TVRNfGv73gKeAVw+mVEkLVVU9TS+8XQyQnte1l/8A+Aet/Vh6h2y/Pd14BjxJmnvfBE5M8mPt+SV9r90OvLfvXL3Xt/u/A/wqvUO+b03yxnmsV9I8S/JZ4KvAjycZS3Ip8M+AS5N8HXgAuKBtfjvwVJIHgT8B/m1VPTXd+B6ilaQ5VlXPJdkI/Pck3wf+HPjJ9vKHgN8C7msh77tJ/glwHfBrVfV4+0F/fZKfrqrnhvEZJA1WVV3yIi/9rQUUVVXAr7TbjKTXR5IkSV3hIVpJkqSOMeBJkiR1jAFPkiSpYwx4kiRJHWPAkyRJ6hgDniRJUscY8CRJkjrGgCdJktQx/wcX1nbE0KW/gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data.reset_index().plot(kind='scatter', y='total_amount', x='index', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4edbad640>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']<1000].reset_index().plot(kind='scatter', y='total_amount', x='index', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two things to deal with: negative values and very high values. Let's first look at negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7131, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4d983ceb0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(taxi_data[taxi_data['total_amount']<0].shape)\n",
    "taxi_data[taxi_data['total_amount']<0].reset_index().plot(kind='scatter', y='total_amount', x='index', figsize=(10,5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2019-01-01 00:32:56</td>\n",
       "      <td>2019-01-01 00:33:35</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402</th>\n",
       "      <td>2019-01-01 00:14:18</td>\n",
       "      <td>2019-01-01 00:40:55</td>\n",
       "      <td>1</td>\n",
       "      <td>4.13</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>238</td>\n",
       "      <td>4</td>\n",
       "      <td>-20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>2019-01-01 00:31:27</td>\n",
       "      <td>2019-01-01 00:42:39</td>\n",
       "      <td>1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>234</td>\n",
       "      <td>4</td>\n",
       "      <td>-9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>2019-01-01 00:45:57</td>\n",
       "      <td>2019-01-01 00:46:07</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>234</td>\n",
       "      <td>234</td>\n",
       "      <td>4</td>\n",
       "      <td>-3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>2019-01-01 00:48:35</td>\n",
       "      <td>2019-01-01 00:49:59</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>234</td>\n",
       "      <td>234</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "663   2019-01-01 00:32:56   2019-01-01 00:33:35                2   \n",
       "2402  2019-01-01 00:14:18   2019-01-01 00:40:55                1   \n",
       "2541  2019-01-01 00:31:27   2019-01-01 00:42:39                1   \n",
       "2544  2019-01-01 00:45:57   2019-01-01 00:46:07                1   \n",
       "2547  2019-01-01 00:48:35   2019-01-01 00:49:59                1   \n",
       "\n",
       "      trip_distance  RatecodeID  PULocationID  DOLocationID  payment_type  \\\n",
       "663            0.10           1           148           148             3   \n",
       "2402           4.13           1           170           238             4   \n",
       "2541           1.35           1           162           234             4   \n",
       "2544           0.00           1           234           234             4   \n",
       "2547           0.16           1           234           234             4   \n",
       "\n",
       "      total_amount  \n",
       "663           -3.8  \n",
       "2402         -20.3  \n",
       "2541          -9.8  \n",
       "2544          -3.8  \n",
       "2547          -4.3  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']<0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    4088\n",
       "4    2666\n",
       "2     376\n",
       "1       1\n",
       "Name: payment_type, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']<0]['payment_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bc600700>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']<0]['trip_distance'].hist(bins=60, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1414, 9)\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>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>2019-01-01 00:18:46</td>\n",
       "      <td>2019-01-01 00:18:46</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>186</td>\n",
       "      <td>264</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9138</th>\n",
       "      <td>2019-01-01 00:41:47</td>\n",
       "      <td>2019-01-01 00:41:47</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>246</td>\n",
       "      <td>264</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13256</th>\n",
       "      <td>2019-01-01 00:19:38</td>\n",
       "      <td>2019-01-01 00:20:35</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13257</th>\n",
       "      <td>2019-01-01 00:43:34</td>\n",
       "      <td>2019-01-01 00:43:34</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13258</th>\n",
       "      <td>2019-01-01 00:02:52</td>\n",
       "      <td>2019-01-01 00:02:52</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
       "3552   2019-01-01 00:18:46   2019-01-01 00:18:46                1   \n",
       "9138   2019-01-01 00:41:47   2019-01-01 00:41:47                1   \n",
       "13256  2019-01-01 00:19:38   2019-01-01 00:20:35                1   \n",
       "13257  2019-01-01 00:43:34   2019-01-01 00:43:34                1   \n",
       "13258  2019-01-01 00:02:52   2019-01-01 00:02:52                1   \n",
       "\n",
       "       trip_distance  RatecodeID  PULocationID  DOLocationID  payment_type  \\\n",
       "3552             0.0           5           186           264             2   \n",
       "9138             0.0           5           246           264             2   \n",
       "13256            0.0           1           193           193             1   \n",
       "13257            0.0           1             7             7             2   \n",
       "13258            0.0           1           264             7             2   \n",
       "\n",
       "       total_amount  \n",
       "3552            0.0  \n",
       "9138            0.0  \n",
       "13256           0.0  \n",
       "13257           0.0  \n",
       "13258           0.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(taxi_data[taxi_data['total_amount']==0].shape)\n",
    "taxi_data[taxi_data['total_amount']==0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    815\n",
       "2    569\n",
       "3     29\n",
       "4      1\n",
       "Name: payment_type, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']==0]['payment_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4ec80cbe0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']==0]['trip_distance'].hist(bins=60, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    1270\n",
       "1.00       4\n",
       "0.01       4\n",
       "0.02       4\n",
       "0.03       3\n",
       "        ... \n",
       "0.59       1\n",
       "0.18       1\n",
       "5.70       1\n",
       "0.87       1\n",
       "1.04       1\n",
       "Name: trip_distance, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']==0]['trip_distance'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can safely get rid of the negative values. What about the very high values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bd2faa30>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data.reset_index().plot(kind='scatter', y='total_amount', x='index', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1166, 9)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data[taxi_data['total_amount']>200].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.68222215901336"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data['total_amount'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can agree to cut from $200 level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data cleaning <a id='data_cleaning'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_filtered = taxi_data[(taxi_data['total_amount']>=0)&(taxi_data['total_amount']<200)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7667792, 9)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7659490, 9)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(taxi_data.shape)\n",
    "taxi_data_filtered.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tpep_pickup_datetime     0\n",
       "tpep_dropoff_datetime    0\n",
       "passenger_count          0\n",
       "trip_distance            0\n",
       "RatecodeID               0\n",
       "PULocationID             0\n",
       "DOLocationID             0\n",
       "payment_type             0\n",
       "total_amount             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data_filtered.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data preparation <a id='data_preparation'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_prepared = taxi_data_filtered.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making sure everything is in the right type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tpep_pickup_datetime      object\n",
       "tpep_dropoff_datetime     object\n",
       "passenger_count            int64\n",
       "trip_distance            float64\n",
       "RatecodeID                 int64\n",
       "PULocationID               int64\n",
       "DOLocationID               int64\n",
       "payment_type               int64\n",
       "total_amount             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data_prepared.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_prepared.loc[:,'tpep_pickup_datetime'] = pd.to_datetime(taxi_data_prepared.loc[:,'tpep_pickup_datetime'])\n",
    "taxi_data_prepared.loc[:,'tpep_dropoff_datetime'] = pd.to_datetime(taxi_data_prepared.loc[:,'tpep_dropoff_datetime'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_prepared.loc[:,'RatecodeID']   = taxi_data_prepared['RatecodeID'].astype(str)\n",
    "taxi_data_prepared.loc[:,'PULocationID'] = taxi_data_prepared['PULocationID'].astype(str)\n",
    "taxi_data_prepared.loc[:,'DOLocationID'] = taxi_data_prepared['DOLocationID'].astype(str)\n",
    "taxi_data_prepared.loc[:,'payment_type'] = taxi_data_prepared['payment_type'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tpep_pickup_datetime     datetime64[ns]\n",
       "tpep_dropoff_datetime    datetime64[ns]\n",
       "passenger_count                   int64\n",
       "trip_distance                   float64\n",
       "RatecodeID                       object\n",
       "PULocationID                     object\n",
       "DOLocationID                     object\n",
       "payment_type                     object\n",
       "total_amount                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data_prepared.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming variables into the formats we need them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>55.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0  2019-01-01 00:46:40   2019-01-01 00:53:20                1            1.5   \n",
       "1  2019-01-01 00:59:47   2019-01-01 01:18:59                1            2.6   \n",
       "2  2018-12-21 13:48:30   2018-12-21 13:52:40                3            0.0   \n",
       "3  2018-11-28 15:52:25   2018-11-28 15:55:45                5            0.0   \n",
       "4  2018-11-28 15:56:57   2018-11-28 15:58:33                5            0.0   \n",
       "\n",
       "  RatecodeID PULocationID DOLocationID payment_type  total_amount  \n",
       "0          1          151          239            1          9.95  \n",
       "1          1          239          246            1         16.30  \n",
       "2          1          236          236            1          5.80  \n",
       "3          1          193          193            2          7.55  \n",
       "4          2          193          193            2         55.55  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_prepared['transaction_date'] = pd.to_datetime(taxi_data_prepared['tpep_pickup_datetime'].dt.date)\n",
    "# -> we make it datetime again because it's very little use when it's just a string (can't compare, sort, etc.)\n",
    "taxi_data_prepared['transaction_year'] = taxi_data_prepared['tpep_pickup_datetime'].dt.year\n",
    "taxi_data_prepared['transaction_month'] = taxi_data_prepared['tpep_pickup_datetime'].dt.month\n",
    "taxi_data_prepared['transaction_day'] =  taxi_data_prepared['tpep_pickup_datetime'].dt.day\n",
