02_Fare Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting fare_amount\n",
    "- Attempting to predict the fare amount from the available data such as trip distance, pickup locations, etc\n",
    "- Anaconda python2 environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pygeohash in c:\\users\\msleo\\anaconda2\\lib\\site-packages (1.2.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 18.1, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pygeohash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pygeohash as gh\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\msleo\\anaconda2\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</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>imp_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-07-18 11:25:58</td>\n",
       "      <td>2015-07-18 11:43:47</td>\n",
       "      <td>1</td>\n",
       "      <td>7.21</td>\n",
       "      <td>-73.862762</td>\n",
       "      <td>40.769028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.949203</td>\n",
       "      <td>40.722584</td>\n",
       "      <td>1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-15 12:50:01</td>\n",
       "      <td>2015-03-15 13:23:35</td>\n",
       "      <td>1</td>\n",
       "      <td>10.80</td>\n",
       "      <td>-73.870926</td>\n",
       "      <td>40.773727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.988228</td>\n",
       "      <td>40.765694</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.10</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.3</td>\n",
       "      <td>48.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-30 12:25:44</td>\n",
       "      <td>2015-04-30 13:03:51</td>\n",
       "      <td>1</td>\n",
       "      <td>4.28</td>\n",
       "      <td>-73.978180</td>\n",
       "      <td>40.762341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.008911</td>\n",
       "      <td>40.710789</td>\n",
       "      <td>1</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-05-28 08:47:56</td>\n",
       "      <td>2015-05-28 09:26:08</td>\n",
       "      <td>1</td>\n",
       "      <td>18.47</td>\n",
       "      <td>-73.776711</td>\n",
       "      <td>40.645302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.843422</td>\n",
       "      <td>40.852852</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>0.3</td>\n",
       "      <td>64.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-20 19:36:17</td>\n",
       "      <td>2015-06-20 20:10:49</td>\n",
       "      <td>1</td>\n",
       "      <td>15.50</td>\n",
       "      <td>-73.777054</td>\n",
       "      <td>40.644947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>-73.946800</td>\n",
       "      <td>40.725021</td>\n",
       "      <td>1</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>54.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id      pickup_datetime     dropoff_datetime  passenger_count  \\\n",
       "0          2  2015-07-18 11:25:58  2015-07-18 11:43:47                1   \n",
       "1          1  2015-03-15 12:50:01  2015-03-15 13:23:35                1   \n",
       "2          2  2015-04-30 12:25:44  2015-04-30 13:03:51                1   \n",
       "3          2  2015-05-28 08:47:56  2015-05-28 09:26:08                1   \n",