    "taxi_data_prepared['transaction_hour'] = taxi_data_prepared['tpep_pickup_datetime'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_year</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:46:40</td>\n",
       "      <td>2019-01-01 00:53:20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>9.95</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:59:47</td>\n",
       "      <td>2019-01-01 01:18:59</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>239</td>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>16.30</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-12-21 13:48:30</td>\n",
       "      <td>2018-12-21 13:52:40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>5.80</td>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-28 15:52:25</td>\n",
       "      <td>2018-11-28 15:55:45</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>7.55</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-28 15:56:57</td>\n",
       "      <td>2018-11-28 15:58:33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "      <td>55.55</td>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0  2019-01-01 00:46:40   2019-01-01 00:53:20                1            1.5   \n",
       "1  2019-01-01 00:59:47   2019-01-01 01:18:59                1            2.6   \n",
       "2  2018-12-21 13:48:30   2018-12-21 13:52:40                3            0.0   \n",
       "3  2018-11-28 15:52:25   2018-11-28 15:55:45                5            0.0   \n",
       "4  2018-11-28 15:56:57   2018-11-28 15:58:33                5            0.0   \n",
       "\n",
       "  RatecodeID PULocationID DOLocationID payment_type  total_amount  \\\n",
       "0          1          151          239            1          9.95   \n",
       "1          1          239          246            1         16.30   \n",
       "2          1          236          236            1          5.80   \n",
       "3          1          193          193            2          7.55   \n",
       "4          2          193          193            2         55.55   \n",
       "\n",
       "  transaction_date  transaction_year  transaction_month  transaction_day  \\\n",
       "0       2019-01-01              2019                  1                1   \n",
       "1       2019-01-01              2019                  1                1   \n",
       "2       2018-12-21              2018                 12               21   \n",
       "3       2018-11-28              2018                 11               28   \n",
       "4       2018-11-28              2018                 11               28   \n",
       "\n",
       "   transaction_hour  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                13  \n",
       "3                15  \n",
       "4                15  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_data_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb4bc589580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb5ff9528e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb615368d60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fb6151e01f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb61518f640>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb6151bb9d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fb6151bbac0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb6151213a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb6151408b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_data_prepared.hist(figsize=(20,10), bins=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "taxi_data_prepared = taxi_data_prepared[taxi_data_prepared['transaction_year']==2019]\n",
    "taxi_data_prepared = taxi_data_prepared[taxi_data_prepared['transaction_month']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noting down categorical and numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['PULocationID','transaction_date','transaction_month','transaction_day','transaction_hour']\n",
    "numerical_columns = ['trip_distance','total_amount']\n",
    "all_needed_columns = categorical_columns + numerical_columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7658954, 7)\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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>16.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>163</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>229</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>141</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0          151       2019-01-01                  1                1   \n",
       "1          239       2019-01-01                  1                1   \n",
       "7          163       2019-01-01                  1                1   \n",
       "8          229       2019-01-01                  1                1   \n",
       "9          141       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  \n",
       "0                 0            1.5          9.95  \n",
       "1                 0            2.6         16.30  \n",
       "7                 0            1.3          9.05  \n",
       "8                 0            3.7         18.50  \n",
       "9                 0            2.1         13.00  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_taxi_df = taxi_data_prepared[all_needed_columns]\n",
    "print(main_taxi_df.shape)\n",
    "main_taxi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregate data points <br>\n",
    "Now is a good time to think about what we want to predict. Depending on this, we need to transform our data to have a certain format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102556, 8)\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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \n",
       "0                 2            0.0        21.800                      1  \n",
       "1                 5            0.0        87.300                      1  \n",
       "2                 6            0.0        80.300                      1  \n",
       "3                 8            0.0       128.580                      2  \n",
       "4                10           16.9        43.245                      4  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_grouped_by_region = main_taxi_df.groupby(categorical_columns).mean().reset_index()\n",
    "taxi_grouped_by_region['count_of_transactions'] = main_taxi_df.groupby(categorical_columns).count().reset_index()['total_amount']\n",
    "print(taxi_grouped_by_region.shape)\n",
    "taxi_grouped_by_region.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bd106cd0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_grouped_by_region['trip_distance'].hist(bins=100, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4b74fa2b0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_grouped_by_region['total_amount'].hist(bins=100, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Benchmark model <a id='benchmark_model'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_for_benchmark_model = taxi_grouped_by_region.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.90</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102551</th>\n",
       "      <td>98</td>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>16.66</td>\n",
       "      <td>58.060</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102552</th>\n",
       "      <td>99</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>1.66</td>\n",
       "      <td>9.800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102553</th>\n",
       "      <td>99</td>\n",
       "      <td>2019-01-26</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>0.42</td>\n",
       "      <td>6.000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102554</th>\n",
       "      <td>99</td>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>2.87</td>\n",
       "      <td>18.050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102555</th>\n",
       "      <td>99</td>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>31.60</td>\n",
       "      <td>99.320</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102556 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0                 1       2019-01-01                  1                1   \n",
       "1                 1       2019-01-01                  1                1   \n",
       "2                 1       2019-01-01                  1                1   \n",
       "3                 1       2019-01-01                  1                1   \n",
       "4                 1       2019-01-01                  1                1   \n",
       "...             ...              ...                ...              ...   \n",
       "102551           98       2019-01-31                  1               31   \n",
       "102552           99       2019-01-18                  1               18   \n",
       "102553           99       2019-01-26                  1               26   \n",
       "102554           99       2019-01-30                  1               30   \n",
       "102555           99       2019-01-30                  1               30   \n",
       "\n",
       "        transaction_hour  trip_distance  total_amount  count_of_transactions  \n",
       "0                      2           0.00        21.800                      1  \n",
       "1                      5           0.00        87.300                      1  \n",
       "2                      6           0.00        80.300                      1  \n",
       "3                      8           0.00       128.580                      2  \n",
       "4                     10          16.90        43.245                      4  \n",
       "...                  ...            ...           ...                    ...  \n",
       "102551                18          16.66        58.060                      1  \n",
       "102552                18           1.66         9.800                      1  \n",
       "102553                 8           0.42         6.000                      1  \n",
       "102554                 7           2.87        18.050                      1  \n",
       "102555                11          31.60        99.320                      1  \n",
       "\n",
       "[102556 rows x 8 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_benchmark_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features_benchmark = ['PULocationID','transaction_month','transaction_day','transaction_hour']\n",
    "input_features_benchmark  = categorical_features_benchmark + ['trip_distance']\n",
    "target_feature_benchmark  = 'total_amount'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1. Train-test split <a id='train_test_split'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_bench = data_for_benchmark_model[input_features_benchmark]\n",
    "y_bench = data_for_benchmark_model[target_feature_benchmark]\n",
    "\n",
    "# one-hot encode\n",
    "X_bench = pd.get_dummies(X_bench)\n",
    "\n",
    "X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(X_bench, y_bench, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2. Fit a model to the data <a id='fit_model'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "\n",
    "tree = DecisionTreeRegressor(max_depth=10)\n",
    "tree.fit(X_train_b, y_train_b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3. Model evaluation <a id='model_evaluation'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error 3.4313471447309563\n",
      "mean_squared_error 64.2961971730041\n",
      "root_mean_squared_error 8.018490953602436\n",
      "r2 0.7705552174199464\n"
     ]
    }
   ],
   "source": [
    "model_at_hand = tree\n",
    "\n",
    "y_pred_b = model_at_hand.predict(X_test_b)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "\n",
    "print('mean_absolute_error', mean_absolute_error(y_test_b, y_pred_b))\n",
    "print('mean_squared_error', mean_squared_error(y_test_b, y_pred_b))\n",
    "print('root_mean_squared_error', sqrt(mean_squared_error(y_test_b, y_pred_b)))\n",
    "print('r2', r2_score(y_test_b, y_pred_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bb92c490>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'true': y_test_b, 'pred': y_pred_b}\n",
    "results = pd.DataFrame(data)\n",
    "\n",
    "results.plot(figsize=(20,10), kind='scatter', x='true', y='pred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could this be too good to be true?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4. Fix problems <a id='fix_problems'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error 9.776803074043205\n",
      "mean_squared_error 217.18626934573558\n",
      "root_mean_squared_error 14.737240900037415\n",
      "r2 0.22495795178492728\n"
     ]
    }
   ],
   "source": [
    "categorical_features_benchmark = ['PULocationID','transaction_month','transaction_day','transaction_hour']\n",
    "input_features_benchmark  = categorical_features_benchmark\n",
    "target_feature_benchmark  = 'total_amount'\n",
    "\n",
    "# Train and test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_bench = data_for_benchmark_model[input_features_benchmark]\n",
    "y_bench = data_for_benchmark_model[target_feature_benchmark]\n",
    "\n",
    "# one-hot encode\n",
    "X_bench = pd.get_dummies(X_bench)\n",
    "\n",
    "X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(X_bench, y_bench, test_size=0.33, random_state=42)\n",
    "\n",
    "# Fit a model\n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "\n",
    "tree = DecisionTreeRegressor(max_depth=10)\n",
    "tree.fit(X_train_b, y_train_b)\n",
    "\n",
    "# Evaluate model\n",
    "model_at_hand = tree\n",
    "\n",
    "y_pred_b = model_at_hand.predict(X_test_b)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from math import sqrt\n",
    "\n",
    "print('mean_absolute_error', mean_absolute_error(y_test_b, y_pred_b))\n",
    "print('mean_squared_error', mean_squared_error(y_test_b, y_pred_b))\n",
    "print('root_mean_squared_error', sqrt(mean_squared_error(y_test_b, y_pred_b)))\n",
    "print('r2', r2_score(y_test_b, y_pred_b))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb4bba19790>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RLxGEONSrXrSOiFMLjFy+oONPPivJ6LKLXhjZ8+bGr/rmZTut1gnynJ986ozmTp3WxI7ztfPCrV1vr9dy+YJePLpF993wGj1/tsRnFwAgkZomkowx90qyjV631u71PaI+lYSxBpJwDH4bHc7y5a8LftQp6iWCEJd6VWkdsb8uVj/vS73Yhx8OzS3q1+86pmJp7WtN2pE+fu1E1+etFw/7ftU3L9vxsk5Q5/xDf/EN3f7wExt/X3/FxfroW36kq232klvZ7d5xvm/biuI9BgDQn4y1DfNEMsb8+Pr//oykH5B0x/rf75D0uLX2N4MNr7nJyUk7OzsbZgi+yOULmrrlsFaK537dG8w4OjJ9ZeS+iDeShGNAtPhRp6iXCEIc61Uvkh1Rbj2Ryxf0Yx87rMJqbSuabNroqx+8quN4e/Gw71d987Kddvfl5zk/+dQZ/cTvPrhp+d/82uti0TLJz/tCHO8xAIDkMcYctdZOur3WdIwka+1XrLVfkfSj1tq3WWvvXf/3TkmvCSLYfpSEsQaScAyIFj/qFPUy2uI6dXgc69XocFa7d5wf6ENoL/bRqYWlZaUcs2l5ynR+3no14LRf9c3Ldtrdl5/nfO7U6baWR42f94U43mMAAP3F6xhJFxhjXmqt/WdJMsa8RNIFwYXVX+I0vkQjSTgGRIsfdYp6GV1x7rZBvYqfsZEhlcqbW2CXbOfnrVfTvPtV37xsJ8y6PdGgC1ij5VHjZ9lxjwEARJ3XWdt+TdKXjTFfNsZ8WdIDkn41sKj6TBJm+EjCMSBa/KhT1MtoivvU4dSr+BkdzurWa8aVSZ1rlZR2pFuv2d3xeevVw75f9c3LdsKs2zsv3Krrr7i4Ztn1V1wci25tkr9lxz0GABB1TcdIqlnRmKykH1r/8x+ttaF/40/KGEkVUR5fwqskHAOihVnbkufYqdO67tOP6ExhdWPZ1mxad7xrT8cD04aBehU/fs/ads/c4qYBp4NqWRelWduClIRZ2/wqO+4xAIAwNRsjyVMiyRjzAknvk/Ria+0vGmMulfQKa+19/obanqQlkgCgH7QzkCwPUog66igAAEiiZokkr2Mk/TdJRyVdsf73gqS7JIWaSAIAxI/XqXQvfr0AACAASURBVMOjOI4SSQPUGx3OUhcAAEBf8ZpIepm19m3GmHdIkrV22RizefoTAAA82DuxXVM7tzVMylSPo1QZzHj/zLymdm4L7aE9ioktAAAAoNe8DrZ91hgzJMlKkjHmZZJCHyMJAOCfXL6gY6dO92zQ6+qpw+v3HbXpr+M+QDiiJ+jrrdfXc9xRXgAAeOe1RdJNkv5K0g5jzJ2SpiT930EFBQDorTBb27jte2rntkhNf92rqd7RH4K+3mg91x7KCwCA9rRskWSMcSSNSPoZrSWPPidp0lr75UAjAwD0RJitbRrtW1Kkpr/u1VTvSL6grzdaz7WH8gIAoH0tWyRZa8vGmBustQcl3d+DmAAAPRRma5tm+241jlIzfg+K7XWAcKCVoK83Ws+1h/ICwEQaQPu8dm37ojHm1yV9XtLzlYXW2u8HEhUAoGfCbG3Tat+dzIgVVDeVbhJbQEXQ1xut59pDeQH9ja6tQGe8Drb985L+g6SvSJqt+gcAbfFjQFMGRfVXpbVNGN3I/N63H91UTj51RnfPntLJp864xlsZINxv1Ov+EPT1Fub1HKSgro+klheA1ujaCnTOa4ukXVpLJL1GazO3/a2kPwoqKADx1ax5cKNBldtp4cEvR8EIs7WNn/vutpvKh/7iG7r94Sc2/r7+iov10bf8SMfxeEW97i+d1Pl2ul5EufVcJ11Igr4+olxeAIJD11agc14TSX8q6TlJv7/+9zvWl10bRFAA4qnZl/3qX30qH9jvv+uYHCMNpFKeHg7ctrF/Zl5TO7fxge+DTrqRRW3f3XRTOfnUmZokkiTd/rUndP2rL9HOC7d2HVsj1Ov+1E6d7ySREub13Egnx9Gr6yOK5QUgWHRtBTrntWvbK6y177LWPrD+792SXhFkYADipVXz4MqvPtWKJavCqvXcnNhtG5VfjgCpu24qc6dOt7XcL9RrNJOUrhedHgfXB4Cg0LUV6JzXFklfN8a82lr7sCQZY/ZIOhJcWADiplXzYLdffeq1ak7ML0fwotNuKhM7zm9ruV/6qV4zM077ktL1otPjSPL1wfUAhI+urUBnvLZI2iPpq8aYx40xj0v6mqQfN8Z8wxgzH1h0AGLDy+xb1b/6ZNOO0nV3oFYPB/xyBK86GRR754Vbdf0VF9csu/6KiwPt1ib1T70+NLeoqVsO67pPP6KpWw7rnrnFsEOKhaQkUjo9jqReH1wPQHQEOZEGkFTGWtt6JWNe3Ox1a+13fIuoDZOTk3Z2lsnjgKi4Z25R+1uMf1H9C+yRk99rub4bfsUNTy5f0PEnn5NkddlF5yWy/E8+dUZzp05rYsf5gSeRqiW5XufyBU3dclgrxXOJhMGMoyPTVybuWIPg5d4aB90cR5KuD64HAEAcGGOOWmsn3V7z1LUtrEQRgHjx0jy4ekDTTpsTMyhqOA7NLer9B+e0uv7sk0kZ3fbW3bF8oG1m54Vbe5pAqkhyvU5K96ywJKXrRTfHkaTrg+sBABB3XsdIAgBP2v2yn6SHgyTL5Qvaf/exjSSStDZY+gfuZnaxKOtlK45m+0pK96ww9fJeGWS9aXQcSWpx1EpQ10M/lSEAIFwkkgAALS0sLStlHEmlmuUpx/ArekTUP0R2MtV6p1rtqzLOTX23JupNb3lJNPSy3oS5zzAFcT30WxkCAMLlaYykqGKMJADojVy+oB/72JdUWK39zMimHX31g4zrEbb6h8gbr96lm+870ZMxWNoZ74UWE+HxkmgIY+yefh4vyK/roZ/LEAAQnGZjJHmdtQ0A0MdGh7O69ZrdNTPtZVJGt15Dq5Kw5fIFTc/Ma6VY1pnCqlaKZX3k3hNKGVOzXmUMFr9Vxnvxsi9mxgmHWx3ZPzOvXL5Qs14759IvYewzKvy6Hvq5DAEA4aBrGwDAk8pAuUmftS1uXAfuTRkVV3szJhHjH0Wf18GdwziX1J/uUYYAgF6jRRIAwLPR4axe9/IL9LqXv4gkUkS4PUSWylY3vfkyDWYcbc2mNZhxAhuTqDLeS5D7yuULOnbq9KYWNPDGa6KhF+eyXhj7TBrKEADQa4yRBABAzN0zt7hp4N69E9sjM2tbNxhE2B+N6oibMMayYvys7lGGAAA/NRsjiUQSAAAJkMSHSAYR9lcS6wgAAAhGs0QSYyQBAJAAo8PZxCUHvI7tA2+SWEcAAEDvMUYSAACIJAYRBgAAiB4SSQAAIJIYRBgAACB66NoGAAAia+/Edk3t3MbYPgAAABFBiyQAbenFNNxM9Q0/RK0e+RFP1I6pEb/jHB3OaveO8ztOInmJJ8iyjct5Q+9QJwAAcUaLJACe9WIabqb6hh+iVo/8iCdqx9RI1OL0Ek+QMUetPBA+6gQAIO5okQTAk1y+oOmZea0UyzpTWNVKsaz9M/O+/prai30g+aJWj/yIJ2rH1EjU4vQST5AxR608ED7qBAAgCUgkAfCkMg13tco03HHaB5IvavXIj3iidkyNRC1OL/EEGXPUygPho04AAJKARBIAT3oxDTdTfcMPUatHfsQTtWNqJGpxeoknyJijVh4IH3UCAJAEJJIAeNKLabiZ6ht+iFo98iOeqB1TI1GL00s8QcYctfJA+KgTAIAkMNbasGPo2OTkpJ2dnQ07DKCv5PKFwKfh7sU+kHxRq0d+xBO1Y2okanF6iSfImKNWHggfdQIAEHXGmKPW2knX13qdSDLGvELS56sWvVTShySdL+kXJT2zvvw3rbV/2WxbJJLa18mX6ah92YlaPOgc57IzXsutej1Jvpd1qziieH6jGJOfwk6YBKVRzGF8XnW7jziWfzf67XgBAEiKZomkdK+Dsdb+k6QJSTLGpCQtSvofkv69pN+11v6nXsfULzqZAvnaV47p4NGFyExRy5S5ycG57IzXcqteb7m4KmOMBtMp38q6VRxRPL9RjMlPYU9zH5RGMYfxedVt+cWx/LvRb8cLAEC/CLVrmzHmJyXdZK2dMsZ8WFK+nUQSLZK8y+ULmrrlsFaK5wZ4HMw4OjJ95cYvhG7r1Kt/Ty95OQbEA+eyM17LrdW13G1Zt4ojiuc3ijH5qdN7fNTLoFHM993wGl39yYd6+nnVbfnFsfy70W/HCwBA0jRrkRT2YNtvl/S5qr9vMMbMG2M+Y4wZCSuoJOp0CuR6UZ/GGfHAueyM13JrdS13W9at4oji+Y1iTH4Ke5r7oDSKee7U6Z5/XnVbfnEs/2702/ECANBPQkskGWMGJO2VdNf6ov8i6WVa6/b2XUm3NXjfu40xs8aY2WeeecZtFbjodArkelGfxhnxwLnsjNdya3Utd1vWreKI4vmNYkx+anR8WwZSOnbqtHL5QizLoFHMEzvO7/nnVbflF8fy70a/HS8AAP0kzBZJ/6ekv7fWPiVJ1tqnrLUla21Z0qckvcrtTdbaP7bWTlprJy+44IIehhtvnU6BfP0VF0dmilqmzE0OzmVnvJZb/XppR8qkjG9l3SqOKJ7fKMbkJ7fju/aVY7r6kw/puk8/oqlbDuvIye/FrgwanbedF27t+edVt3Uo6XWwXr8dLwAA/SS0MZKMMX8u6QvW2v+2/vcPWmu/u/7/vyZpj7X27c22kbQxkqIy2wyztqFXOJedYda2ziV1VrOKSuxbBlKbxhCqjE8jSceffE6S1WUXnReLY2TWtvjqt+MFACApmo2RFEoiyRjzAkmnJL3UWvvs+rI/01q3NivpcUm/VEksNZKkRBIzmwCoxwNY7yXlXnzs1Gld9+lHdKawurFsazatO961R4/nnk/EMQIAACA4zRJJ6V4HI0nW2n+VNFq37OfCiCUKcvmCpmfmtVIsa0Vrvx7vn5nX1M5tPDwi1rpJhFS3rHj+bKnvkilJSWh0qtcth3L5go4/+Zz23z2vwmr878XNxkzi8wYAAADdCCWRhFqVmU0qX+qlczOb8MUecdVNIqTyXklaKZaVTRkZx/RNMqXfk8te6o6fibbKthwZFVZrky+2bGN5L66MT7O/royeP1vi8wYAAABdIZEUAcxsgqTpJhFS/d6KQslKJds3yZR+Ti57qTt+Jtrc6lu1Qslqy0CqiyMKz96J7ZrauW3TGEJ83gAAAKAbYc7ahnXMbIKkqSRCqlUSIZ28t91txF0/J5e91J1u6peX/VUbzDh6/myp7e1GxehwVrt3nB/pGfUAAAAQL7RIigi3X46BuOomEeL23na3EXeNuiXF7b7QyRhGXuqOn4m2ZvWtep0k4fMGAAAA3aBFUoTU/3IMxFU3rR4q782mjQZSRpKUTZm+azmxd2K7jkxfqTvetUdHpq+M3dhQh+YWNXXLYV336Uc0dcth3TO36Ol9repOJTl149W7PNWvXL6gY6dOK5cvNN1fNm30gkxKKSNlUiaU1jqtYvUTnzcAAADoFC2SAASim1YPVpJklE4ZGWN1wxt26p17Lo7UQ6+fM4Y1Mjqc3bTtXuy3W92OYdSo7tQPsH3jm3bp8u3n1Yz/U/0erwNyV+qbjJROOfrQm3fp8ovO62kZ9/ssfQAAAIgPEkkAAuOWCGmlkoSonj3rD758Uu/cc7Hf4XUsrIf+uCQb/BgsvL7uuCWnbr7/hI5MX+maNLrx6l26+b4TLZNZbvXt5vvObbcX+n2WPgAAAMQLXdsARIqfAykHofqh/0xhVSvFsvbPzAfeHSms/XYiiMHCm9ULt7L5yL0nlDLGdX2v2+2VKMQAAAAAeEUiCUCkRH3Gsk4e+v0Y+yZOyYYgZgZzqxeF1ZK2DKTcyyZlVCy1rkdbBlIqrJZartcOr+e7st6WgVSk6zwAAABQja5tAELjNt5Pr2Ys63SsIbeExtlSqeFDv1/d0cJIsHktI7f1/J4ZrLpe2LJVoWTlOEZXf/Ih3Xj1rk1lUypb3fTmy3Tz/Sdqyl6Sjp06rbGRIT108nuanpmX4xipZJVNGRnHdFXfvJ7vynopY3S2VNZPX/4D+sKJp2I9Sx8QJXEYTw5A+LhXAJ0x1tqwY+jY5OSknZ2dDTsMAB1o9cAd5Ad7t8mde+YW9b6Dc6oMq5NJGd321t2btpHLFzR1y2GtFM8lOQYzTsfj73zo0Dd0+9ee2Pj7+isu1kff8iNtb8eLdhMivRq36eRTZ/TT//khnV2tLdMb37RrU9Jo78T2mnpUSRxlHEdnSyWVrVQsnfsMHEg7+stffo12Xri1o9i8nm+39SRp7+4f0C+85mV8mQW6FJfx5ACEi3sF0Jwx5qi1dtLtNbq2Aeg5L+P9BDU9uR9jDU3t3KZUVVeqYsm6bsPP7mi5fEEHZxdqlh2cXQhkjCSvZRTGuE3Pny0pm9pcppdvP09Hpq/UHe/aoyPTV258EazUI0k1sRZWbU0SSZKyKUfPn63t5tYOr+d7YWl50/hNknTPsX/RloEUSSSgC3EaTw5AeLhXAN0hkQSg58Ic78ePfS8sLWvAJZlRvw0/u6P1sszaSYj0+jw2K9NmyUe3WOt121XQ6/keGxnS2brxmyrmTp3ueP8A4jWeHIDwcK8AukMiCUDPBTXej5dBjv3Yt9dtuA06fePVuzZmGmtHL8dIaich0mg9L+eik0HIvQ7kXb9tt1jTjpRNbz43J58609Hg6F5jGx3O6gM/+QrXbUyst54C0JmoT9gAIBq4VwDdYYwkxAID4SXPPXOLmwbUbtQv3cv5b6efezv79jP+RxefdR3HJ4h9dsvrvtzWs1LLc9HtuATN6kSjbbvFWhkQvHJuKgN5D2bWfmfppIy93q/23z2ng7OLG38HOeYV0E96ea8EEF/cK4Dmmo2RRCIJkcdAeMnlV4Kok0Gt/UhOtrMNvwbe7mVStZNZ2yS1PE6/ByGvj6XZtt2OqdHg137G1cjJp85o7tRpTew4v+NBvhEfSf1RJIrHFcWYAEQP9wqgsWaJpHSvgwHaUT0Q3orWHvL2z8xrauc2bvYJMDqcbZmg8HL+K/3cK+tI5/q5N9p+q337EX+1TmLsdp/d8rqv6vWOnTrd8jj9Kgs3bttOGaMH/vFpveGHXuR6TG7v8TuuRnZeuJUEUp9I6o8iUT2uXt4rAcQX9wqgM4yRhEhjILz+5vX8x6GfexxibKSdsYzWBpKunfms/jiDLAu3bT9/tqQP33tcU7cc1j1zi57e43dc6G9JnR0oqccFAACaI5GESIvzwze6182g1m6DHIcpDjG6OTS3qKlbDuu6Tz/SMBFT7aGT31O5qsd02tGm4wyyLKq3vWUgtbE8Xyg1fMitfk82ZSStdWmL0jnqZGByREdSfxRJ6nEBAIDm6NqGSKs84NUPhBeFBzsEr53zv3di+8bAyVHt5x6HGKu127W0sn6xdC6TlHIcTe3ctmndIMuisu0H/vFpffje48oXzrWQatRVrTqeLQMpPX+2FJlzFNWuQ/AuqT+KJPW4AABAcySSEHlxe/iGv9o5/3Ho5x6HGCvaHcvIbf2BVOP1gyyL0eGs3vBDL9JvHXq0Znn9Q279IJte42l3cM5OB/NknLhkSOqPIkk9LgAA0ByJJMRCnB6+vWKWCO+SeP7joN3WBlFrndDqIbfTlj6t3ld/bXfToijIgcn9EpV7WVTiaCSpP4ok9biCFvX6CgBAMySSgBCE1VWFL65oR7utDaLYOqHRQ26nLX1ava/+2r7x6l26+b4THbcoilpyrl5Uut1FJY5WkpoUT+pxBSUu9RUAgEZIJAE9FlZXFb64ohPttjbopnVCUIlOt4fchaVlpYypWealpU+zFkKSNl3bH7n3hDJO+/upjt3P5JyfZRyVbndRiQPwgvoKAEgCEklAj4XRVYUvruhGu60NOmmd0OtE56OLz+r5s6WaZV5a+jRrIeR6baeMiqvdtSjyq+uQ32UclW53UYkD8IL6CgBIAqf1KgD8FEZXFaZo9ke3U7D7MYV7N9vwewp5v7ZXneg8U1jVSrGs/TPzgU11n8sXdPP9JzYtv/FNu1o+yFVaCA1mHG3NpjWYcTZaCLld26Wy1U1vvsx1/XaMDme1e8f5XbVE8ruMo9LtLipxAF5QXwEASUCLJKDHwhhHhi+u3eu2NYcfrUG62YbfrVH83F6vf6F329+WbEqXbz/P0/sbtRBqdG3vndiun7r8B0IdnyyIMq4+3pQxKpbKnpJxfovi2FxAI9RXAEASkEgCQtDrWW744tqdbrsG+tG1sJtt+N210e/t9TrR2ajlUDv7a9R9r1mSyW39Xg2AH1QZ753YrjMrq/rIfSc0kHZ08/0ntHUw3fPx15g5DHFCfQUAxB2JpIRhVi5/eSnPTsu817PcROWLayflFXa97rY1R6P3H3/yOZ03lPF0XO3GUF1m3bx3dDi76W+/W7eMDmd17eSYbv/aExvLrp0c8/1cVx9Hs8Tq7LdzevCb39PrLt2myZeMdryPVvH3clyooJLJlW6CZ1fLOru6tiys8deYOQxxQn0FAMQZiaQEOTS3qP13H1PKOCrZsm69Zrenh5I4Ptj3wlp5zivlGJXKVrdes/khL24zoYX9xfXOh7+jj9x7XJmUo5K1OrBvvGVyq1UZV9dFSZ7rZTt1uNvWHFsGUiqs1g7svFxc1S/ePquBlKOzpZJueMOleueeixvG4jWGXL6gOx95Qn/wwDc1kEqtTUH/pl2b3nu2VNazy0Xl8oWafdaX97WTY/r8352qua9M7dy2aXuFUllbBlINy/XkU2c0d+q0Jnacr5EtAzr+5HOSrC67aK072cHZhZrtHZxd0N7xi/Tok89pMO1oeDCtFw4N6LKLXuia3Kr8vWUgpefPljYtf3TxWd18/wk567H+3J4X674bXqMnn12WZHTZRS+UJF336Yf10MmcJOn3D5/Ua3eO6s/e9eqa8m1Ub9q5H4QxAH4QyWQGDt6sHz4f+1kUzm8UYgAQHdwTEAZjrQ07ho5NTk7a2dnZsMOIhFy+oD3/79+oenKgtCM98ps/Ianxw3UniZC4JU86kcsX9Orf+ZKKpXPXRyZl9PBvXLVRhrl8QVO3HNZK8VyhD2YcHZm+kpu4izsf/o7+4188WrMs7Ugpx9FA6lxdqn7QldS0jKvr4nJxVcYYDaZTLetlJ3X4nrlF17FvWqnsS5JWimVlU0YyUtmqpn5JUjbtuCYsKz506Bs1rXauv+JiffQtP1Kzr/13z6tQN0vYQMro19/4Cn38i4+tPfSvlmSt1VAmXXMsbnW6XuW+8okvPVYTS8pIxmjjHFQnxz7xN4/p9oefcN1eJmX0K1deqj/6yrdqZlFLGank8vGUdqR3vupiHTy6UJPsOji7IFu2KpSsBjNrA8tf+8oxHTy6oLRjlC+UNm9sfXuVcvil175Unzh8ctM6d//SqzX5ktGm9abd+8GxU6d13acf0ZnC6sayrdm07njXHu3ecb5rrFHEfbBWP3w+9rMonN8oxAAgOrgnIEjGmKPW2km312iRlBDHn3xOdc+OWi1Ln/rbf9affPXxhg8+7f4i3i/TyB9/8tlND/nFktXxJ5/V617+Ikn8Et+OXL6gj9y3eZas1bK0Wi5vJD7ef9cxOUYbLWne+/qdDctY0qa6KFkVS2sP5o3qZad1uJPWHNX7qrDG6ONvHddv/PdHN2KtKKyWm8bt1mrn/7nq5Rstb6ZnNieRJOlsyeo//fVjuunNu7RjZEi/ePusCiVtJDEq+1xYWlbaMU2PabUsfe1bOX3+72pjKVlJVqo+B7d98TH93t885poQqiiWrH7vS99UqVy7UqP3rJa1kZSqnL/qhJakjfJulLyq316lHH7fJYkkSQ9+83t6yQXDTetNu/eDpAyAz/hr5/TL52O/isL5jUIMAKKDewLC5LReBfHg/tT1Xx/654bTPXcyJXz/TCPf6GH63PKkPAj2wsLSsgZSzRMU0lpSobBqN+rrJx/4ps6W3MvYrS5Wa1Qvu6nD7U7B7ravbMrRC4cym+pOt3G3Ko+zq2XdfN8JSUYDqZTrdh5dfLZhy51qf/rVb7smrNw0SyJtrFOORsvYRlHsHjuvZfm3ez+oJGAGM462ZtMazDixTcDsndiuI9NX6o537dGR6Sv79pfQ/vl87E9ROL9RiAFAdHBPQJhIJCXEZRedp0zdg3raafzAKHWWCOmX5MllF71Q6bqrI+1oYxwVKVkPgkEbGxnSqkuyINWi9ctAKqWfn7pEAymjLQOpmjJ2q4vVGtXLVnU4ly/o2KnTGwnXbjTa12UXnacD+8aVTW8+/k7jblUekta/bFjX7WwZSOmj9x33clj6u++c9rReEhhJ27YOtiz/yv0gmzZ6QSalbNq0vB8kKQHTbpI1ifrl87FfReH8RiEGANHBPQFhIpGUEKPDWd321t3Kph29YCClbNrRR95ymUp1Y2C5Pfg0SoS4PVDHKXnSTUJgdDirj187UfNQ+PFrJzYdZ5IeBINUXW+2ZFNKO0YpI1XyKIMZR9m0syl5t1xc1WeOPK6BtKNi2erGq3dtlHF9XUw7a+PttKqXzerwoblFTd1yWNd9+hFN3XJY98wt+nbc9fvaO7FdX/3gVXr///FyZdOtr6dW11796wMps6mbWnUSq347z58tKWU2fyS0yPX1XMqsjQ1VHX/l7+x6Mn0w49Qs35JNNdye0drYRNm02ZSMl9ZaKVW6MlaXWzbt6L2v37lpXcmsN1yMWMEhcHH6fET7onB+oxADgOjgnoAwMdh2wtSP2u9lgGC3Wa8eXXxWH73vRMMZyxrNkBQVbgPPdTJbUdRnQfA7vqCPN5cv6PiTz66N0bN67t4zkDL6y195rU5897mN+nq2VNo0ILXbIL7NZm1rdjxus34FNWhwozhaXUdr5XVudrNWx1S/r786/i/6yL0nlEmtXcf1Y6TVz5z2Yx/7Us15CcJQ2lFZ0jtetUN3PvLEprHI6g04RqtlK6u181G20oeu3qUd/2ZIldnWvM7a9tH7jm86vkzK6FevulRvvOwH9Pm/O6VPPfTtTTG8+7Uv0W++aZck91nxKveXdutPvwyQ2c51WL9M8j4TY9Cxen2f1NuYG8URxc+suGi3zkYpPgD9h3sCgtJssG0SSX3A682l8lCTMqZm9iRp84xl1etH7SHILSHgNjtYFGLtht/l36vz2Wq2qkp9fXb5rN5759c7ntWq1Qxb9dfEsVOn9bY//tqmRMDn331FILNotSrvQ3OLev/BuY1B9DMpo9veutvzOam+noulsm5682X62Ve/uOn+Jel9Vfust2UgpdVyWVf98Iv0hUef2hj/KJMy+skfvlD3P/ovno9/MOPoxjft0s33n9hIHF71wy/Sl/7haaUdZ9M9qN5wNqXVuuRYKw8+9rTec8ff619dtm20Nutco+Gafvv/ulw/++oXN0w4/vHPvbKt+tovs501q+dur1mpo5kYg441iPf5LSpxxB3lCADAmmaJJLq29YFWY1fk8gU9+NjT2n/3Ma0Uy64PcJUZyypOPnVGH7h7vuFA3m778GvcmVbbdBt4brW8NiOWl1iD0CjWyvKTT51pq3xy+cLG+aoc0wfu7rz8q2d9OLe9Y3rwsad9K6dKDFsGUi3Hmtm943xddtF5Lft91x9X5e/Zb+f0gbtqy+f9dx3TyafOuHZfy+ULOvX9f9007f1KsazvnVnpqu66lb37+TtWcxwfuOtYTUKnWLJNz3H9PvevX5/Pny3pbMnq5vtP1G1/btP1u+sHX6jfe9uErtuz1iWs2lDG0Rsvu1CS0RdPPK2SlTLO2lhs/+H1L9PbXrVDA3U9yAZS0u/828tdu8elHKPLt5+nI9NX6g9+9n/Tp66f1M1v+RHd/8uv1Tv37HAdP6pavlDSSrGs9x08ptlv5zaV88mnzujWv/oH/db/+IZmv52TtDaWXKOBva0aJ5Ek6SP3Ht9IQLoNbPncclGFuoHhC6W1saek2nqQyxf0wD8+vanrYbMBMuvf7/f9NAhu95XKvdf9njNfc12sltfqvdt73a6pbq/TRrEG8T6/RSWOuKMcgeDFTOcO0wAAIABJREFU5TMMaEc/1ut02AGgc340Y6z88uYY46FLi9l4zwfuOqazdV1SGk11HcSve8226XXQ4UbTckv+NhFt1M2u0j1GViqU7MaDu5fyufORJzadr8JqWZ995An98lWX1hzH2n5ONm2NtbC0LFuu357Ve+74e5Wtt1YfzcqsvgyunRzTwdmFptOFt5pWfNM2Xzmmg0cXVC6Vddbl9BdLVm/8xN/KyGq1fG7q+F/7/JzSKafhwN+/cPtRDTiSk3LarruNzv1tf/1PLufP6rOPPKF37rlYt/31Y5uuL2kt+dKo3lZ37frMkcddZ1WrvPe3/uJRuTX4eePvPdhwlrXlYln//etP1ixby7tZfeJLJ5VNOypZo+q5z0rW6M5HvuOaoDm7WtZds6dULJX1F3NPKpMyWimWVba2aUKn3mrZ6pr/72GljNEL1pOU//uLR/S3J3Mb69zxyBN67c5R/dm7Xq0b3rBTt33xMe87WJdJORvlW1itLbyV1ZLed3BOldtOer07nrFWV3/yoZr6vrJakrVW2bSj5+sq6spqyXWAzENzi9p/9zGlzFrLLUkayqRDaS3Rzr2xknSrXGtSbbKs/rWUYyRrJLm3Rss4ju585An94ZdPNmzF1GmZNIu12XF2+j6/RSWOuKMcgWDR4g9J1K/1mkRSTPlRYat/eWulMmNZ5T1uD7luswRU76PyxWz/zLymdm7r+EtZq23WJyDcxtppNqOBnzcDt1jfd3BOKcfZ9KBfOQ+tyieXL+gPHjjp+tonH/im3rnn4o2Bo/fffWwjYVHZn9v2twykVHA5p5UuQK1iatWNrL4MDs4u6L4bXtNyfK29E9tdx7Zy2+btDz/huo1qbq1RSlYqtZjK/mxZUrncVt11i/H9dx2TkXVN4kjSJ770mP7z4ccavl4q24aJhumZedmydT2P0lr9+vYzZ/TQN5/R/3TpgublPtCMW+KqVLZ69MkzrusXS1Z3PHLunFX1COtIydqNbmXVSaSKvz2Z0+y3c3rnnov1u198TO0ebclaPbr4rG6+/4Qcx0gluza4tzE6u1pWdalXZigslKxUsrr9a2vHWf1wuuqS7XTrap7LF6q6OJ6rGJVj7fZ+2o52742tZpOpf23t+mycRTxbKusPHvimCqt2oyw/cPcxSUaF1e4+Yzqd+SYqM+ZEJY64oxyB4ATxTACErZ/rNV3bYsivptduXTTqZdNOzYxljd4zkHafJaBRN5BG3Tc6jbt+m9WzqX31g1fptrfu9jSjgd/N2pt1s2ukVfksLC1rIOV+3gZSKS0sLW8ch1srM7ftP3+2tKkrk9eYWpVZo/P1/NmSp+nC3bpmeqm7QUgZ47nuusVYLDVOIklrdaPR62nH6NZrNtfb6vJvlESq+NWD87r1r91b4/TDh8EXTqwl0FIus7M1kzLaGM9ppVjeSLpZY/ThN+9qkvpoz1Amval+HX/yuYZjVknd30+96uTe2Gw2GbfXbr1mXLdec+5eXT8T4w1v2KmBVG3/yZTZ3JqwkzLpdOabqMyYE5U44o5yBIITxDMBELZ+rte0SIohv5pet+oClk0bfer6V27MFtXoPQMpo8/+wquUSaeUyxdqYgji1z2v26w8rEiNW7bU87tZu5dudvValU+zbVbe63Yczbbf6nw0i6lVmfWqDniVSRnJ/v/svXl8VdW5//9Ze+9zTuYACQYCCVADegmFVNMCRWgdam1F216RtmL5/b6tenu/4u0odrjW6ddB0Xrb6r23au/vVqWD4q3g0NYq+FVUaINNkESUCEIGiSSETCRn2Gd//zjZJ3tYezjz9Lxfr74qZ++11rPWetbK3s9+1vMoiMcBJyjzve54epWIjEY8IvDHf1mNhppy07XuwXFTrJ14SI6k2c0Hqssm9ZU5ZovTcs+VS3FqPARmKOITBdMxt0Tgrwt7OdPlLRHv3mi391pd0/6mtq3+9/0v6r0xZSU8eRxuinjHxO3fiWSVSzbZIkeuQ+NIEKkhnR5/lMWMSBeF7MlaCB+h845kKazxy5tHZJAEaL4OL8OaRWfoNmDe17ovfLgOV//XX3XBi+3uT/TrXiJfjp08YJK9GRhl9YqwfOkv8giu+qKtU/Ui8olMV9bKiOGz8Bxz0gU7mZzGLF06sL7Z+fihmvnsj19bA68U+/Z3y2WNpsyFxsDdVjL6JAF2TYpMjUKmxycJuOfKJkwv9XKD+B3oGcKoP3nGjFzHZzPIFzfOwta9R3E6Rivit7a9gVuf6sB4yKzn5zXMhJWDU4lXRJFHwMaV9bq1ZSU3b1001lZyy5RO1p0ub4lE9kZ1T1K9JY3XjPuy9jfjf5u9mJZhy7rk7S9u/k4ks1yyyRY5ch0aR4JIPuny+LN7NiOIZFPInqyMF5MhV2hublZaWloyLUZG2NHaYwpCnEgcH97XX7sFoA3su/a+3Y4prFPxZSBVXxuSObYq2qDXUJRocG1FATad34BPLZnlGC+IV6c6B7yy2n4EZBmbzl8YjZ/kVCdPF+zG282YpUMHtu49ilu2t0dj1IgMuO5jH0Dj7EpUFEs67zrj+KxeOBPPv/m+qY1ijwBZAW65bDE2LJ+na9tN+natjK909uva/PKqBWisrURFsQe1lUX41M9f1nnKiALw56+tQft7w9zYNDwZshmPAAiCgPlVxXirb8zyPgZAEhkkQUAoHIaiKCj2SNF08F5RQEAO49z6aWg5egpeiUEOK1Edf6WzH1//XWvUd0YA8G9faMLi2RW46N6XbGUUmH32NhWfxLBl3TJc3jQHO1p78LXftep8h9Y3z8GG5fO56+dPB47jtqfa4REj/XNamztae3Djtv0QhUg/f3DZYiyprUz7l9Z498ZkB6Hk7SX09ZkgCCI3SOV+7fbZjCCSTb4+hzDG9imK0sy9Roak3CUbFLat6xSufmhvNPArEPFiefSa5VhWNy0jMiWDZI8t7w+bV2R41uK4UrJIVj/cvAhmgz4OjPrx0Z+8oIsNZfcAoZW5e3AcVz24B2OaIEWlPhG3XdaI888+w1Q+Xt23Gier+u7f8CFc98g+7kNR9+C4qUw2YjTOFHkE3P/FD2HPkZPwigy/fu2ozqsq0u9zUFns4R5vcmPgHBj1o713GIASNSBua+nCt7ftt5SzxCNg8yVn4yd/PIgJm8BEPknAgxvPxZpFZ+jae+2dAfSPTuC8hpmO6zrW9ZIN6yseOeihniAIgkgX+fpeQhCZws6QRDGSchhtDKBMke5zoel6mUr22PLii/gkUWe0SAXJ6IfbbATZoI+RQOQi/KGpBwi7OC5GmWWDYV0OK1wjEhC/7luNk1V9ALOMTZPMOEzJQBSAe9YtRSgMTC/xYPB0EEUeEd/9nzd0D3UeQUB1eRG+f+liDIz68atX3tXVEwyH0VhbYdIvq/+2mts1i2bqfmtyeIgMIxII386IFCFinDK2t3ZZbXSPMsaL48nntF6M+12m1xcQ+zqndOoEQRBEuijkeDUEkW7IkJRFpMpIkkrji3oudPMTkWMXQVnBzWsXx9yOGxmNXjE3X7oYS+ak73hHIuOYDX/Y4pU/3hdBY3vJ0kO7enjjPB6SEZwMiGxXVqvLWs8rq6MzTvfH0g8gMs5qZjCtjgMKArLe4BiQZQyNBzB3ejFuvnQxbt1xIK7g4cmiWBIgKwq+ffFZWDCz3HTkyE0MrW8/vh+RwNKMO468uevsG0Fr16mogai16xTmV5XAI4ko9YroHRrH8HgIFcUSaiuL8bd3T2LxrDJ0HB+N1ssAFHtEyEo4Ov5GRMaiRkZJALasW8ad52Qe4Ur2cbBkEes6TnTvi+fodbaQLV5kBKElV70hidSSL/Oc6LMZQRDuoaNtWUKqXhrS9TKydc/RaMwPWVFiasftsSleLJgyn4hQOLb24iEZ45iK2EtuSUT+gVE/Vvz4BV3sHo/IsOe7F7o2+q0/dy4e29edcN/d9EMd55Ac1qVNP6+hCi1HBx1l4D1M2bXr9PBlV58kMEwEIzGASrxSxHi0NhL/5kDPUNSoNBGSoSgKfJKIiaA8OQeROEFQAJ9HwHiGLEkiA0RBAGMRbx6fyMAEphsjJ92/+qE92N05EP336oYqPHLNiui/eePf8u5JPLznGFcmBqdcZ8CimaVY11yHe/7ydjT20KbzG3DPX9423fvwlz8C1chl9JRSSeYRrmw9DhbvPpKM2ErqGij2SFllWLMiWw2BRGETq16SHhcG+TjP+WIYI4hMQzGSspxUvTSk62UkkXbcluWdedaSypesZL8gpvsPW6LyD4z6sfxHz+uMMpIA7P3eRa5fqI3EM36x9KPlyADW/XKPbX3J1lEevIezVQ3V+OhPdsJvcXyqyCPg6U3nmYLYe0QGKEDQTSToNCIyQOaIZBwjK923mqtt/7QCzQuqLOOLBXiNxohHgM6TSwTAO2z6/12+GFd/dIFtXcmMy5CNMR6SsY8kGltJSzYY1qzIVkMgUdjEqpekx4UBzTNBEHbYGZJiz39NJB316JAW9ehQNtabzHbclnWKBZOKfsUqoxuqytKfUjhR+bsHx1Hs0Z+CLfZIluV57RmJZ/xi6ce7A6cd60u2jhrRxpYa8YcwEQxj8xP78eDLhy2NSGrdrV2nTG0GZSXrjEgAIFnMtXGMrHT/pUP93PLq77zxZ4wlIrK2IsO/+be9aCGjlmQeX82Go7BGEt1HYt37nPaRVO75iZKuv70EEQux6iXpcWFA80wQRLyQISkLSNVLQ7peRhJpx21Z9cxzkUdAqVc01ZPKl6xsfKmLhUTlj7W8mwDQ8YxfLHI4BVWORYZSrwh/SO+n4qYs7+FMFBh+tfuwo1xNddOyKoi2HQr4crod3zULq21/58170jxpDfVYmafWfnCWY1XaParcJ6HII8QdlyGZdSWLdO+DTvtINu/Buf43g8hPkvG3nPQ4/6B5JggiXsRbb7010zLEzQMPPHDrddddl2kxEqbEK6F+Rgl2vfU+ij0iBAG464ql+FD99KysN552Bkb9ONQ3Co/IUOKVdGWnl3qxu7MfRR4B4mRsFZ6MZ82qwBc/Uo9PLJ6FD86txKvv9Ke0X1b9Y0zBl89bAJ8koswn6fpj19dUYzfGseqBtq6qMl9M5XntXbW8Dm/1jdiW17Y5HpDR8u5JHB04jTKfhPGAjO7BcfzD7ArdvN+8djGKPGK0jFp+7owSnBzzo617KFr/6oYqvD/i18lQP6PEdq62t/bgy7/+GxhjCIUV+EQGjyS4KusRGR7cfRghjRcRYwxeUdDFm9LiFRm2rFuGlQ3VqJ9Rghfe7IMkMDDmLu5Puln1gSp88+KzsOut9wEo0DpMXbW8DpcurQVgvyZqp5dg37sncezk1NfPc+orccE/zDLpn1diUADc+MmzsKC6RDe/Wtz4K61uqMI3PnGWTk/vvnIZ9h0dwFhg6qF6doUXd6//kKt1fdasClzSOAtnzyrHNz+xCCsb+EYyo67z6lX3u4sW1+AbFy3Ch+qnx7W3JGs/sttHUrHnGdtToEBgQKlXiqz9S6fWPq9NVabRiSC6Bsej/5+OfTldf3uznUz9LST4xKqXpMeFAc0zQRB23Hbbbe/deuutD/CuUYykLCIXs7a5accuiJ96TWQMQTmMWy5rxIYV8xJqL1UMjPqxde8x/GLnoaghQBKAn65vMvUn3QEL3QYsdzNeVnUlmunFrrwxqK4cnjJKMACSyFAkibpsfdpg1OPBEBibukeVWZvVq6GmXCfD7s5+2zHjxuaRBDx7w3lof2/Y1TwbgwzfvHYx7ni6gxv3RWSAJArYsi5S1w+efEMXTFo1jqRzx7aKf6Ty/DfWoKGmHJ19I/j0L3YjEDLHWHAaZ5V7n3sL97/4DgQGBGTFFLR787Y2PNbSHb1/48p6bFwxn5u17dXDJ3HfzkOTmSTD+NhZM7H70AAABSE5ElT7GxefBQAmnfj671p1Y7y6oQrrmutc9cHNOow3gHQ8e0sq9iPjOk71nsfL2qZd+7w2VZmUsAK/rMAjRjKK8gLBp5JCDvaaj8F78wXK2kbwoHkmCIIHBdsmMoZdED8AORHgT/3jWuoVcekvdpvi2/gkhle/cyGAzPQnW7NFuX0ocROcW4tVMGrjPXYyO+ll9+A4hsaDuH7r66aAx/dv+BCue2SfrqxPYnhwYzMaayt1bQ6M+tHeOwxAiV7b0dqDG7e1wR/i770+ScDWr3zEMVh4qimSGAQm4HSQF346Ysi5/TMfBAC89PYJfPWRfbp7rcaKNzd2OlDkEfDol/njoRqytLjRJ6/I8Oy/rNaVHRj1Y+WPX+AG8faqGfJi7AMv4LidbOpeYtTbeNZlOgKoZiJIq1ObqQr2T7iHgvcSBEEQRH5gZ0giX+MCIJ6vDG5Tmpd6RYwFZO59A6N+7Dr4vikQl8gYdh18H2dUFEW+ymtirAhgaO8dxppFM2PuZzz9cLpP+1XVGCdnqj9TQQmN/VEDFhpfON3Ohxuvnu7BcUiC/jCPwBjae4ewZtEZtvUbUeP6OPXBqS9b9xzFbU+1wyMKCIXD2HT+Qly1vJ5bB69NO7TBqK3KiAKzlXnXwfe5Y/bzFw7ht3/rgmfSk0U2BLcOyGEcPjEG0RCk2R9S8NVHX0dYUbgeU9ov8pc3zYHAGL79eBsmOEG3/aEwvvpo5g3kssKPf3TjxYvwycZZUSPM1j1HcetT7abjen45jOHxoGmeRKafmydf78KvXzuKsEUgcVkO456/vMW9truz37T/uNGngKzgkp+9jNs+04gNy+dFy1l9VpE5H1zUPqhroOukOcC7ce04BS/1hxT89Lm38c2LF+l0N9Z1GW+ZWElHG7G26Wb+Uy2jkVT8Dc5mMqEXBBEPubzOCIIgMg0ZkvKcVByHUK8DwEQwzD0uED1aoMDkwTMWkKMvnrIhwN/poIxrH26JHu9JR9+t7uvsG8GNj7chICu2LyWyMhWU0ClgYSzzYbx3/blz8di+blPZAz1DGPXrjVynAzKu+fXfcPeVTTGN49zpxRgPhnS/jQdD3KCLVn3Zuucovv/kAQBAQI7Idc9f3sZ9uzq58+omOLdRHqdg1GN+GQd6hqKp0tWHRdW4IzKGsYB5zH792tGI3JrfRRaJIRA5chfG3c+9ZSqrlgeA7z95IGpgBRDVnc1P7Meqhmrs7uzHZhuPJADoHwtZXksXt17WiPIiCf/yu1bd71ueext1M0rQUFOum2sjTFHwrcfbTAamsYCMA72RuVnxo7/g+HCAW14lGAZeOzzIvfajZ9+Ez3CckafDPEJhBd//wwFAATasmIcDPUOWsauMBsWJYBilXtF0hMrI6aCMUq+Itq5TmDu9GKVe0dHzbutfj2Hb6926tRJPMFSrMlp5En1xykSQVqc2Yw32n+oXyWw5kphOKHgvkQvk+jojCILINJS1LY+xSj8+MOqPu4z2uvpC5JcV3X3ae6zSnI/6ZfhD4cngw/pr/pCznMnqu9V9W/ccjcR8sQsQg4iRYcu6Zagq8zlmWoplPnj3PrznmKlsZ98I7nimgytbQAa++VhrzONoTK3OS7Vu1ZfOvhHc9jRfHqt5NY6bR2QQbKIlM8YwvdSLu65YCp9kfePtT7djYNSP7a09WHXnTlz14B58/8kDmAiGuYYgKwSB4cf/uAQCA0Jh6Ix2RRJ/C+XV7xEEtPdGYivZGZEyjVdk+OHnlmDDinn462F+2vuv/67Vdq6ByL7gD+kDcKvc/lQHHn31iKMRyQ41lhJvLfF01opbdhywXUc8vCJD79DE1D5nY4D69M9fxtUP7cWqO3fijweOwyc6y2ZcK/FkceOVWX/uXKy9b3dUnh2tPa777LaNVGeXc2pTe10da8/k//tEprtf3RuSNR5GUvE3OBfIxqyDBKElH9YZQRBEpiGPpDwmWcchRCFyFK2pbhpau06ZjgQZ61b/281RpSJJxLcuXoi7/vx21KPDjZxOuO07t7+M4banO3SBg3kUSwJ+ubFZdwzv8qY5WNVQzf3CHct8uD2e4XTEKxRGTEcFuwfHIwGr5SmPjiJJdDVuqjxekSFg4RBi1V/juAFAe+8QDp8Yw93PvWUw3kTkubxpDqaVeHDdw/u4R8RENmW4sfICKfYICIcjx7Cs8IoCJoJheEUR/tBUx0p9IhpnV+Cv7/K9ZYxEvtArpmNx2YRPEvDgpE5vb+3Bo3/t5t4XBhzn2g5RYNi+/z3uNQZAFCK6y+PzzXOxemE1vvs/B3Txq3T7jyggKLs3FrZ2nYpjXhRX+1xAVhCYXE/37ep0l1IO5rVit7dYoS1T6hWjccWMXnKJvODHI1eiOLVp7PdYQDYdw9Z9FEnieGjJ1iOJ6SATekEQbsmXdUYQBJFJyJCUxyTrOMSYX8b3/vBGJJOSxCy9KbR1uz2qFAyHcV7DTPzkT2+Zfk/EDd5t37n3yWF4JcHxBVlhQGNthel31TspXpms7jUSDIfRVDctenzMRlKH67HLaHVfU900Xbp7nsxW82oct8baykgZ2VqextpKm7g2EcON3ct+WAHA7MdHVhRML/GYjksFgrIrI1KpV4SsKLjriqVorK009SebUBQFtZVFeOntE9i8bb/lfQJgOddOmd6AiKfOZ5bOxt8sxk9gDFZ6e+3qD2B6qddWT41H0ewQGIsclYxhXm65rDEylzEcyQQiRsnr1nwA97/YCYExnfHcCG+tWO0tdqhl2jhG52S9OMUjV6I4tel0PR0vkm72U+PRunw6FpYJvSAIN+TTOiMIgsgUdLQtj0n0OESpb+rMmXrEi2dEkgTo6tbWUeQxq5jAoJOnoaY86W7wbvvOu++WyxpNL8heScD3PnV2QjLGMh+8ezeurDeVbX9vmHt0SMUjsqhBJpkyWt2nnctSnwiRRWSIdczUIyfXb/07wkpEx6yOsWxZtzR6dEVFEiJHDq1e9kt9Ioo8ArasW4ot65bpjsEYHUbmzSjGVx7eZ/KQcTKW+KTIEbHfXLsCr9x0AS5vmoOqMh9uuazRsf9a7I75JRtZAS752Uv46iP7LI+lAsC/faHJNNdeScAPP7cE936+yaS32hOAHpFhy7qluPqjCzC7wmuqW0EkfhGv3xtX1qOhphxVZT6sP3eu7tr65rnR/YenE1bcfeUyNNSU286LsY8bVszjHqEyYuxDMBzGVcvr8cpNF+C3167A+mZ9PA6Rxb5W3EIvTnrSMR5O+ynvaB0dCyOI1EPrjCAIInGYwslGkys0NzcrLS2Zz26U7cSbMWbXwfdxy452x3gyvFTaah3tvcO49uEW3UupenymsbYi7mxmbvsXb9a2rXuP4ranOuARGeSwEg3CmIzArLHU0dk3gtauU2iqm4aGmnJdWQD46E926sZWZIAkChCFiNybzm/AVcvrAcD1OLT3DmN4PIiKYo/jHFn9W3uMxKltXh3G1NFaneHVF5F7aFJuL2ori6Lt/6n9OG57qgOSwBAKK/jnj30AddNLomOq7XfP4GncsqPdMTaWHV6R4YYLzFnqtP18Yl83tjwX8cILykp0vrQwAJLILANAx4MoMHgndfqGCxZi5Qdm4IsP7XXdxiWLz8AP/3Gpo0609w4BYFH9UccXUNBYW6krf++f38TPdh02teUVGTZ/8iy0dZ2CzyPgCx+uR/OCqmibRh0x7kNTOhFC/6gfd/3pTZwOGgzEooDXvjuVklxd98FQGAqAOZVe7Lhhjak/WrQ6f/D4CN58bwglXgmfbJyFjveGsfmJ/RBZJAvgLZc1YsOKebry2jU+vdSb0qNAO1p7sDnJwWVzOetRKsaDB2+MeDpc5BHwyk0XcPdWgiCSD60zgiASJd/3EcbYPkVRmnnXMnK0jTH2LoARADKAkKIozYyxGQB+D2A+gHcBrFcUxV3wEcKWeI9DnH/2GfjX7fyMTFoY9MGF9caOyEuyFq8ooLLYk/BiGxj1Y+veY7h/Vye8Iv9FwG3f1fsGRv34+QuHcP+uQ5GXi1Dk5U+tMxmu+tq27LInWWUUUe/9+QuHTF4jJV4J9284B6++049f7T6M//w/7+DnOw9BURQUeyTH7HXfeqw16nnjERnuuXKZKROfVp5VDdW28qpZ06zGzFjm5rWL4RMFGO3bEYO3gt2d/ZZj0lhbGc3Mdt0jLZFjKyEZihLRwdOBMAQAP3uhEx4hEkR7y7pI//504Dhue6odAmMJGZEmpcW8qpJon1uODODBl49g58E+eCURE0EZgsAmYy/JEa8tATCerFJgdhmVBAYGBQ6JvyyRwwrGwwokAZhXVYJXD590NCKVeEWdUZLnnVZV5kPLkQF8+/E2vHyoHx5RQFhRopnHIvNTEY1hpPKDJ9/Aw3uOcdtVFOBHfzwY9bh7srUXP13fhFUN1dh18H1TrLaArODTv9iNuzVtrll0BoDIXnEHJzC4R2K6o0wbls9D27FBPLYvEnS5ZyiAD//weZR6JZ3Oax8YtHtCQ0051i6rjdbfUFOOkYkQbnu6A15JwB3PdKC8SNKtv4aacp0RPpUPIcmOW5PrWY/SFceH93fD6WhdvhwLy/cHbCK3yZd1RhBEZsj156BEyYhH0qQhqVlRlH7Nb3cBOKkoyk8YY98BMF1RlJvs6iGPpNSjfrFV01v7JIF75OX5b6xBQ025bkFNhGSEw4rpCJD2q6tKrAtxe2sPN4U6r263ROrcb+pfInXatWXXXzdfqz/6kxdM/fdJAr75iUX48R8PWrbN649dfa9+5wIAMMmjZlfziiICsoywAp1RwmnceH0EgGKJYZxzhJLnoaO2oRqYRMZiysgmCcDmT56NH9mMVzxIArD3exfha7/7O3Z3DiS17mThkwSEw2Fbo5RPYvjyqgX4r1eOwCuKlmvz6of2cPvpERn2fPdCrgFw8ewKXHTvSzHJrHrcSYL1PFvp9/IfPW86nigy4K/fvyh6b2ffiK1MWp13s085reNcJp/7lg4KYfwK/QEEl0e7AAAgAElEQVSbIAiCyF8K4e84YO+RlE0xkj4D4NeT//1rAJ/NoCx5her5Ek9a08ub5uDpTefhh5/7ILb90wr88LNLTCnXfVIkW1dn34gunWpQNhuRfJKAm9cuRvfgeFSeWNOwqvfz4jWJLJJhLta+TtVpfqvWZoNKBm76q36ttpKjvXcYIjMv3y+vmo97/vK2bfu8/nQPjnPrE4WIx0b34LjJAyQ4md59xB+CP6SYPFsEBttx4/URANeIBEQ8dIxteAQB7b1D0fGMxYgERDKD3flnvhHJIzKIguskW6Z6/+f17oSNSF6bOD/eJOzeAmfOtXx1zZn41e4j0Xnm6WrLkQHLfgZlBa+908/V992d/dwydsgK4A/ZzzMD8FRbLzr7RqL7XvfgOLwip6+GTG1/+Ds/S522P3ZjAUSMUdtautDZN+K4jjNFIn8TVNLRt2TIma3ke4wWSq9OEARB5DPZ+oyXTjKVtU0B8BxjTAHwS0VRHgBQoyjKewCgKMp7jLEzMiRbXpHoF0HT0aNLF4MZMir5Q2HcsqMdQTkMwSYysADgf62ajzue7tDJM6+qNKbsObwjASpjARm3PtWOf91+IKa+2tWZ7ACsbrIF2QWCtfKc8kkMK8+swiN7jtpmnLPKwhbJcma4V47c+6cDxzHqj81IczoQxta9R6PH24y4yUznRKQ8c5WG3QqRRc7YmupO8Iibk1HCDQoiBqOAoWs+ScB3P3UWqsuKsPmJ/bbZv6wIhMLwiPb33LfrHcgGr9WJYFinqy8dsjcI9Y8GuL+/2z/mXtgYGA+GcetTkWNsarD/y5fV4jTH9UoOK/jN3mO44cKFAIChcYdUjRy0Y2E8qre+eU7WBbhOlpdIqoNVF4I3S7qO1mUCSq9OEARB5DOUxCRzHkmrFEU5B8CnAFzPGFvjtiBj7DrGWAtjrOXEiROpkzAPSPSLIK/8Hc904Oa1i01Z3cYCMgKyYjqmpCUM4FcvHzbJU+oVY1qITgaIUb8cc1+t6vRJLCPZk6y+VgPgek75JBbNUmaVkt0pC9sP1pqzVimKgsGxAO54xhxfxg2PtXSjs2+Ee03to9HDzQ1ekaHIE/FuA4AAJ3V7qU+MeBUxwGtjMHFrgymJ0QWo473RmO7n8cE5FSYjEhAx3v74jwcxNB5EOM7jyQqAb3/ybF1GNSNGI5LKkRNTc7pmYTX3HiCid0tqK0z7wkQwjEf3HI1J3niYCIYxEQzjsRZro94vdh6K7hMTgWDM9ZdOKldn34gp3tNjLT345kWLssbrJJleIqn0qCkkb5aqMh+W1U3LO+MKPWATBEEQ+Uy+exa7ISMeSYqi9E7+//uMsT8A+AiAPsbY7ElvpNkA3rco+wCAB4BIjKR0yZwraANbtvcOQTAc3Yjli6DVF8UltZV45aYLuFndfCKDwhgYwD0mFrmi6OrrHZrA9R9vwH27DkXj7Vz/8QYMjgW4X2rVhatm2wnIMtadU4ftbT06rxmBMbT3DkUD7tphrjNsGVw4UarKfLj50sW47al2eEQBsqJENx7t/PG+Vrd1nTLNSYlXxH9efU60n2o/RIEhGArjK+ctwBXnzI1mMbPKnOaThGi2NRWvJGJ3Zz9Egx5JAhAOQ+cDJABcn6DWrlO6TFra/lzeNAcCA771uNk4ZlUfAIQV4NsXLYp6t4Xk8GTQagYZCm68+GycPB3Agy8dhigwhBUFaxZW4ZV3Tpqyo7nl+o834OjJ07ZGCTeUeEUEQzJCYe1K4PP6sSHLa/6Qgtufbsfqhmo8f3DKqH75stkYPB3Ay4ecj9XNKPFi7/cuQnvvMLb/vRtP/L3XVR/+0NqD1ZP6dnzYjw9Ul+Bw/2ndPQKA2z+zBAd6hyAJ0MUn8giTcxvjVPxDTRne6R+DRxTgD8mQXYyhE4yxaKbCF96K7eOER5hKNGB1VG9GqRev3HSBpdfJCx3H8VxHHy5eXIMLF88ylTdmakzEeyXZXiKrGqrxwJfOhVVGu3ghb5bUko4A2Ma/qapXGc0fQRAEkS/ks2exG9IebJsxVgpAUBRlZPK//wLgdgAXAhjQBNueoSjKZru6KNi2nngDXVvhJuAz7/rTm87DwePD2PTbVsc2JAEQBQFeMWK8ufDsM/DCwffBWORrv09kYALjHmswvmDxAjernjpuj0Sk4wFbnSdJiGQIu+WyxdiwfJ6roxxuA7tNZbQ75BgYWG3XKlC1nUHHDd/71Nm47mNncvunANxjeiKDSXeN2N2jN1dOIQnA1SvmYX5VKbb8+a2YA3MDMAVsjoUiSUBQjqSWj9Oe5Qqr/htRg+QDEZ35yI9ecGVo80mRrGzG439zKn04PuyHVxIRCMkAY3Eb7nh4RAafJCAgK/j2Jxbh7ufeSjjLHkPE6GzlfQVEYoXx+qEG697d2Y8bH2/jyqLqP4+L730Rb/dNHfE7q6YUf/7Gx6P/1q6Z8WAIjDEUSe4CffNIZmDIVB49y5UAlrmYkSzdRwZzcYwIgiAIgoiQbcG2awDsZoy1AfgrgGcURfkTgJ8A+ARj7BCAT0z+m3CJ8SiAVaDrWL4IOrnsWV1vqCnHyjOrXQUpZozBHwpPBmwO49kDx+EPhaMvEP7J43K8Yw3aIwFTx6T0Ku0PKTEdibA6ZhBr0Fer+wdG/di8LTJPo34ZgVAYdzzdYQpUbtdndcxLfSK8k8HLeXP67y92mgIDawMQq/I4BapOLIoRcM9f3uL278ZtbVwjEuBsRHK6x+pSKAz896tHMTgWQDBGA0conJgRCQAmQmHISmqNSIA7I9LGlfW6tPORgu4E84fC3BhSPUN+yAowHpQhK0iqEQmIxK1S183df3kbHptg5EDE+FDkEbC+ea7lPQqsj/CpbP7EQu4fS0FgGBwL4KYn9lsatH76/NvcfeOFjuM6IxIAvNU3hhc6jgMw7+mhcKT/iRz10u0fXhFekeHmS837h9N+l+qjZ7ngLr69tQer7tyJqx/ai1V37sSO1p5Mi+RIJo4M5uvRPYIgCIIodNJ+tE1RlMMAlnF+H0DEK4mIA7tg0QBQ4hHxn186F2sWzYypXieXPavr3YPjKPNJGPFbB68t8ggQGENQdvYKcXOs4fKmOZhW4sVXH9mH00E5prJ2xPoF1+7+rXuPmQwnHiGS9c7tUY7Lm+ZgZCIUPRp3x9MdKPdJOpms9OHTP38ZPo1HAy/QebEkIAz+0cR48IcU/P+vvGtqR2RxpkRLAj/b2QkHG0RWUSwJGE/SfAART5rmeTN0v3UPjkNw4QmWLXhEZqmjqkH5+o9Hjqd2D47jmf3vxZzVT+X5t06gyCPq9hUV3trVyWmxjp/r6OPe/1xHHy5cPMsx60e8+1p0/3i6A15JwB3PdKC8aGr/cLPfpePoWTa7i2sNMuoYbH5iP1Y1VGeVnEboyCBBEARBEMkiU8G2iSTjFIA6DAWNtRVx1e30RZF33U1GLkVRXHssuA3S2VhbgbDBHyORAJ+xfsG1u39g1I/7dx0ylQnIYTTVTXMdmHRg1I87nulAQFYwFuAHFueN/0QwjIDBo4EX6Nw4fsng8X1dpoDYshJOyGPFIzJ4EtjBUmkw+f6nz+anm48DnyTglxub8b1Pn52U+oBI32/c1qbTmYguJK2JlCOHFWz+5Fnca/5QGP5QGPe/2AlAzUoY/4RfdNZMhMJmI1JQVjC/qsR2r7NaxxcvruHer/5e6hVtkxfEu69F949Q2JSYwO1+l65AytnqzZKrKX8pADZBEARBEMmCDEl5gvEogEdkkAT7TF3plKfII2Djynrdv7esW4Yt6/j3qGm7fZPZudzKn+wjEbG+MNjd3z04Dq9oTh+26fwGNNSUu5bbjUzGcfBKAnyiOfD6WEA2tWucF0mYjE1j4cLDELmulv/0B80vyD5JxKbzGyzbMc43rw4tXknAPVcuw6/+3w9zjzcxJObs5BUZiuO0Um1cWY9r15yJGy5ocHX/ho/U458/9gG+HJKALeuWYs2imbhuzZn44eeWwCsJ0Yx0VtRNK3JsV2R6nRkLyNF50HLpklncfYXX/uqGqui9VuKp9ZV4+Kn0RCESj8pY/KyaUtP6uG7NmfjhZ5dEsvhx0s9pvS1UPTfq2qVLzAGujaxomIkbLlhk+r3II8AjiXHtvRcunoWzakpNfVQDbo8FZO6aK/GKCe1rTnuUm/0uF46epZJcNcgU+rwRBEEQBJE8MpK1jUgNxqMAQGIZfpItT1WZD1+7cJFJJqt71AxiscqfzCMRsb4wON1vvOaTGK5aXh+T3G5l0tZX6hWx9r7dOjcctcyyummmdgdG/XjgS80AFDTWVgJAtJ7eoXEMj0fSpFcUe6Oeblq9e+HNE7pjR8FwGFctr48eM+LNv3a+eXWoeESGZ284Dw015RgY9Uey02n65RUZnv2X1Zhe6sVr7/SjfzSAwbEA/uP/vANJYDjtwu2GsdjiGIkM+OYnFuKTjbOjcYeuWl6P+3Ydgj9kXZFPYvjmxREDxX+9ckR3r1fTT5UNy+fhksZZ0TF8+e338fXH9pvq/dbFi7D5if2wO8klK5HU9W1dpzB3ejHmTi9G2NBprwjc/tkluB3g7isA8No7A+gfncB5DTOjc6LO56W/eFnXJ5/EovW19w7j2odbdHPslQQ8e8N5mF7qRffgOPpHJtDWPYQ1C6uxYGYZ2nuHoeqkqj8bVszDJUtmcevTrgvjetDq2vMH+yznqcgjYO704sn57DTpJG8NacfIbv/58zc+bpm1be70YjDDWUOfxPCfV5+j63+sxLpHWe132Xz0LNXkckYy3joYGPXnhOwEQRAEQWQPac/alkwoaxuRDna09pheGOxiJNndH2tdyZIpljLJyOqTjH7+4Mk38PCeY7rfPCLDPVfqs/C5bUs1cPzo2Q7sPTLIbVP1VrnriqUAoKt3ffNcPNbSDQC6I0c8maxkU+tIpm588t4X8ZYh89f/Pn8hvv14W9TAxhAJDK0eJZSEiKFLK0vzvOnY3Tmgq9uub25w6lOydTLeMdSWM8Z1W91QhUeuWZFQ/fGSqvbSsUcVArmckSzd2dsIgiAIgsg97LK2kSEpi0jkoTSVD7R2dbttV3sf4O5rfTxl4pHNTT3tvUMAGBprK1zV5TRmPM+KWGWOtX9W7RrH2Snttjoew+NBVBR7UVtZxPUc442bk8xTdYfwrcfb9N4qk55Gpkxjmj44ebF19o3gontfMv3+488twYfnzzCVNcrb2TeC1q5TmF9VMunZZJ5DK9m03l68f0e8vSZc1WlE69XSVD/dNIc+ScAzN5yH3qFxAAy1lUVYe99u2xg8KkUeAY9++SN4d+A0muqmRb2FYtM76/XjtFZee2cA3/j933Xxm+xSwcezLtTxP3h8GJt+22q65/lvrInqXbqNB1btuVlL8V7PZQMJ4czAqN9xnycIgiAIIj7y6TnKzpBER9uyhES+Dqbyy6Jd3W7b1d43EZKhKAqKPVLSy6RiTOKtp6rMZ7lx7O7s59YZa1t2bbjth/H36z/eYJvVZ3trj87TRcUnMjCB6WQ29tPOG0eVkVd3tA1JtMy6VVXmsxxXLa1dp7jlD/QM44vL53HrVcc4WbpgrHPztjYoChCQFe44uuHCxbOix6LaOFnEvGIkHtaaRWdY3mOFHFaw7pd7ov9mAMp87tei07xY6bGdPthlmkpkXfzjh/h9ae06FTUkxVJ/MuC156SLbnTVrh/p7iORXih7G0EQBEGkhkLy+KVg21lArJnBklU2kbrdtmu8LygrCIWR9DKpGJNUjK1VnZ19I2mfR16b9+3qREDWG2vUGCkDo35s3raf+2LvlxVH/Xj4tWOW/bOr2yhHLH00jl9T3TRu+cf3HUuLThnr/NZjrfCHFAQm+20cRzd1tHWdcszYZxw7N1kVo2UNc6Ig9WvRSR+SEdiYJ9tjf+vi3suJ450xnMY0lX8TiPwgV4OFEwRBEEQ2U2jPYFn0eFy4JJJKOJVpiJOR3Yd3XyrKuJU7FlIxtlZ1tk56iCSzrXja9IoCNp2/kJvVp3twHKJgnwvNTj+s7lVltKrbTZYqt3PVUFPOzdLlk6S06JSW9t5hcGKJu657e2sPVt25E1c/tBer7tyJHa09ANxlZuLds7qhSi+DyGwzw7mRM95xs9MHr8iSEtiYJxuzaPNw/+mE2komTmOaq6npifRB2dsIgiAIIvkU2jMYHW3LAhL5OpjKL4vJyO7j5PmQrDKxyO2WVIytVZ1NddPSPo9WbVplV5s7vTgarNkKO/2wu9eqbq8IV1mqYpmr2z+7BH9587guo1m6dEpPfN5XgP6Lh3o8ZfMT+7GqoRpVZT5XGbV492hjQHkkEcGQrDvWFquc8Y6blT54bOJkxQp3n7GIGbhmYXXC7SULpzElbxPCDYWcdY8gCIIgUkGhPYORR1IWkMjXwVR+WbSr2227xvs8IoMkIOllUjEmqRhbqzobasrTPo92bVaV+bCsbprJi2XLuqVcLxWfyBz1Y+PKesv+8eqWBODuK5uwZtEZjuMQy1xVlflw95VNMY11KnShsbaSP5aSs8eNmy8evDk0YrynoaYc65rr0LygCsvqpqF5QRU2rqzXlWFI/Vq00od7rlyWFCOSlWz3rG8yeWatbqhC84Iqi1rSj9OYxjLmvKORROFgt0eQbhAEQRBEbBSaxy9lbcsi8jlrm+rpEEvWp2zK2pbssY03E1M2tOk2axuvbqe2OvtGsLvzBKrLirDyzKqUroN47o1kVotkPnObwc+uvR2tPbhx236IAoMcVrDp/AZctbzecV209w7j2odbdFntUpl1KZ71a5Q5Hr2OJ2NirPBkazkygJcO9WPNwuqsMiJpSTRrWyEFgyRig3SDIAiCIOKnULK2kSGJSDn0UEq4IVv1JNEMgsY6jOVUwxCgOB7h09YzHgyBMYYiScyq8SKyH0r/TlhBukEQBEEQhIqdIYliJBEpxSmWC5GfxOPllI16wpMLiGQtA9zJ6NS33Z39rgxovHp8EnD/hg85GqAKiXz6CpQqKP07YQXpBkEQBEEQbiBDEpFS6KG08IjHsyhb9YQnlxY3Mtr1DYBrAxqvHq8oorLYS2tpkmz1ass2Ci0YJOEe0g2CIAiCINxAwbaJlJLsh9LOvhFsa+lCZ99IMsRLKlbBSRMJWsora/wtnvq1ZdyUd9uG1mtmxB/CRDCMzU/st21nYNSPofEgAnL6X17s+tXZN4K/Hxs0yaXFTXa1ofEAArKs+90fklHqFblBs0XG0N47jLauU+jsG4nKV+oV4Q/p6wnIMobGAykPiNtyZAA/fe4ttBwZ0P2eDF3kYVePnR4Zde/bj7eZ9go3Mr7QcRw3bWvDCx3HE+pHIqQy2LFdMMhk7gdE7lFogUIJgiAIgogPipFEpJwdrT3YnAQvgR88+QYe3nMs+u+NK+tx+2c+mExR48bKEyIRDwleWQXQ/bb+3Ll4bF93TPXHGvMnlj60dZ3C5x94zRRf439/vAH//mKn7fiM+kPQ7kapnl+7fhl1TRQYSjwixgIhaLPS28nIG2eRMfhlBUWeiPHo5ksX49an2hGU9fuwyACPJGAiGIZPZJAVBYwxiAIz/ZbqGElXP7QHuzunDEirG6rwyDUrTOMXjy7ysJsXu2ttXadw9UN7o8cOVbySgLvXuV+PF9/7It7uG4v++6yaUvzm2pVpPS6XLs8q4zFAN+2S11dhQEdECYIgCIKgYNtExkn0obSzbwQX3fuS6ffnv7EmaenA48UqOOnTm87D2vt2xxW0lFenT2IAmC5TlxGn+nn12pWPNfCq1Tz5JMGUYYw3PrH0JRHs+jU4FuD24cefW4Jbn+pwlSmNP38CwoqiMxr5JAHBUNji4Jw1XlEAoCCgqSsV49VyZADrfrnH9PuvNp6L63/7d8u5i1ceu3kBYKuLdrrtdj2+0HEcX3l4n6m8V2TwpSmoeaaCHbtplwIxEwRBEARBFA52hiQ62kakhaoyH5bVTYv7ZaO161RMv6cT3vEkjyCgtesU93c1Nk6sdYpMgCgw23JO9fPqtStv1TerNsYCMnwiM9wPk9xW4xNLXxLBrl9WOvXe0MSkAcdZRu78CYz/mxj7NiyJDFKcuhULLx3q5/7+XEef7dzFK4/dvDjponokxyua14jb9fhcRx9XroCsmI5qpopY11w6282UbARBEARBEER2QYYkIidoqpsW0+/pxCoOVFPdtLjjQ/HqlJUwQjbxetzUz6vXrnysMa7mTi8GMxiNBIFBDus9H63GJ5a+JIJdv6x0as3CatdjwZ2/sAJZMf+mxOyPxK8rFeO1ZmE19/eLF9fYzh0ABOTY5LGKJ6X2y40uXt40B8/+y2p4JcF0n5v1ePHiGkc5RYFh18H3U2ZMylSwYzftUiBmgiAIgiAIAiBDEpEhYg3W2lBTjo0r63W/bVxZn/FjbYB1cNKGmvK4g5by6vx8c50uhpAkRMYglvqN9XpEBkmAZflYA6/y7t+ybhm2rHMeH0kAPCJLS4BXu35Z6VrzgirXY8Efh6XYsm6Z6be7r2yC1u4hCpE5UeMo+SbnSDs2vLpSMV7NC6qwuqFK99vqhipcuHhWtH+qB5rH4Akkh8N4pZPv0WRke2sPVt25E9dv/TvCCrg66VYXG2rK8YUPz9X9tr55rqv12FQ/HVWl9slMx/wybtnRjlV37sSO1h5X/YuFTAU7dtMuBWImCIIgCIIgAIqRRCRAvHGPEgnW2tk3gtauU2iqm5YVRiQtVuORSHwotWypVzTFd/FJAl79TiR2jLZ+N+1p7zGWj6VvTnJr73czPm5kSSZ2/bLStVjGwu04DIz60d47DEBBY20lAETnfSwgW45NugLithwZwEuH+rFmYTWaF0wZljr7RvDpX+xGwCJul5v4OVbxpB7c2IzG2gpu/Cm7PjvF8bEqv721B5u37TfFIJMEQBQESCLDmF/vLZXqOF6ZCHYc6/5BRiSCIAiCIIj8xC5Gkv2nV4KwIF5jkDZF98TkkZ7NT+zHqoZqVy8kDTXlWWdAUlG9Jtz+HkudbZPxXSY0x6C8YiQ2iTb2lNt5McrkJF+sfeDd73Z80vliatcvK12LZSzcjkNVmQ9rFs00/carL15ZEqF5QZXOgKQSiYklWBqS1Pg5djKqcXeMul1Z7IlrPfHq08rBK6/uS7xA9sUeCfdvOAfvD0/glh3tGAtMGZPc9C9e0jW38bSbKdkIgiAIgiCI7IAMSYRrtN4xboxBvK/W3YPjEJk58LIarNXNV25jvVb/1npz2HkixOIh4vYLfGffCHZ39qO6zIuVZ7ozktn1kRebxC+HEQzJeOntEwAU1FYWY/O2NvhDSnRebtzWhmklHtRWFpvGor13CMPjQVQUe9FYW4HBsQBau05hflUJTgdlAEznEaId14PHR9A/6sd5DdVoqCmPjtfweBDvDY2jresU+obHcXIsiJAcRiisYGa5F3XTS8DA0HF8GD6PAAEM/qAMWQFmlHpQ6vVgzB/C+6N+MKZg3oxS1M8oxdt9I+gZPI2TpwMIKwqKvSIWVJcCYFAUBWfNqoBPELDnyABOB2QUeQQUeUXMrihGTUURjvSPoWSyzJH+MXSdPI2womBxbQU+sqAKDMDAWAAzSr0oL5Jw+MQo3jkxhvkzivHuyQnMqvBh9rQinNcQMfbs7uxHSA5j8LQfJV4PPtk4Cw015VEvpvlVJfBIYtSTSB2bimIPaiuLJsdvAuc1zDR5O7X3DpnG3kk/Yr2eLJxibvlDMkq9Ysx1JBJ3J576eMYnbdnG2go01lbgX7cfSJqcAHn2EARBEARBELkJHW0jXKH1dPGHZAgC0x0dKfdJePSa5Vg2GajYyjNm656j+P6T+pexIo+Amy9djDue6XD0pDHWu/7cuXhsX7fp3wAwEQzDJzIwgWF981w81tJtqp8npwLgW4+1QnVOEAUGBgXFHokrm/Fl8AdPvoGH9xyLXhcY8G+fb3J9fM9y7PYexW1PdYAB8IfCkARA60AhMCDMWc5eUUBA1ozFuXPx27916dLQWyEJwE/XN2FkIoTbnmoHAF3KeSASM+e1wwOwcEopGM6qKcVbfWPRf0sCwBhDSFZgN9IbV9bj9s98ENtbe/Dtx9ui8yIy4PbPLMElS2bp9Mu4Fq88tw7/a9X8qEHKSn9SZbTY0dqDzZPtTYRkKIoCkTH4ZSUa58nJY1FbRzAcxs1rF2NJbaVJVqejrWofD/QO4Y6nnfcTbTnjcTgA8EkMP1jbiCVzIrK80tmPG7e1QWQCZCWMLeuWReu1G1/etUSO+KaaXDJwZaOs2ShToUFzQBAEQRCJY3e0jQxJeYBTjJl449Vo7+W9ZGkxxiAxxzxhuOfKZfjW4+bjI9/71Nn46fNvc2OaaPsDwFEOtxR5BDy96Txu3CE5HLY1imj7unXPUdz2VDs8ogBZUfDNTyzCj549aCrjFRle++6Fuv7wHm55MWd8EsOXVy3Af73yLgQoGA+ld80ywNYQQiTOrzaei3/e+ncEOFn5RAaUeCNGzJsvXYzbn+7gHsFa3zwXa5fOxjW//hs0p6+ixhA3hlotWq+8YCiMtu4hU4wk7b3dg+MIhmTsPTKAf3uhU2eo9EoCnr3hPMtjqdq2uk6Oc2U1Gmg3rqzHxhXzo4alvUdO6tbizZcuxpI5lSbPRKv2f7P3GO7b1QmvGDHQnX/WTJw9qwK/fPnwlKG6eS5+/7duiJOZCLesszZIq+PLu7aqodo2jlMmicSK4hvLso1sNMZlo0yFBs0BQRAEQSQHMiTlMdoHJtUbQOs5owBcjxveQ5bVw1db1ylc/dBejPhD0XZ9IoPCGHyi+UGNdz8ASAJDyOAy45ME/PCzS3DbUx26+8t9Eq5d8wH8+4udUXmu/3gDHnjpsKneeCj3SbjlssWmdt2WffSa5TjQM2TyrpLEiAeKEVEAvnbhIl1/jA+321t7cOPjbSaPH4dKPdIAACAASURBVCL/cWusExlgpx4eAeDZWY1rz8losb21R+eVp2V1QxUeuWaF6XejoceIV2S4+8plJm++rXuP4f5dh+AVRfhDMuSwouujV2T4jw3n4CsP77Osm4dbT0ftvheQZfzDrHK0dg+7boNvkGZ49TsRwzHPYPTAl87FdY/sM/3+++tWRr06M8HAqB/Lf/S8bt4lAdj7vYsybuAy4hRUnWQqTGgOCIIgYoe8OAkrKNh2nsILXA0gahi5cdt+AIopZg7A4A/p4xstnl1hGfeIF3OECQzPbDqP+6XfKm6K0YgERI5oza8qMd0fkMO4f9chnez37epEsnxjguEwmuqm2cZ3sStb6hVx29MdpmseAQjJ5jJyGLh/V6dp3NW4UupckhGpMHE7607qYeWsZ1x7dkGiB0b92LytzdIr7+XOAbQcGTBlb7MzIgGRI5FanTdmSfOH+AbdgKzgnx593bZuHgIDbnuqHQFZsYzlxttD3RqRAEAJK2jlBML3hxT8Zu8xrFk0kxv4e3g8ZPKsnAiGHeNJpZr23mHTvIfCkd+NweAzjVNQdZKpMKE5IAiCiA3y4iTiRci0AET8qA9MVogCg8j010UmQBTMwa7VlyHj7+rD111XLEWRR0C5T0KRR8BdVyxFQ025LmOYivb+Eo/9i5FHYPBIoqn+Tec3wCvqy3pFAZvOXxi9zysyeER9XyRD36b6HfFsMMofldPlC5xXZLjriqUYC8jwiua25DBw0dnmFy6vyLjjrg0ybjeXycAnMvgkAfwRIgoJuyDRkYD49rr40qF+3b9bu065alfVebssaTx4RmgnArICj8jf01QSXXd+WcH8qhIEZLP1+L5dnSj1itzA3xXFEnyG/cMnMl1GuMxgNc7ZZ+BOdpD2ZJCNMhUaNAcEQRDu0X5QG/FHPnJtfmI/Bkb9mRaNyAHIIymHccqYJIcVGF8AZCUMKPoXGCvvHO3D1+VNc7Cqodq126N6f3vvMK59uMXyhVEQIv1YVjdNVz8A3P9ip0meq5bX46rl9dHsYWvv262LxaIGxjZ6ZkgiwzM3rDZ5UE3JOTQpp/ULkzbOy8Con/tye8tljbhkySys/PELOu8ixtT50PdH7StvLj0CIAiC5dh5JQG/+cpHcOj9UXz3Dwe49wARI9ZvrlkOjyRiaDyI67e+HvfxQGOAb62sG5bPw6N7j1m+9C+ZXYoF1WVgYDjQO4zDA6fjksGOD1SXIBgKx5y1bcwfwo/+eJAbrByIHD1jACenlzVWYyUiog9WqiawiB57JQGBUDh6XDUgywgrcBUknSeLKAjwao6iWq3hudOLI/uEDWsWVuv+3WRxHMs4BqrO22VJs8Ijspj6vvmTZ+Gnf3lb95vxhdJpD1Up8QgIKUDYED+tyCPAI4nYdP5C3GNoyysKGAvIuOuKpbpA4nddsRSNtZVggv6cIhNYxl92G2srTePsEVk0c2U2oX6wMI5tJr1OslGmQoPmgCAIwj3kxUkkAhmSchjjAxMvRhIA0wMV7zfVO8fu4auqzBfTplJV5sOaRTOxZV2kXgC64xySAGxZtyxap7F+O3nU/+fdAwDf0mS/UtuxCvQbkfMMbFm3TFcXL9ObWod27EUh8uJ1y2WLsWH5PADA3VcuczXu2v5Y9UX97XQgBMYYfB4BcljBXVcsRfOCKjQvqMKbx4fx8GtTR4sYgDLflB6ox5AGRv2WL87GF0iGiAGuSBIRkMPYdH4Drlpej5+98LaprXvWR7LS3XDhQvxm7zH82/NvR9+RPWIk0LrRTXZHaw++qYnD4xEZPvehWjzW0qO7TzWCqAGOP//hyLwA+sx8ibrinlFR5LiWvv77Vp2xySpmkdrnlqMnTWN17xeaAMC2LaNRVf