       "4          1  2015-06-20 19:36:17  2015-06-20 20:10:49                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  rate_code  \\\n",
       "0           7.21        -73.862762        40.769028        1.0   \n",
       "1          10.80        -73.870926        40.773727        NaN   \n",
       "2           4.28        -73.978180        40.762341        NaN   \n",
       "3          18.47        -73.776711        40.645302        NaN   \n",
       "4          15.50        -73.777054        40.644947        NaN   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude  payment_type  \\\n",
       "0                  N         -73.949203         40.722584             1   \n",
       "1                  N         -73.988228         40.765694             1   \n",
       "2                  N         -74.008911         40.710789             1   \n",
       "3                  N         -73.843422         40.852852             1   \n",
       "4                  Y         -73.946800         40.725021             1   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount  imp_surcharge  \\\n",
       "0         22.5    0.0      0.5        4.66          0.00            0.3   \n",
       "1         34.5    0.0      0.5        8.10          5.33            0.3   \n",
       "2         24.5    0.0      0.5        2.50          0.00            0.3   \n",
       "3         51.0    0.0      0.5        7.00          5.54            0.3   \n",
       "4         44.5    0.0      0.5        9.06          0.00            0.3   \n",
       "\n",
       "   total_amount  \n",
       "0         27.96  \n",
       "1         48.73  \n",
       "2         27.80  \n",
       "3         64.34  \n",
       "4         54.36  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df.drop(['Unnamed: 0'], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of trip_distance and fare_amount\n",
    "df[['trip_distance','fare_amount']].hist(figsize = (10,5), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the correlation between trip_distance and fare_amount\n",
    "plt.scatter(df['trip_distance'], df['fare_amount'])\n",
    "plt.xlabel('trip_distance')\n",
    "plt.ylabel('fare_amount');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove those outliers with very low or very high trip_distance or fare_amount \n",
    "df_filtered = df[(df['trip_distance'] >= 2) & (df['trip_distance'] <= 50) & (df['fare_amount'] >= 3) & (df['fare_amount'] <=300)].copy()\n",
    "df_filtered[['trip_distance','fare_amount']].hist(figsize = (10,5), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the correlation between trip_distance and fare_amount to see if outliers are removed\n",
    "plt.scatter(df_filtered['trip_distance'], df_filtered['fare_amount'])\n",
    "plt.xlabel('trip_distance')\n",
    "plt.ylabel('fare_amount');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate trip_duration and year\n",
    "df_filtered['pickup_datetime'] = pd.to_datetime(df_filtered['pickup_datetime'])\n",
    "df_filtered['dropoff_datetime'] = pd.to_datetime(df_filtered['dropoff_datetime'])\n",
    "df_filtered['trip_duration'] = (df_filtered['dropoff_datetime']-df_filtered['pickup_datetime']).dt.total_seconds()/60\n",
    "df_filtered['year'] = df_filtered['pickup_datetime'].dt.year\n",
    "df_filtered[['trip_duration', 'year']].hist(figsize = (10,5), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove those outliers with very low or very high trip_duration \n",