3vVzr7dTryzYsWYUapFydPB6JGk4lgOCqXNmuaW0NwVZkPW9Yt082NltUNVaaA2w015di4sl7X140r69E8b4alzsd6rFQUGP5zwzn46tbXuQYlUWAo8gi6tThLM6dWe5px3X143nS83DkQvWd981xsWD4vargOGeSeO70YVy2vx32TR1dVVKOV0Uhut3dl+qGtqsyHe65chhu37dcFFM+0XFbE+oGjUGUqNGgOCIIg3EFenEQiULDtPCDVWduSKWOpV0Tv0DgAhsbaCsc23Mhj1b/23iHX7VjV5dR+rCm/46nPaX5VtKnRp5d6Le/TplpXDUSfWjILYwHZND9W7XX2jWB35wlUlxVh5ZlV3L6rWbgaaytt585439a9R3HbUx3wiCxqMDO+FGj1ySkjVyy4WUuvvdOP/tEAltRWwCOJk2M2geHxIACgoljS9dlqrNzOq52MvLWrjkmiYxNL1jYVrQ6qRlcreY16eNHZZ+D5g+9HvaZ4htzLm+aYyl1xzhx8edUCS52PZw/h9cMoszGOgN01uzHOxpfdbJWLIAiCIIjkEs/zC1E4UNY2giB0ZPuLYrbLFw9WxolCxslwm26Ddzwyu71GEARBEASRjdDzC2EFGZKIjEEbExEv+aQ7P3jyDV1Ws40r63H7Zz6YQYkIgiAIgiAIgiCssTMkUYwkImVQOkkiXvJJdzr7RnRGJAB4+LVj2Lhift55JiVi/MsXw2G+9IMgCIIgCIIgrCBDEpEStOkk1UwAm5/Yj1UN1fRyRdiSb7rT2nXK8vd8MiQlYvzLF8NhvvSDIAiCIAiCIOwQMi0AkZ+o6SS1qOkkCcKOfNOdprppMf2ei2iNfyP+ECaCYWx+Yj8GRv0pLZtN5Es/CIIgCIIgCMIJMiQRKYHSSRLxkm+601BTjo0r63W/bVxZn1feSIkY//LFcJgv/SAIgiAIgiAIJ+hoWxaRT7E1qsp8uOuKpaZ0krncr3jmJxfnNNMy56Pu3P6ZD2LjivmusrZlavwTaTcR4182Gg7jGYu504sRkLOrHwRBEARBEASRCsiQlCXkY2yNy5vmYFVDdc4ZUnjEMz+5OKfZInM+6Y5KQ025oxdSpsY/0XYTMf5lm+Ew3rHY3dkPWWMQ84gs4wbQTBuFicKE9I4gCIIg8h+mKEqmZYib5uZmpaWlJdNiJMzAqB+r7tyJieDUS0iRR8ArN11AD2FZQDzzk4tzmosy5xOZGv9ktqu+QJZ6RYwF5Jzz3ot3LHjlfBLDq9+5MOcMYgSRCKR3BEEkSjY8DxAEEYExtk9RlGbeNYqRlAVQbI3sJp75ycU5zUWZ84lMjX8y260q8+HdgTGsvW83rn5oL1bduRM7Wntcl11WNy2jD43xjgWvnFcUM7Z2KPA3kQlI7wiCSJTtrT1YdefOmJ8hCIJIP2RIygKyMUYIMUU885OLc5qLMgORl5e2rlM5/7LidvyT3d9kznuuv0jGOxaZWjtWupCvRuF8Wev5Sr7qHUEQ6SHXnyEIotAgQ1IWoMYIKfIIKPdJKPIIGY+tQUwRz/zk4pzmosz59OXKzfinor/JnPdcf5GMdywysXbsdCFXjcJ25NNaz1fyUe+SDRlDCcKaXH+GIIhCg2IkZRF0Jji7oaxt2UW+xnSyGv9U9zcZ854vcxLvWKRr7bgZ5x2tPaYA5rkaqyZf9KoQyCe9SzYUP4og7KG9niCyD7sYSZS1LYuoKvPRRpnFxDM/uTinuSKz+uVqAppMWZNfrnJBfiusxj/V/U3GvGdbBrZ4iXcs1DLq19NU9duNLuRT5sN8Xev5SDx6lysfLxJBe2RH1ePNT+zHqobqvO0zQcRKvjxDEEShQIYkgiBykkI7RpEr/c0nA0aspMvjwK0u5IpR2Ilc0X0iQix6VyheOmQMJQh3FPIzBEHkGhQjiSCInCQXYzolQi71NxsysKWbdAYJzSVdSAaF1t9CoZAC65IxlCDcU4jPEASRi5BHEkEQOUuhfbkqtP7mEun2OCg0XSi0/hYCheSlQ0d2CIIgiHyDDEkEQeQ0+XJ8xy2F1t9cIRMeB4WmC+nsbyHE7ck0healQ8ZQgiAIIp+go20EQRAEkSB0/Cp/2N7ag1V37sTVD+3Fqjt3YkdrT6ZFyksKcc3QkR2CIAgiX2CKomRahrhpbm5WWlpaMi0GQRAEQQAgT5Zch9JPpx9aMwRBEASRnTDG9imK0sy7Rkfb8hh6OCMIgkgvhXbcLN8opLg92QKtGYIgCILIPciQlKcUSkpdgiAIgkgWhRa3hyAIgiAIIh4oRlIekk0pdQdG/WjrOpUV6XyzSZZUk0hftWULacwIgiAKMW4PQRAEQRBErJBHUh6SLa752eQVlU2ypJpE+qotOxGSoSgKij0SguEwbr50MZbMqXR9VJKOVhIqpAtELkHZtQiCIAiCIOwhQ1IeMnd6McaDId1v48FQWl3ztV5RqkFr8xP7saqhOu0P5dkkS6pJpK+8sgAw4o/o0vefPIAyn4hQWHE0ThWS4Y6wh3SByEUobg9BEARBEIQ1dLQtT2GM2f47WVgdg1K9orQIjKG9dyglctjBk0X10EoWsRwBS+VxsUT6yitrZNQvOx6VdHu0MtZxoGN2uUcsx2yt5tdu3uM9hul0L+kaQRAEQRAEQVhDHkl5SPfgOIokEUF5yiupSBKTfrTN6RiUMWDp6YCMax9uwZZ1y9LqkZDq4KmxeFwk4p3h5nhQIn3llbXC7qikm6OVsY4DebXkJm6P2VrNr9282+0/iazBTOkaHf8jCIIgCIIgcgXySMpD0pF1xuhpEJQVhMKIeh3c8UwHbl67GD5Jr2L+kJL2wN+pDJ4aq8dFvEHQt7f2YNWdO3H1Q3ux6s6d2NHak/S+Gst6RAZJAEq9ouleO31y0r9YxyGbgscTseFmL7Ka386+Ect5d9p/4l2DmdI1t+ubIAiCIAiCILIB8kjKQ1SDwGbDV/VkfuXmeRpo8QgCltRW4sGNzfjqI/twOijrrqU78HeqgqfGEtg83iDoscY9SqSvxrKq3Ad6h3DH0x2u9MlJ/2Idh2wJHk/Ejpu9yGp+W7tOWc67+t92+088azATulZIMdwIgiAIgiCI/IAMSXlKqrPOOB2DUr0O5k4vRhgK91q6SUXw1Fi8v+L1FIvn5TaRvhrLVpX5sKxuGi5pnOVan+z0L9ZxSIeHHZE6nPYiq/ltqptmO+9u9h8jTrqUCV0jQylBEARBEASRa9DRtjxGNQCk4mXE6hiU8ThVKo+VZQOx9C/escgWQ0qs+mR1f6zjkO86VAjY6Y7V/DbUlFvOu9v9x21b6r2Z0LVsWd8EQRAEQRAE4RamKIrzXclskLE6AA8DmAUgDOABRVF+xhi7FcC1AE5M3vo9RVGetaurublZaWlpSaW4hAPaALEALL0O8j2QbCz9i2csdrT2mI4H5Xqw6VjHId91qNCxml+7eXe7/7hty+31ZJOP65sgCIIgCILIbRhj+xRFaeZey4AhaTaA2YqivM4YKwewD8BnAawHMKooyt1u6yJDEuH2hS8fjBC53odcl58gUgmtD4IgCIIgCCKbsDMkpT1GkqIo7wF4b/K/RxhjbwKgT69EzLhN050vqeNTEeMpXeTLHBQqhWbkyER/c3l9EwRBEARBEIVFRmMkMcbmA/gQgL2TP21ijO1njP0XY2x6xgQjsh63abopdXzmoTnIbbItNf3AqB9tXadSpj/Z1l+CSAapXjcEQRAEQRQWGTMkMcbKADwB4OuKogwD+A8AZwJoQsRj6R6LctcxxloYYy0nTpzg3UIUAGqmIy3a1OCx3kekDpqD3CXbjICpNvJkW38JIhmQcZQgCIIgiGSTEUMSY8yDiBFpq6Io/wMAiqL0KYoiK4oSBvAggI/wyiqK8oCiKM2KojTPnDkzfUITWYXbTEeUESnz0BzkLtlkBEyHkSeb+ksQyYCMowRBEARBpIK0G5IYYwzArwC8qSjKTzW/z9bc9jkAB9ItG5E7aNN0l3pFeEWGmy9dnHCqeSL50BxEyMWjJdlkBEyHkSeb+ksQyYCMowRBEARBpIK0B9sGsArAlwC8wRhrnfztewC+yBhrAqAAeBfAP2VANiKHuLxpDkYmQrjt6Q54JQF3PNOB8iLJFMT58qY5WNVQXVDBgrMNpznI92DO2RZs3O14q0ZAY2r6TMxROow82dTfQiPf94BUYjd2ZBwlCIIgCCIVMEVRMi1D3DQ3NystLS2ZFoPIEAOjfqy6cycmglMPyUUeAa/cdAG9iOQQ2WZkSTbZpqfxjHe2vOTvaO0xGXlSoSvZ0t9CId/3gFTiZuzStW4IgiAIgsgvGGP7FEVp5l3LhEcSQSQF1WV/AlMv6KrLPr385Qba+B3qPG5+Yj9WNVTnzRxmk57GO97Zkpo+Xd6F2dLfQqAQ9oBU4XbsyCuXIAiCIIhkk7GsbQSRKOSyHxvZGKOnEOJ3ZJOe5sN4V5X5sKxuGr0M5wn5oJOZIpaxo3VDEARBEEQyIUMSkbNQEGf3ZGv652wysqSKbNLTQhhvIrcgnYwfGjuCIAiCIDIFxUgich6KZ2JPtsXoMVIo8TuyRU8LZbyJ3IF0Mn5o7AiCIAiCSBV2MZLIkEQQeU5b1ylc/dBejPhD0d/KfRIevWY5ltVNy6BkU2SLkaVQoPEmsg3SyfihsSMIgiAIIhVQsG2CKGBy4fgDBTdOLzTeRLZBOhk/NHYEQRAEQaQbipFEEHlONsXoIQiCIAiCIAiCIHIbOtqWBVz333vxUucAJkKRuWAAjvzk0uj1gVE/XnunH6909uPwiVEUeyTMnlaE6aVejIyHsL9nEG3dIxmSniCIbEYAoPVH8wAIuihXLAHjIef7BADK5P94SAwQFCAw+W8RgM8DMDCMBfmlvAyYNb0Ip8YCGPGH4RWBgBxpo9wrIIwwxgNT/ZpbWQSBAceHJnBGhQ+iyPD+8AT8IaCyWMQlS2ZjYGQCrx4+iQqfhLFACMGwgsoiDxgDJoIyBEFARZGEk2N+nJqYGrFLFp+B90cm8P6oH8FQGB5BwBkVRfB5BCyZXYETY0E01lZgZpkXv9p9BB29I5ABVJeI8HolVJd4UeyVUOwRMHtaMYbHg3jr+AhG/CGU+CTIIRnHTvkhAijxiTi7phxNdZVo7RnGRxdMR1ABugZOo76qBAvPKMMzbxzHsYExzK4sxsxyHzreG8boRABeScT/s3I+Zk8rxrbXu1FZ7MGyudPQ0TuMPYcH4JMETCvxYkF1KcaDIZQXeVBZ7MFYIITG2RU4NR7C4fdH0fHeEBbWlOOif6hB1+BpBGUFgVAY86pKsfLMKhw5MYonW3vhERnOnTcDxR4BO986ASUcxvRSL6aXeCGJApbUVsAjiSj1ijh4fARvvjeEEq+Es2eVY/B0ENNLPOgaHEd1mQ8rz6zC4FgArV2n0FQ3DQ015RgY9aO9dxiAghKPiAO9w/BJkW9fJ8cCUXmqynzRo12lXhFjATnqbdneOwSAobG2Qnff3OnFGBwL4M/txwEAyxfMiMqqljca2jv7RnTyAdC12zs0rmtLvf7aOwM4OjBmKW/v0AQABY21lQDAPaJmPLqmyjK/qsQkt7GOyDhGxqG2skjXnnFMrNrXwhtrbX+drhnbsWvT6sie8XfefU79sjsOaFW/nX7YjZVWBisdra0swlhAjrkNq74k67hjIvWkUi67dmLVaW0d8Yx/PHK6kcuNniciQy59TMy03OnSZbu2nXQzlj2tEIi3z4U4Vk5QjKQsZv53nrG89u5PLsX21h584/etCOfuNBEEQRAFiMAQ19+u1Q1VeO3wAEJh+/tEgWHD8jo81tINAJgIhuETGWRFgQIGebJxSQCuWl6Px1q64REEjPpDJsMnQ8RQWeSJGKu0Qat/8OQbeHjPsei9G1fW49x5M3DTE/uhhBX45anaJAH46fomKAC+/rtWXTsMwJdW1OOxfVPyavvCoKDYI+mCZm9v7cFNmmDazfOmY3fnAFduOaxAUabqWH/uXPz2b10IyuZJ8IgMX/xIXXRMJkKyriwvaLcqi9pn7VgpAG56Yr9uHpjAdNc8goDxYAiMMRRJom2bxn5bjcf6c+fisX3duvu07fHa0F53aletn9cvu6Dm2nrUPosCi9YRBiCHFd36kAQgFObroJt23PQvFqzmIN6yyZLLrh2tPrjRaW0dPL1OVuB6rZyxrDU7PY9VtkTmM5NkWu506bJd24D9/mM3Rpkev0wQb58LcazcQIakLOW6/96L5w72297jFRkCnIdAgiAIgiBSg5rZcnAsgIvufcl03e5vs1dkUBQFQQdDmFP7T286D2vv260zOKULY2ZPXvZPFZ/EADD4OZa/iBeZAn/I+TlGbRMAN9Oom/Gwk8VKHrt2nWS18gxwW0+8bVi1w+t/PFlaE8n2mkq5nNqxg9eeXR3Jym7rJGcsay1e2bI9e68VmZY7Xbrstm1ee3ZjBPD30myf90SIV2cyrWvZjJ0hiWIkZZCXNF8VrWCMpUESgiAIgiBUPIKA7sFxtHad4l63+9scuZbY326PIKC16xQ8QmYe09T+q3QPjlvKIjIBosDvrygwiMxdH9Q2eW25HQ87WazksWvXSVYesdQTbxtW7fD671SP27rd1pNKuZzasYPXnl0dicrnpg1eO276FatsicxnJsm03OnSZbdt89qzG6NMj18miLfPhThWyYCytmWQNQ1Vjh5JuewxRhAEQRC5iJrZstQrcq/b/W2OXEvsb3cwHEZT3TRTxs10Yczsycv+qSIrYUDhG28ixwvdjYW2TV6mUTfjYSeLlTx27bqR1YjdWMWCU3ZVXju8/v/f9u4+xs6svg/49zcvGRu86breDYL1bqE4tGoi26ksELVAKGlS0kZL22UjaNIlfVGKGqRGtN0lbaUi/gID/a9NRRKkRA0QEhftqk3zJqIkQgpZL/UalpewYTddG7SAs1vW1J61Pad/zJ1lZnxn5npm7jz35fORLM+c+8y9v+c5zzn3Pr97nnO2s0rrTlZ7HWZcW73OZvq93mbPsVur224V5820te3GNg6r9/bTddx7dS4P+tr9Xm+rYzSO9b4T2z1nuj7XxpURSR360E+9ZtPHn3zv38sH7j2WTb5cA4CRtN33rtcdOZS5AT6dzM5U7nvtXdk3P/PCvCYLs5W5maz5xnhuJi9sd8vCXN+xQitlK8+1srLlkZfckvtee9eabe977V35wL3Hsm9+Jguza59tbib5wL3H8sEfP37D61RyQ7yr92VuJmtW1jzykltuWHHzdUcObRj3/Oza57jvtXdlfrZ/JczP1ppjsv5v16/suXr1z5V9XtmP97/5WN7/5qM31MPyY0fz/jcfe+F15maWX3uz19xopdF+x2P1PqyPpd9rrI9nq9ftd35ttfLp+udZ2efVzzE/Wze0j5Vzfv05uJF+8a7f/+2u0rqT1V6HGddWr3Mz5/T651h/Xu/W6rbr47yZtrbReX6zsY3r6r1dx71X5/JWr71Z/7PZMer6+HVhu/s8jcdqN5gjaQRYtQ0YFqu2WbXNqm1WbbNqm1XbtsOqbbvHqm3b13XcVm0bP1Zt2z0m22bXDTopmcnLGBd78SHNGxQAADAONkskmSOJbdlsksnVF8grk5ddWTUmYmXyMhfSjIrdWPJzq+ewrCgAADAJzJHEtrz4u2ZvWJLyytWlGyYmNXkZo+7ipcU8cPpcrlxdynOL13Ll6lLuP30uFy8t7tpz7MZrAAAAjAKJJLbl289fv2GS0YXZyrefv76mzORljLrdWPJzq+ewrCgAADAp3NrGthw+uD81U8n178yxVTPVd6TR3cfvyMkjt5kbhpG0G6PmtnoOI/MAAIBJYUQS23KzI40OHVjIsTtvlURi5OzGqLmtnsPIPAAAYFJYtY0dsQoVk8KqbQAAAMus2sbQHDqw4IKYibAb5/JWz6G9AAAA486tbQAAAAAMRCIJAAAAgIFIJAEAAAAwEIkkAAAAAAYikQQAAADAQCSSAAAAABiIRBIAAAAAA5FIAgAAAGAgEkkAAAAADEQiCQAAAICBSCQxti5eWsyjTz2bi5cWuw4FdpVzGwAAGFVzXQcA6128tJjzz1zO4YP7c+jAQt9tHjx7IQ+cPpf5mZlcXVrKqXuO5u7jd+xxpLvv4qXFPPbVbyVp+b6X/aUN939UDVJ3bG5Sz20YN/ozAID+JJIYKYNcRF+8tJgHTp/LlatLuZKlJMn9p8/l5JHbxvrD/oNnL+Rff/xsri3vUuZnKx+899jYJBEkQHZuUs9tGDf6MwCAjbm1jZGx+iL6ucVruXJ1KfefPnfD7T3nn7mc+Zm1p+78zEzOP3N5L8PdVRcvLeb+33j0hSRSkly93vJvf+PG/R9Fg9Ydm5vEcxvGjf4MAGBzEkmMjEEvog8f3J+rS0tryq4uLeXwwf1Dj3FYzj9zObN1Y3OcnamxSCJIgOyOSTy3Ydzoz6aDuegAYPskkhgZg15EHzqwkFP3HM2++ZncsjCXffMzOXXP0bG+9efwwf253pZuKL++1MYiiSABsjsm8dyGcaM/m3wPnr2Qk+/7ZH7yFz+dk+/7ZB46e6HrkABgrFRrresYtu3EiRPtzJkzXYfBLnro7IXcP+C8FJM2EepDZy/knWM8R9LN1B2bm7RzG8aN/mxyXby0mJPv+2SuXP1OsnDf/Ew+9cAP6m8BYJWqeqS1dqLvYxJJjJppvoi2att4m/b9H4RjxLhwrk6mR596Nj/5i5/Oc4vXXii7ZWEu/+2fvybH7ry1w8gAYLRslkiyahsj59CBhan90H7owEJe/6rbuw5j26a57qzytDXHiHEyzf3ZJHPrIgDsnDmSAHbIKk9bc4yAUWAuOgDYOSOSAHZoZZWnK/nOt9wrqzy5OFnmGAGj4u7jd+TkkdvcuggA2ySRBLBDbpXYmmMEjBK3LgLA9rm1DWCH3CqxNccIAAAmg1XbYBdY3YfEeTAIxwgAAEafVdtgiKxExQq3SmzNMQIAgPHm1ja4CRcvLebRp559YaUpK1EB/azvKwAAYFIYkcSeGufbWvqNPLr1RfOZqVqzwtbCbQAACwhJREFUnZWo1hrnOoftMEoRAIBJJpHEnhnni6vVI49Wli9/58fPZqYqz19fO8+Ylai+Y5zrHLajX19x/+lzOXnkNolUAAAmglvb2BPjfgvY+WcuZ35mbXO5tpQbkkgLc1aiWjHudQ7b0a+vWBmlCAAAk0AiiT0x7hdXhw/uz9WlpU23edH8bH7hvhNG3PSMe53DdvTrK4xSBABgkkgksSfG/eLq0IGFnLrnaPbNz+SWhbkszFXmZ9fOjbSUlu972Xd3FOHoGfc6h+1Y31fsmzdKEQCAyWKOJPbEysXV/evmyxmni6u7j9+Rk0due2Hi6E89/s2x3p9hm4Q6h+1Y31c45wEAmCTVWtt6qxF14sSJdubMma7D4CZM2gpek7Y/w+AYAQAAjJeqeqS1dqLfY0YksacOHViYqGTCpO3PMDhGAAAAk8McSQAAAAAMRCIJAAAAgIFIJAEAAAAwEIkkAAAAAAYikQQAAADAQCSSAAAAABjIyCWSquqNVfWlqnq8qt7VdTzsnYuXFvPoU8/m4qXFrkMB2BH9GQAAk2qu6wBWq6rZJP85yQ8nOZ/k4ap6qLX2+W4jY9gePHshD5w+l/mZmVxdWsqpe47m7uN3dB0WwE3TnwEAMMlGbUTSq5M83lr7Smvt+SQfS/KmjmNiyC5eWswDp8/lytWlPLd4LVeuLuX+0+d8kw+MHf0ZAACTbtQSSXckeWrV7+d7ZUyw889czvzM2lNxfmYm55+53FFEANujPwMAYNKNWiKp+pS1NRtU/XRVnamqM9/4xjf2KCyG6fDB/bm6tLSm7OrSUg4f3N9RRADboz8DAGDSjVoi6XySO1f9fjjJV1dv0Fr7UGvtRGvtxO23376nwTEchw4s5NQ9R7Nvfia3LMxl3/xMTt1zNIcOLHQdGsBN0Z8BADDpqrW29VZ7pKrmkvxpkh9KciHJw0n+UWvtsX7bnzhxop05c2YPI2SYLl5azPlnLufwwf0uuoCxpj8DAGCcVdUjrbUT/R4bqVXbWmvXquodSX47yWySD2+URGLyHDqw4IILmAj6MwAAJtVIJZKSpLX2m0l+s+s4AAAAAFhr1OZIAgAAAGBESSQBAAAAMBCJJAAAAAAGIpEEAAAAwEAkkgAAAAAYiEQSAAAAAAORSAIAAABgIBJJAAAAAAxEIgkAAACAgUgkAQAAADAQiSQAAAAABiKRBAAAAMBAJJIAAAAAGIhEEgAAAAADkUgCAAAAYCASSQAAAAAMRCIJAAAAgIFIJAEAAAAwEIkkAAAAAAZSrbWuY9i2qvpGkj/vOo5dcluSb3YdBJ1Q99NL3U8vdT+91P30UvfTS91PL3U/vSah7v9Ka+32fg+MdSJpklTVmdbaia7jYO+p++ml7qeXup9e6n56qfvppe6nl7qfXpNe925tAwAAAGAgEkkAAAAADEQiaXR8qOsA6Iy6n17qfnqp++ml7qeXup9e6n56qfvpNdF1b44kAAAAAAZiRBIAAAAAA5FI6lhVvbGqvlRVj1fVu7qOh+Gpqjur6ver6gtV9VhV/ate+bur6kJVne39+7tdx8ruq6onq+qzvTo+0yv7y1X1u1X15d7/B7uOk91VVX9tVds+W1Xfqqqf1e4nU1V9uKq+XlWfW1W2YTuvqp/rvf9/qar+TjdRsxs2qPv3V9UXq+pcVX2iqm7tlb+8qi6vav//tbvI2akN6n7DPl67nxwb1P2vrar3J6vqbK9cu58gm1zXTc17vlvbOlRVs0n+NMkPJzmf5OEkb22tfb7TwBiKqnppkpe21j5TVbckeSTJ30/y40kutdY+0GmADFVVPZnkRGvtm6vKTiX5i9bae3uJ5IOttQe6ipHh6vX5F5K8Jsk/iXY/carq9UkuJfmV1tr398r6tvOq+htJPprk1UleluT3kryqtXa9o/DZgQ3q/keSfLK1dq2q3pckvbp/eZL/sbId422Dun93+vTx2v1k6Vf36x7/YJL/21p7j3Y/WTa5rvupTMl7vhFJ3Xp1ksdba19prT2f5GNJ3tRxTAxJa+1rrbXP9H5+LskXktzRbVR07E1Jfrn38y9n+Q2IyfVDSf6stfbnXQfCcLTW/jDJX6wr3qidvynJx1pri621J5I8nuXPBYyhfnXfWvud1tq13q9/nOTwngfG0G3Q7jei3U+Qzeq+qirLXxZ/dE+DYk9scl03Ne/5EknduiPJU6t+Px+JhanQ+1biB5J8ulf0jt7Q9w+7vWlitSS/U1WPVNVP98pe0lr7WrL8hpTkezqLjr3wlqz9QKndT4eN2rnPANPlnyb5X6t+f0VV/e+q+oOqel1XQTFU/fp47X56vC7J0621L68q0+4n0Lrruql5z5dI6lb1KXOv4YSrqgNJTif52dbat5L8fJJXJjme5GtJPthheAzPydba30zyo0l+pjccmilRVd+V5O4kv94r0u7xGWBKVNW/T3Itya/2ir6W5K7W2g8keWeSj1TVd3cVH0OxUR+v3U+Pt2btl0fa/QTqc1234aZ9ysa67Uskdet8kjtX/X44yVc7ioU9UFXzWe5sfrW19t+TpLX2dGvtemttKckvZMyHOdJfa+2rvf+/nuQTWa7np3v3WK/ca/317iJkyH40yWdaa08n2v2U2aid+wwwBarqbUl+LMlPtN7EpL1bGy72fn4kyZ8leVV3UbLbNunjtfspUFVzSf5hkl9bKdPuJ0+/67pM0Xu+RFK3Hk7yvVX1it631W9J8lDHMTEkvXulfynJF1pr/2lV+UtXbfYPknxu/d8y3qrqxb2J+FJVL07yI1mu54eSvK232duSPNhNhOyBNd9MavdTZaN2/lCSt1TVQlW9Isn3JvmTDuJjSKrqjUkeSHJ3a+3/rSq/vTf5fqrqr2a57r/STZQMwyZ9vHY/Hf52ki+21s6vFGj3k2Wj67pM0Xv+XNcBTLPeKh7vSPLbSWaTfLi19ljHYTE8J5P84ySfXVkKNMm/S/LWqjqe5eGNTyb5F92ExxC9JMknlt9zMpfkI62136qqh5N8vKr+WZL/k+TeDmNkSKrqRVlenXN12z6l3U+eqvpokjckua2qzif5j0nemz7tvLX2WFV9PMnns3zb08+M8+ot026Duv+5JAtJfrfX//9xa+3tSV6f5D1VdS3J9SRvb60NOlkzI2aDun9Dvz5eu58s/eq+tfZLuXFOxES7nzQbXddNzXt+9UbZAgAAAMCm3NoGAAAAwEAkkgAAAAAYiEQSAAAAAAORSAIAAABgIBJJAAAAAAxEIgkAYAeq6taq+pddxwEAsBckkgAAdubWJDckkqpqtoNYAACGSiIJAGBn3pvklVV1tqoerqrfr6qPJPlsVb28qj63smFV/Zuqenfv51dW1W9V1SNV9UdV9dc7ih8AYGBzXQcAADDm3pXk+1trx6vqDUn+Z+/3J6rq5Zv83YeSvL219uWqek2S/5LkB4cdLADATkgkAQDsrj9prT2x2QZVdSDJ30ry61W1Urww7MAAAHZKIgkAYHd9e9XP17J2KoF9vf9nkjzbWju+Z1EBAOwCcyQBAOzMc0lu2eCxp5N8T1UdqqqFJD+WJK21byV5oqruTZJadmxPogUA2AEjkgAAdqC1drGqPtWbVPtylpNHK49drar3JPl0kieSfHHVn/5Ekp+vqv+QZD7Jx5I8uneRAwDcvGqtdR0DAAAAAGPArW0AAAAADEQiCQAAAICBSCQBAAAAMBCJJAAAAAAGIpEEAAAAwEAkkgAAAAAYiEQSAAAAAAORSAIAAABgIP8fSZP0YvJGurAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {'true': y_test_b, 'pred': y_pred_b}\n",
    "results = pd.DataFrame(data)\n",
    "\n",
    "results.plot(figsize=(20,10), kind='scatter', x='true', y='pred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Feature engineering <a id='feature_engineering'></a>\n",
    "[Back to top](#table_of_contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \n",
       "0                 2            0.0        21.800                      1  \n",
       "1                 5            0.0        87.300                      1  \n",
       "2                 6            0.0        80.300                      1  \n",
       "3                 8            0.0       128.580                      2  \n",
       "4                10           16.9        43.245                      4  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taxi_grouped_by_region.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_new_features = taxi_grouped_by_region.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Date-related features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_new_features['transaction_week_day'] = data_with_new_features['transaction_date'].dt.weekday\n",
    "data_with_new_features['weekend'] = data_with_new_features['transaction_week_day'].apply(lambda x: True if x == 5 or x == 6 else False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "\n",
    "cal = USFederalHolidayCalendar()\n",
    "holidays = cal.holidays(start='2018',\n",
    "                        end='2020').date\n",
    "data_with_new_features['is_holiday'] = data_with_new_features['transaction_date'].isin(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "      <th>transaction_week_day</th>\n",
       "      <th>weekend</th>\n",
       "      <th>is_holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 2            0.0        21.800                      1   \n",
       "1                 5            0.0        87.300                      1   \n",
       "2                 6            0.0        80.300                      1   \n",
       "3                 8            0.0       128.580                      2   \n",
       "4                10           16.9        43.245                      4   \n",
       "\n",
       "   transaction_week_day  weekend  is_holiday  \n",
       "0                     1    False        True  \n",
       "1                     1    False        True  \n",
       "2                     1    False        True  \n",
       "3                     1    False        True  \n",
       "4                     1    False        True  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_new_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Borough information**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>LocationID</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LocationID        Borough\n",
       "0          1            EWR\n",
       "1          2         Queens\n",
       "2          3          Bronx\n",
       "3          4      Manhattan\n",
       "4          5  Staten Island"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_lookup = pd.read_csv('data/taxi_zone_lookup.csv')\n",
    "zone_lookup = zone_lookup[['LocationID','Borough']]\n",
    "zone_lookup['LocationID'] = zone_lookup['LocationID'].astype(str)\n",
    "zone_lookup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "      <th>transaction_week_day</th>\n",
       "      <th>weekend</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 2            0.0        21.800                      1   \n",
       "1                 5            0.0        87.300                      1   \n",
       "2                 6            0.0        80.300                      1   \n",
       "3                 8            0.0       128.580                      2   \n",
       "4                10           16.9        43.245                      4   \n",
       "\n",
       "   transaction_week_day  weekend  is_holiday Borough  \n",
       "0                     1    False        True     EWR  \n",
       "1                     1    False        True     EWR  \n",
       "2                     1    False        True     EWR  \n",
       "3                     1    False        True     EWR  \n",
       "4                     1    False        True     EWR  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_new_features = data_with_new_features.merge(zone_lookup, left_on='PULocationID', right_on='LocationID', how='left')\n",
    "data_with_new_features.drop('LocationID', axis=1, inplace=True)\n",
    "data_with_new_features.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manhattan        45309\n",
       "Brooklyn         23633\n",
       "Queens           22002\n",
       "Bronx             9586\n",
       "Unknown           1453\n",
       "Staten Island      302\n",
       "EWR                271\n",
       "Name: Borough, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_new_features['Borough'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Weather related features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>date and time</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>cloud cover</th>\n",
       "      <th>amount of precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.12.2019 22:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>100%.</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.12.2019 19:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>70 – 80%.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.12.2019 16:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>50%.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.12.2019 13:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>100%.</td>\n",
       "      <td>Trace of precipitation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.12.2019 10:00</td>\n",
       "      <td>4.4</td>\n",
       "      <td>83</td>\n",
       "      <td>2</td>\n",
       "      <td>100%.</td>\n",
       "      <td>Trace of precipitation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      date and time  temperature  humidity  wind speed cloud cover  \\\n",
       "0  31.12.2019 22:00          6.1        65           6       100%.   \n",
       "1  31.12.2019 19:00          6.7        71           5   70 – 80%.   \n",
       "2  31.12.2019 16:00          7.2        66           5        50%.   \n",
       "3  31.12.2019 13:00          6.1        76           3       100%.   \n",
       "4  31.12.2019 10:00          4.4        83           2       100%.   \n",
       "\n",
       "  amount of precipitation  \n",
       "0                     0.3  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3  Trace of precipitation  \n",
       "4  Trace of precipitation  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather = pd.read_csv('data/nyc_weather.csv')\n",
    "nyc_weather.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2936, 6)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some missing values and there are percentage values that we cannot work easily with. Let's look at them more closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date and time               object\n",
       "temperature                float64\n",
       "humidity                     int64\n",
       "wind speed                   int64\n",
       "cloud cover                 object\n",
       "amount of precipitation     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70 – 80%.                                                     973\n",
       "100%.                                                         896\n",
       "20–30%.                                                       479\n",
       "50%.                                                          413\n",
       "no clouds                                                     168\n",
       "Sky obscured by fog and/or other meteorological phenomena.      3\n",
       "Name: cloud cover, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather['cloud cover'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trace of precipitation    266\n",
       "0.3                        61\n",
       "2.0                        49\n",
       "1.0                        45\n",
       "0.5                        42\n",
       "0.8                        42\n",
       "4.0                        33\n",
       "5.0                        24\n",
       "3.0                        23\n",
       "6.0                        19\n",
       "7.0                        13\n",
       "8.0                        13\n",
       "9.0                        11\n",
       "10.0                       10\n",
       "12.0                        7\n",
       "13.0                        7\n",
       "15.0                        6\n",
       "11.0                        5\n",
       "16.0                        3\n",
       "14.0                        2\n",
       "29.0                        2\n",
       "22.0                        2\n",
       "18.0                        1\n",
       "24.0                        1\n",
       "25.0                        1\n",
       "20.0                        1\n",
       "21.0                        1\n",
       "68.0                        1\n",
       "30.0                        1\n",
       "34.0                        1\n",
       "63.0                        1\n",
       "17.0                        1\n",
       "35.0                        1\n",
       "Name: amount of precipitation, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather['amount of precipitation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date and time                 0\n",
       "temperature                   0\n",
       "humidity                      0\n",
       "wind speed                    0\n",
       "cloud cover                   4\n",
       "amount of precipitation    2240\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trace of precipitation can be thought of as 0.1<br>\n",
    "And the missing values 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_weather['amount of precipitation'] = nyc_weather['amount of precipitation'].replace('Trace of precipitation', 0.1)\n",
    "nyc_weather['amount of precipitation'] = nyc_weather['amount of precipitation'].astype(float)\n",
    "# option 1\n",
    "# nyc_weather['amount of precipitation'].fillna(0, inplace=True)\n",
    "# option 2\n",
    "#nyc_weather['amount of precipitation'].fillna(method='ffill', inplace=True)\n",
    "# option 3\n",
    "nyc_weather['amount of precipitation'].interpolate(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('70 – 80%.', 0.7)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('100%.', 1)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('20–30%.', 0.3)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('50%.', 0.5)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('no clouds', 0)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].replace('Sky obscured by fog and/or other meteorological phenomena.', 1)\n",
    "nyc_weather['cloud cover'] = nyc_weather['cloud cover'].astype(float)\n",
    "nyc_weather['cloud cover'].interpolate(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date and time               object\n",
       "temperature                float64\n",
       "humidity                     int64\n",
       "wind speed                   int64\n",
       "cloud cover                float64\n",
       "amount of precipitation    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_weather.loc[:,'date and time'] = pd.to_datetime(nyc_weather.loc[:,'date and time'])\n",
    "nyc_weather['hour'] = nyc_weather['date and time'].dt.hour\n",
    "nyc_weather['month'] = nyc_weather['date and time'].dt.month\n",
    "nyc_weather['day'] = nyc_weather['date and time'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date and time</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>cloud cover</th>\n",
       "      <th>amount of precipitation</th>\n",
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       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-12-31 22:00:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-12-31 19:00:00</td>\n",