    "df_filtered = df_filtered[(df_filtered['trip_duration'] <= 200) & (df_filtered['trip_duration'] > 1)].copy()\n",
    "df_filtered[['trip_duration', 'year']].hist(figsize = (10,5), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the correlation between trip_duration and fare_amount to see if outliers are removed\n",
    "plt.scatter(df_filtered['trip_duration'], df_filtered['fare_amount'])\n",
    "plt.xlabel('trip_duration')\n",
    "plt.ylabel('fare_amount');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some helper functions\n",
    "def train_test_split(X, y):\n",
    "    \"\"\"Split X and y into training set and testing set.\n",
    "    Data from year = 2015 is used as training set while data from year = 2016 is used as testing set.\n",
    "    Returns X_train, y_train, X_test, y_test\n",
    "    \"\"\"\n",
    "    \n",
    "    X_train = X.loc[X['year'] == 2015].drop('year', axis=1)\n",
    "    y_train = y.loc[y['year'] == 2015].drop('year', axis=1).values.ravel()\n",
    "    X_test = X.loc[X['year'] == 2016].drop('year', axis=1)\n",
    "    y_test = y.loc[y['year'] == 2016].drop('year', axis=1).values.ravel()\n",
    "    return X_train, y_train, X_test, y_test\n",
    "  \n",
    "def model_results(X_train, y_train, X_test, y_test, model):\n",
    "    \"\"\"Print model parameters of RMSE and R-square on training and testing sets.\n",
    "    \"\"\"\n",
    "    \n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    print(\"----Training Data results (2015 data set)----\")\n",
    "    print(\"RMSE: ${:.1f}\".format(mean_squared_error(y_train, y_train_pred)**0.5))\n",
    "    print(\"R2: {:.2f}\\n\".format(r2_score(y_train, y_train_pred)))\n",
    "    \n",
    "    print(\"----Test Data results (2016 data set)----\")\n",
    "    print(\"RMSE: ${:.1f}\".format(mean_squared_error(y_test, y_test_pred)**0.5))\n",
    "    print(\"R2: {:.2f}\\n\".format(r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Train and Test sets\n",
    "y = df_filtered[['fare_amount', 'year']]\n",
    "X = df_filtered[['trip_distance', 'trip_duration', 'year']]\n",
    "X_train, y_train, X_test, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>trip_distance</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.21</td>\n",
       "      <td>17.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.80</td>\n",
       "      <td>33.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.28</td>\n",
       "      <td>38.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.47</td>\n",
       "      <td>38.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.50</td>\n",
       "      <td>34.533333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_distance  trip_duration\n",
       "0           7.21      17.816667\n",
       "1          10.80      33.566667\n",
       "2           4.28      38.116667\n",
       "3          18.47      38.200000\n",
       "4          15.50      34.533333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Intuitive model\n",
    "- Intuitively, the fare amount depends on distance traveled as well as travel time\n",
    "- Build a simple bivariate linear model to include these 2 features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Data results (2015 data set)----\n",
      "RMSE: $5.6\n",