       "      <td>6.7</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-12-31 16:00:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-12-31 13:00:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-31 10:00:00</td>\n",
       "      <td>4.4</td>\n",
       "      <td>83</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date and time  temperature  humidity  wind speed  cloud cover  \\\n",
       "0 2019-12-31 22:00:00          6.1        65           6          1.0   \n",
       "1 2019-12-31 19:00:00          6.7        71           5          0.7   \n",
       "2 2019-12-31 16:00:00          7.2        66           5          0.5   \n",
       "3 2019-12-31 13:00:00          6.1        76           3          1.0   \n",
       "4 2019-12-31 10:00:00          4.4        83           2          1.0   \n",
       "\n",
       "   amount of precipitation  hour  month  day  \n",
       "0                 0.300000    22     12   31  \n",
       "1                 0.233333    19     12   31  \n",
       "2                 0.166667    16     12   31  \n",
       "3                 0.100000    13     12   31  \n",
       "4                 0.100000    10     12   31  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "      <th>transaction_week_day</th>\n",
       "      <th>weekend</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
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       "      <td>1</td>\n",
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       "      <td>EWR</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 2            0.0        21.800                      1   \n",
       "1                 5            0.0        87.300                      1   \n",
       "2                 6            0.0        80.300                      1   \n",
       "3                 8            0.0       128.580                      2   \n",
       "4                10           16.9        43.245                      4   \n",
       "\n",
       "   transaction_week_day  weekend  is_holiday Borough  \n",
       "0                     1    False        True     EWR  \n",
       "1                     1    False        True     EWR  \n",
       "2                     1    False        True     EWR  \n",
       "3                     1    False        True     EWR  \n",
       "4                     1    False        True     EWR  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_new_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102556, 21)\n"
     ]
    },
    {
     "data": {
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       "      <th>transaction_date</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>EWR</td>\n",
       "      <td>2019-01-01 10:00:00</td>\n",
       "      <td>15.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.473333</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 2            0.0        21.800                      1   \n",
       "1                 5            0.0        87.300                      1   \n",
       "2                 6            0.0        80.300                      1   \n",
       "3                 8            0.0       128.580                      2   \n",
       "4                10           16.9        43.245                      4   \n",
       "\n",
       "   transaction_week_day  weekend  ...  Borough       date and time  \\\n",
       "0                     1    False  ...      EWR                 NaT   \n",
       "1                     1    False  ...      EWR                 NaT   \n",
       "2                     1    False  ...      EWR                 NaT   \n",
       "3                     1    False  ...      EWR                 NaT   \n",
       "4                     1    False  ...      EWR 2019-01-01 10:00:00   \n",
       "\n",
       "  temperature  humidity  wind speed  cloud cover  amount of precipitation  \\\n",
       "0         NaN       NaN         NaN          NaN                      NaN   \n",
       "1         NaN       NaN         NaN          NaN                      NaN   \n",
       "2         NaN       NaN         NaN          NaN                      NaN   \n",
       "3         NaN       NaN         NaN          NaN                      NaN   \n",
       "4        15.6      62.0        11.0          0.7                 0.473333   \n",
       "\n",
       "   hour  month  day  \n",
       "0   NaN    NaN  NaN  \n",
       "1   NaN    NaN  NaN  \n",
       "2   NaN    NaN  NaN  \n",
       "3   NaN    NaN  NaN  \n",
       "4  10.0    1.0  1.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_taxi_with_weather = data_with_new_features.merge(nyc_weather, left_on = ['transaction_month','transaction_day','transaction_hour'], right_on = ['month','day','hour'], how='left')\n",
    "\n",
    "print(nyc_taxi_with_weather.shape)\n",
    "nyc_taxi_with_weather.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_taxi_with_weather = nyc_taxi_with_weather.drop(['date and time','hour','month','day'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>PULocationID</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_month</th>\n",
       "      <th>transaction_day</th>\n",
       "      <th>transaction_hour</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>count_of_transactions</th>\n",
       "      <th>transaction_week_day</th>\n",
       "      <th>weekend</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>Borough</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>cloud cover</th>\n",
       "      <th>amount of precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2019-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.580</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>16.9</td>\n",
       "      <td>43.245</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>EWR</td>\n",
       "      <td>15.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.473333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0            1       2019-01-01                  1                1   \n",
       "1            1       2019-01-01                  1                1   \n",
       "2            1       2019-01-01                  1                1   \n",
       "3            1       2019-01-01                  1                1   \n",
       "4            1       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 2            0.0        21.800                      1   \n",
       "1                 5            0.0        87.300                      1   \n",
       "2                 6            0.0        80.300                      1   \n",
       "3                 8            0.0       128.580                      2   \n",
       "4                10           16.9        43.245                      4   \n",
       "\n",
       "   transaction_week_day  weekend  is_holiday Borough  temperature  humidity  \\\n",
       "0                     1    False        True     EWR          NaN       NaN   \n",
       "1                     1    False        True     EWR          NaN       NaN   \n",
       "2                     1    False        True     EWR          NaN       NaN   \n",
       "3                     1    False        True     EWR          NaN       NaN   \n",
       "4                     1    False        True     EWR         15.6      62.0   \n",
       "\n",
       "   wind speed  cloud cover  amount of precipitation  \n",
       "0         NaN          NaN                      NaN  \n",
       "1         NaN          NaN                      NaN  \n",
       "2         NaN          NaN                      NaN  \n",
       "3         NaN          NaN                      NaN  \n",
       "4        11.0          0.7                 0.473333  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_taxi_with_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PULocationID                   0\n",
       "transaction_date               0\n",
       "transaction_month              0\n",
       "transaction_day                0\n",
       "transaction_hour               0\n",
       "trip_distance                  0\n",
       "total_amount                   0\n",
       "count_of_transactions          0\n",
       "transaction_week_day           0\n",
       "weekend                        0\n",
       "is_holiday                     0\n",
       "Borough                        0\n",
       "temperature                68371\n",
       "humidity                   68371\n",
       "wind speed                 68371\n",
       "cloud cover                68371\n",
       "amount of precipitation    68371\n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_taxi_with_weather.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_taxi_with_weather = nyc_taxi_with_weather.sort_values(['transaction_date','transaction_hour']).reset_index().drop('index',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PULocationID               0\n",
       "transaction_date           0\n",
       "transaction_month          0\n",
       "transaction_day            0\n",
       "transaction_hour           0\n",
       "trip_distance              0\n",
       "total_amount               0\n",
       "count_of_transactions      0\n",
       "transaction_week_day       0\n",
       "weekend                    0\n",
       "is_holiday                 0\n",
       "Borough                    0\n",
       "temperature                0\n",
       "humidity                   0\n",
       "wind speed                 0\n",
       "cloud cover                0\n",
       "amount of precipitation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_taxi_with_weather = nyc_taxi_with_weather.interpolate()\n",
    "nyc_taxi_with_weather = nyc_taxi_with_weather.fillna(method='bfill')\n",
    "nyc_taxi_with_weather.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>weekend</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>Borough</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
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       "      <th>cloud cover</th>\n",
       "      <th>amount of precipitation</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>14.897458</td>\n",
       "      <td>421</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "  PULocationID transaction_date  transaction_month  transaction_day  \\\n",
       "0           10       2019-01-01                  1                1   \n",
       "1          100       2019-01-01                  1                1   \n",
       "2          106       2019-01-01                  1                1   \n",
       "3          107       2019-01-01                  1                1   \n",
       "4           11       2019-01-01                  1                1   \n",
       "\n",
       "   transaction_hour  trip_distance  total_amount  count_of_transactions  \\\n",
       "0                 0       3.020000     14.300000                      2   \n",
       "1                 0       2.801852     18.130000                     54   \n",
       "2                 0       2.593333     15.373333                      3   \n",
       "3                 0       2.437458     14.897458                    421   \n",
       "4                 0       1.795000      9.300000                      2   \n",
       "\n",
       "   transaction_week_day  weekend  is_holiday    Borough  temperature  \\\n",
       "0                     1    False        True     Queens          8.3   \n",
       "1                     1    False        True  Manhattan          8.3   \n",
       "2                     1    False        True   Brooklyn          8.3   \n",
       "3                     1    False        True  Manhattan          8.3   \n",
       "4                     1    False        True   Brooklyn          8.3   \n",
       "\n",
       "   humidity  wind speed  cloud cover  amount of precipitation  \n",
       "0      97.0         0.0          1.0                     29.0  \n",
       "1      97.0         0.0          1.0                     29.0  \n",
       "2      97.0         0.0          1.0                     29.0  \n",
       "3      97.0         0.0          1.0                     29.0  \n",
       "4      97.0         0.0          1.0                     29.0  "
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_taxi_with_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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