      "R2: 0.79\n",
      "\n",
      "----Test Data results (2016 data set)----\n",
      "RMSE: $5.5\n",
      "R2: 0.81\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Simple bivariate model\n",
    "lm = LinearRegression().fit(X_train,y_train)\n",
    "model_results(X_train, y_train, X_test, y_test, lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Intuitive model results\n",
    "- Simple bivariate linear model has a high RMSE as 5.5\n",
    "- Try multivariate linear model to reduce the RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "- Thinking deeper about the problem\n",
    "    - Pickup day of the week and hour of the day could play an important role in demand and traffic conditions leading to an impact on fare\n",
    "    - Latitude difference and Longitude difference could be an additional data to augment trip_distance's impact on fare\n",
    "    - Pickup and dropoff neighbourhoods could have an effect on fare due to additional fees for specific locations?\n",
    "\n",
    "\n",
    "- Add in features as below\n",
    "    1. Pickup month\n",
    "    2. Pickup day of the week\n",
    "    3. Pickup hour of the day\n",
    "    4. Lattitude difference\n",
    "    5. Longitude difference\n",
    "    6. Geohashed pickup location (idea from internet)\n",
    "    7. Geohashed dropoff location (idea from internet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>lat_dif</th>\n",
       "      <th>lon_dif</th>\n",
       "      <th>pickup_geohash</th>\n",
       "      <th>dropoff_geohash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-07-18 11:25:58</td>\n",
       "      <td>2015-07-18 11:43:47</td>\n",
       "      <td>1</td>\n",
       "      <td>7.21</td>\n",
       "      <td>-73.862762</td>\n",
       "      <td>40.769028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.949203</td>\n",
       "      <td>...</td>\n",
       "      <td>17.816667</td>\n",
       "      <td>2015</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.046444</td>\n",
       "      <td>0.086441</td>\n",
       "      <td>dr5rz</td>\n",
       "      <td>dr5rt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-03-15 12:50:01</td>\n",
       "      <td>2015-03-15 13:23:35</td>\n",
       "      <td>1</td>\n",
       "      <td>10.80</td>\n",
       "      <td>-73.870926</td>\n",
       "      <td>40.773727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.988228</td>\n",
       "      <td>...</td>\n",
       "      <td>33.566667</td>\n",
       "      <td>2015</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>12</td>\n",
       "      <td>0.008034</td>\n",
       "      <td>0.117302</td>\n",
       "      <td>dr5rz</td>\n",
       "      <td>dr5ru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-30 12:25:44</td>\n",
       "      <td>2015-04-30 13:03:51</td>\n",
       "      <td>1</td>\n",
       "      <td>4.28</td>\n",
       "      <td>-73.978180</td>\n",
       "      <td>40.762341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.008911</td>\n",
       "      <td>...</td>\n",
       "      <td>38.116667</td>\n",
       "      <td>2015</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>12</td>\n",
       "      <td>0.051552</td>\n",
       "      <td>0.030731</td>\n",
       "      <td>dr5ru</td>\n",
       "      <td>dr5re</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-05-28 08:47:56</td>\n",
       "      <td>2015-05-28 09:26:08</td>\n",
       "      <td>1</td>\n",
       "      <td>18.47</td>\n",
       "      <td>-73.776711</td>\n",
       "      <td>40.645302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.843422</td>\n",
       "      <td>...</td>\n",
       "      <td>38.200000</td>\n",
       "      <td>2015</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.207550</td>\n",
       "      <td>0.066711</td>\n",
       "      <td>dr5x1</td>\n",
       "      <td>dr72r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-06-20 19:36:17</td>\n",
       "      <td>2015-06-20 20:10:49</td>\n",
       "      <td>1</td>\n",
       "      <td>15.50</td>\n",
       "      <td>-73.777054</td>\n",
       "      <td>40.644947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>-73.946800</td>\n",
       "      <td>...</td>\n",
       "      <td>34.533333</td>\n",
       "      <td>2015</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>19</td>\n",
       "      <td>-0.080074</td>\n",
       "      <td>0.169746</td>\n",
       "      <td>dr5x1</td>\n",
       "      <td>dr5rt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id     pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "0          2 2015-07-18 11:25:58 2015-07-18 11:43:47                1   \n",
       "1          1 2015-03-15 12:50:01 2015-03-15 13:23:35                1   \n",
       "2          2 2015-04-30 12:25:44 2015-04-30 13:03:51                1   \n",
       "3          2 2015-05-28 08:47:56 2015-05-28 09:26:08                1   \n",
       "4          1 2015-06-20 19:36:17 2015-06-20 20:10:49                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  rate_code  \\\n",
       "0           7.21        -73.862762        40.769028        1.0   \n",
       "1          10.80        -73.870926        40.773727        NaN   \n",
       "2           4.28        -73.978180        40.762341        NaN   \n",
       "3          18.47        -73.776711        40.645302        NaN   \n",
       "4          15.50        -73.777054        40.644947        NaN   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  ...  trip_duration  year  day  month  \\\n",
       "0                  N         -73.949203  ...      17.816667  2015   18      7   \n",
       "1                  N         -73.988228  ...      33.566667  2015   15      3   \n",
       "2                  N         -74.008911  ...      38.116667  2015   30      4   \n",
       "3                  N         -73.843422  ...      38.200000  2015   28      5   \n",
       "4                  Y         -73.946800  ...      34.533333  2015   20      6   \n",
       "\n",
       "   day_of_week  hour_of_day   lat_dif   lon_dif  pickup_geohash  \\\n",
       "0     Saturday           11  0.046444  0.086441           dr5rz   \n",
       "1       Sunday           12  0.008034  0.117302           dr5rz   \n",
       "2     Thursday           12  0.051552  0.030731           dr5ru   \n",
       "3     Thursday            8 -0.207550  0.066711           dr5x1   \n",
       "4     Saturday           19 -0.080074  0.169746           dr5x1   \n",
       "\n",
       "   dropoff_geohash  \n",
       "0            dr5rt  \n",
       "1            dr5ru  \n",
       "2            dr5re  \n",
       "3            dr72r  \n",
       "4            dr5rt  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new features as mentioned above\n",
    "df_filtered['day'] = df_filtered['pickup_datetime'].dt.day\n",
    "df_filtered['month'] = df_filtered['pickup_datetime'].dt.month\n",
    "df_filtered['day_of_week'] = df_filtered['pickup_datetime'].dt.weekday_name\n",
    "df_filtered['hour_of_day'] = df_filtered['pickup_datetime'].dt.hour\n",
    "df_filtered['lat_dif'] = df_filtered['pickup_latitude'] - df_filtered['dropoff_latitude']\n",
    "df_filtered['lon_dif'] = df_filtered['pickup_longitude'] - df_filtered['dropoff_longitude']\n",
    "df_filtered['pickup_geohash']=df_filtered.apply(lambda x: gh.encode(x.pickup_latitude, x.pickup_longitude, precision=5), axis=1)\n",
    "df_filtered['dropoff_geohash']=df_filtered.apply(lambda x: gh.encode(x.dropoff_latitude, x.dropoff_longitude, precision=5), axis=1)\n",
    "\n",
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179231, 29)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(135568, 29)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null values from lattitude and longitude\n",
    "df_filtered = df_filtered[df_filtered['lat_dif'].notnull() & df_filtered['lon_dif'].notnull()].copy()\n",
    "df_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vendor_id                 0\n",
       "pickup_datetime           0\n",
       "dropoff_datetime          0\n",
       "passenger_count           0\n",
       "trip_distance             0\n",
       "pickup_longitude          0\n",
       "pickup_latitude           0\n",
       "rate_code             45595\n",
       "store_and_fwd_flag        0\n",
       "dropoff_longitude         0\n",
       "dropoff_latitude          0\n",
       "payment_type              0\n",
       "fare_amount               0\n",
       "extra                     0\n",
       "mta_tax                   0\n",
       "tip_amount                0\n",
       "tolls_amount              0\n",
       "imp_surcharge             0\n",
       "total_amount              0\n",
       "trip_duration             0\n",
       "year                      0\n",
       "day                       0\n",
       "month                     0\n",
       "day_of_week               0\n",
       "hour_of_day               0\n",
       "lat_dif                   0\n",
       "lon_dif                   0\n",
       "pickup_geohash            0\n",
       "dropoff_geohash           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split response (y) and features (X)\n",
    "y = df_filtered[['fare_amount', 'year']]\n",
    "X = df_filtered[['passenger_count', 'trip_distance', 'trip_duration', 'year', 'month', 'day_of_week', 'hour_of_day', 'lat_dif', 'lon_dif', 'pickup_geohash']]\n",
    "X_encoded = pd.get_dummies(X, columns=['month', 'day_of_week', 'hour_of_day', 'pickup_geohash'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TbmZmZjaoVNMrU2TznK2KiEtyqxYCpZ6V04Cbc+mnpN6ZE4Hn0i3PxcDRkkakHpxHA4vTuhckTUzHOiW3LzMzM7NBo5pbmUcCJwMrJN2f0s4GLgIWSDodeBw4Ia1bBBwHrAFeAk4DiIjNks4H7k75zouIzWn508BVwK5kvTHdI9PMzMwGnR4Ds4j4OeXbgQEcVSZ/ADMq7GsuMLdM+j3AQT2VxczMzGwg88j/ZmZmZgXhwMzMzMysIByYmZmZmRWEAzMzMzOzgnBgZlYDSXMlbZL0YC5tb0lLJK1Of0ekdEm6XNIaSQ9IOjS3zbSUf7Wkabn0wyStSNtcnoaQqXgMs6JxHTHrGwdmZrW5CpjcJW0WcHtEjANuT88BjgXGpcd04ArIvkCAc4AjgMOBc3JfIlekvKXtJvdwDLOiuQrXEbNec2BmVoOIuBPY3CV5CjAvLc8Djs+lXx2ZpcDwNEvGMcCSiNgcEVuAJcDktG7PiPhFGnbm6i77KncMs0JxHTHrm5omMTezstrSDBZExAZJ+6X0kcC6XL6OlNZdekeZ9O6OsR1J08muJtDW1tbj5OzlJoJvhkaUo9YJ7dt2zbZp9usxQN+TwtQRqL2eNFpR3vNqlepWqc5UqyjnWPTX24GZWeOUG5g5epFetYiYA8wBmDBhQvQ0sX25ieCboRHlqHVC+5kHd3LxiqFNn9B+IL8nZfR7HYHa60mjFeU9r1apbpXqTLWaXbdKiv56+1amWd9tTLdYSH83pfQOYHQu3yhgfQ/po8qkd3cMs1bgOmJWJQdmZn23ECj1GpsG3JxLPyX1PJsIPJdutSwGjpY0IjVoPhpYnNa9IGli6ml2Spd9lTuGWStwHTGrkm9lmtVA0rXAJGBfSR1kPccuAhZIOh14HDghZV8EHAesAV4CTgOIiM2SzgfuTvnOi4hSY+lPk/Vq2xW4LT3o5hhmheI6YtY3DszMahARJ1ZYdVSZvAHMqLCfucDcMun3AAeVSX+m3DHMisZ1xKxvfCvTzMzMrCAcmJmZmZkVhAMzMzMzs4JwYGZmZmZWEA7MzMzMzArCgZmZmZlZQTgwMzMzMysIB2ZmZmZmBdFjYCZprqRNkh7MpZ0r6QlJ96fHcbl1syWtkfSIpGNy6ZNT2hpJs3LpYyUtk7Ra0nWSdq7nCZqZmZm1imqumF0FTC6TfmlEHJIeiwAkjQemAgembb4paYikIcA3gGOB8cCJKS/Al9O+xgFbgNP7ckJmZmZmrarHwCwi7gQ295QvmQLMj4iXI+IxsvnPDk+PNRHxaES8AswHpqRJaN8LXJ+2nwccX+M5mJmZmQ0IfZkr8wxJpwD3ADMjYgswEliay9OR0gDWdUk/AtgHeDYiOsvk34Gk6cB0gLa2Ntrb28vm27p1a8V1RbLiied6td3BI/faIa2R5zzz4M6eM5XR6Peg3DkXtaxmZmbV6G1gdgVwPhDp78XAJwCVyRuUvzIX3eQvKyLmAHMAJkyYEJMmTSqbr729nUrriuTUWbf2aru1J03aIa2R51zPctZTuXMualnNzMyq0avALCI2lpYlfQe4JT3tAEbnso4C1qflculPA8MlDU1XzfL5zczMzAaVXg2XIWn/3NMPA6UemwuBqZJ2kTQWGAfcBdwNjEs9MHcm6yCwMCICuAP4SNp+GnBzb8pkZmZm1up6vGIm6VpgErCvpA7gHGCSpEPIbjuuBT4JEBErJS0AHgI6gRkR8VrazxnAYmAIMDciVqZDnAXMl3QBcB9wZd3OzszMzKyF9BiYRcSJZZIrBk8RcSFwYZn0RcCiMumPkvXaNDMzMxvUPPK/WR1IeltuwOX7JT0v6bMejNks4zpiVp2+DJdRWCueeK5XvfPWXvSBBpTGBoOIeAQ4BCANqPwEcBNwGtkAyl/N5+8yGPMBwE8lvTWt/gbwfrLONHdLWhgRD7FtMOb5kr5FNhjzFQ0/ObM6cB0xq46vmJnV31HAryPiN93k8WDMNpi5jphVMCCvmFkxjOntmGKtf+VyKnBt7nm/DcZc7SDMJUUZjLkR5ah1sOG2XbNtmv16DOT3JKdpdQRqryeNVpT3vFqlulWqM9UqyjkW/fV2YGZWR6lNy4eA2SmpXwdjrnYQ5pKiDMbciHLU2pxh5sGdXLxiaNMHGx7I7wk0v45A7fWk0YrynlerVLdKdaZaza5bJUV/vR2YWeFUe6Vt5sGdvR7pv4GOBe4tDcLswZjNduA6YtYNB2aDSG9vLVpNTiR3i0bS/hGxIT3tOhjzDyVdQtawuTQYs0iDMZM1jp4KfDwiQlJpMOb5eDBma12uI2bdcGBmVieSdiPrKfbJXPJXPBizWcZ1xKxnDszM6iQiXiJrgJxPO7mb/B6M2QYV1xGznnm4DDMzM7OCcGBmZmZmVhC+ldmCyjXiL2gPRTMzM6uBr5iZmZmZFYQDMzMzM7OCcGBmZmZmVhAOzMzMzMwKwoGZmZmZWUE4MDMzMzMrCAdmZmZmZgXhwMzMzMysIByYmZmZmRVEj4GZpLmSNkl6MJe2t6QlklanvyNSuiRdLmmNpAckHZrbZlrKv1rStFz6YZJWpG0ul6R6n6SZmZlZK6jmitlVwOQuabOA2yNiHHB7eg5wLDAuPaYDV0AWyAHnAEcAhwPnlIK5lGd6bruuxzIzMzMbFHoMzCLiTmBzl+QpwLy0PA84Ppd+dWSWAsMl7Q8cAyyJiM0RsQVYAkxO6/aMiF9ERABX5/ZlZmZmNqj0dhLztojYABARGyTtl9JHAuty+TpSWnfpHWXSy5I0nezqGm1tbbS3t5cv3K7ZpN61qrS/RulNGSvp7Tm3snqec3+/92ZmZuX0NjCrpFz7sOhFelkRMQeYAzBhwoSYNGlS2Xz/fs3NXLyi9lNbe1L5/TXKqbNurdu+Zh7c2atzbmX1POf+fu/NzMzK6W2vzI3pNiTp76aU3gGMzuUbBazvIX1UmXQzMzOzQae3gdlCoNSzchpwcy79lNQ7cyLwXLrluRg4WtKI1Oj/aGBxWveCpImpN+YpuX2ZtRRJa1MP4/sl3ZPS3IPZLHEdMetZNcNlXAv8AnibpA5JpwMXAe+XtBp4f3oOsAh4FFgDfAf4O4CI2AycD9ydHuelNIBPA99N2/wauK0+p2bWFO+JiEMiYkJ67h7MZttzHTHrRo8NdCLixAqrjiqTN4AZFfYzF5hbJv0e4KCeymHWoqYAk9LyPKAdOItcD2ZgqaRSD+ZJpB7MAJJKPZjbST2YU3qpB7N/yFircx0xyxlcrcXNGiuAn0gK4Nups0q/9mCutudyydatWwvRI7UR5ai1x26pl2+zX4+B/J5QgDoCtdeTRivKe16tUt2qtWd8Uc6x6K+3AzOz+jkyItanL5Ylkh7uJm9DejBX23O5pL29nZ7y9IdGlKPWXs+lXr7N7qE7kN8TClBHoPZ60mhFec+rVapbtfaMb3bdKin66+25Ms3qJCLWp7+bgJvI2r+4B7NZ4jpi1jMHZmZ1IGl3SXuUlsl6Hj+IezCbAa4jZtXyrUyz+mgDbkq984cCP4yIH0u6G1iQejM/DpyQ8i8CjiPrjfwScBpkPZgllXoww449mK8CdiVr0OxGzdZKXEfMquDAzKwOIuJR4J1l0p/BPZjNXEfMquRbmWZmZmYF4cDMzMzMrCAcmJmZmZkVhAMzMzMzs4JwYGZmZmZWEA7MzMzMzArCgZmZmZlZQTgwMzMzMysIB2ZmZmZmBeHAzMzMzKwgHJiZmZmZFYQDMzMzM7OCcGBmZmZmVhAOzMzMzMwKok+BmaS1klZIul/SPSltb0lLJK1Of0ekdEm6XNIaSQ9IOjS3n2kp/2pJ0/p2SmZmZmataWgd9vGeiHg693wWcHtEXCRpVnp+FnAsMC49jgCuAI6QtDdwDjABCGC5pIURsaUOZavJmFm39mq7tRd9oM4lMTMzs8GoHoFZV1OASWl5HtBOFphNAa6OiACWShouaf+Ud0lEbAaQtASYDFzbgLI1RG8DOjMzM7O8vgZmAfxEUgDfjog5QFtEbACIiA2S9kt5RwLrctt2pLRK6TuQNB2YDtDW1kZ7e3vZQrXtCjMP7uztObUkn3PfVPosmZmZ9ae+BmZHRsT6FHwtkfRwN3lVJi26Sd8xMQv85gBMmDAhJk2aVPZA/37NzVy8ohEXA4tr5sGdPuc+WHvSpF5vK2k0cDXwBuD3wJyIuEzSucDfAk+lrGdHxKK0zWzgdOA14DMRsTilTwYuA4YA342Ii1L6WGA+sDdwL3ByRLzS60Kb9TPXE7Pq9Knxf0SsT383ATcBhwMb0y1K0t9NKXsHMDq3+ShgfTfpZq2iE5gZEe8AJgIzJI1P6y6NiEPSo/RlMx6YChxIdtv+m5KGSBoCfIOsPeZ44MTcfr6c9jUO2EL2ZWXWSlxPzKrQ68BM0u6S9igtA0cDDwILgVLPymnAzWl5IXBK6p05EXgu3fJcDBwtaUTqwXl0SjNrCRGxISLuTcsvAKuocDs+mQLMj4iXI+IxYA3Zj5rDgTUR8Wj6lT8fmCJJwHuB69P284DjG3M2Zo3hemJWnb7cB2oDbsrqAkOBH0bEjyXdDSyQdDrwOHBCyr8IOI6scr0EnAYQEZslnQ/cnfKdV+oIYNZqJI0B3gUsA44EzpB0CnAP2dWCLWRfRktzm+XbVXZtb3kEsA/wbER0lsnf9fhVtcMs2bp1ayHa1zWiHLW2Pyy1WWz26zGQ35OSVqsnjVaU97xapbpVazvfopxj0V/vXgdmEfEo8M4y6c8AR5VJD2BGhX3NBeb2tixmRSBpGHAD8NmIeF7SFcD5ZG0mzwcuBj5B5XaV5a5gN6QdZkl7ezs95ekPjSjHqTX2li61WexLe8N6GMjvCbRmPWm0orzn1SrVrVrb+Ta7bpUU/fUeXK3FzRpE0k5kXzbXRMSNABGxMbf+O8At6Wl37SrLpT8NDJc0NF0NcDtMa0muJ2Y985RMZn2U2rZcCayKiEty6fvnsn2YrA0mZO0tp0raJfUiGwfcRXY7f5yksZJ2Jmv4vDBdbb4D+EjaPt9206wluJ6YVcdXzMz67kjgZGCFpPtT2tlkvcUOIbudshb4JEBErJS0AHiIrKfajIh4DUDSGWSdX4YAcyNiZdrfWcB8SRcA95F9wZm1EtcTsyo4MDPro4j4OeXbtyzqZpsLgQvLpC8qt11q03l4H4pp1lSuJ2bV8a1MMzMzs4JwYGZmZmZWEA7MzMzMzArCgZmZmZlZQTgwMzMzMysIB2ZmZmZmBeHAzMzMzKwgHJiZmZmZFYQDMzMzM7OCcGBmZmZmVhAOzMzMzMwKwoGZmZmZWUE4MDMzMzMrCAdmZmZmZgXhwMzMzMysIByYmZmZmRWEAzMzMzOzgihMYCZpsqRHJK2RNKvZ5TErItcTs+65jlirK0RgJmkI8A3gWGA8cKKk8c0tlVmxuJ6Ydc91xAaCQgRmwOHAmoh4NCJeAeYDU5pcJrOicT0x657riLW8oc0uQDISWJd73gEc0TWTpOnA9PR0q6RHKuxvX+Dpupaw4D7jc+4Tfbnb1W+qxzHqoMd6UkMdKSnK56bp5Sh9nnr4LPSHpr8WSS3laJk6Ar2qJ41WlPe8JrX+Dy5A3Spp1utdVT0pSmCmMmmxQ0LEHGBOjzuT7omICfUoWKvwOQ8KPdaTauvIH3ZYkNewCOUoQhlcjj6r63dJf2nR19rlbpCi3MrsAEbnno8C1jepLGZF5Xpi1j3XEWt5RQnM7gbGSRoraWdgKrCwyWUyKxrXE7PuuY5YyyvErcyI6JR0BrAYGALMjYiVfdhlYS5R9yOf8wDXgHoCxXkNi1COIpQBXI5ea1Ad6Q8t91onLncDKGKH2+9mZmZm1gRFuZVpZmZmNug5MDMzMzMriAEVmA2WqTgkzZW0SdKDubS9JS2RtDr9HdHMMtabpNGS7pC0StJKSWem9AF93o0m6Z8khaR903NJujzVoQckHdrAY/9fSQ+n49wkaXhu3exUhkckHdOoMuSO15T/HUX6XEsaIuk+Sbek52MlLUtluC41prc6knSupCck3Z8ex+XW9WsdqFUrfd9KWitpRXqN70lphf3uGDCB2SCbiuMqYHKXtFnA7RExDrg9PR9IOoGZEfHetsZPAAAgAElEQVQOYCIwI72/A/28G0bSaOD9wOO55GOBcekxHbiigUVYAhwUEX8C/AqYnco1nqw33YFkn/NvpvrdEE3+31Gkz/WZwKrc8y8Dl6YybAFO74cyDEaXRsQh6bEI+r8O1KpFv2/fk17j0vhlhf3uGDCBGYNoKo6IuBPY3CV5CjAvLc8Dju/XQjVYRGyIiHvT8gtkXyAjGeDn3WCXAp9j+wE4pwBXR2YpMFzS/o04eET8JCI609OlZGNOlcowPyJejojHgDVk9btRmva/oyifa0mjgA8A303PBbwXuL6/ymDb6e86UKuB8H1b2O+OgRSYlZuKY2STytIMbRGxAbJ/9sB+TS5Pw0gaA7wLWMYgOu96kvQh4ImI+GWXVc2qR58AbmtSGQrxv6PJn+uvkQXpv0/P9wGezQXOg+3/aX86I93On5u7nVaIz2Q3il6+rgL4iaTlyqbjggJ/dxRiHLM6qWoqDmttkoYBNwCfjYjnsx/2Vo6knwJvKLPq88DZwNHlNiuT1ut61F0ZIuLmlOfzZLf0rmlEGarQ9P8dzfxcS/ogsCkilkuaVEouk9X/T3uhh3p4BXA+2Wt7PnAx2Y+Uor/+RS9fV0dGxHpJ+wFLJD3c7AJ1ZyAFZoN9Ko6NkvaPiA3p1tOmZheo3iTtRPbldU1E3JiSB/x591ZEvK9cuqSDgbHAL1MAMAq4V9Lh1LkeVSpDrizTgA8CR8W2QRX7uy439X9HAT7XRwIfSg3PXwfsSXYFbbikoemq2WD7f1o3PdWBEknfAW5JT4v+fVb08m0nItanv5sk3UR2K7aw3x0D6VbmYJ+KYyEwLS1PA25uYlnqLrV5uRJYFRGX5FYN6PNuhIhYERH7RcSYiBhD9k/20Ih4kuz1PCX1zpwIPFe63F9vkiYDZwEfioiXcqsWAlMl7SJpLFlHhLsaUYakaf87ivC5jojZETEqfRamAj+LiJOAO4CP9EcZBqsu7Tc/DJR62vd3HahVy3zfStpd0h6lZbI7BQ9S4O+OAXPFrIWn4qiZpGuBScC+kjqAc4CLgAWSTifrZXdC80rYEEcCJwMrJN2f0s5m4J93f1sEHEfW2Pgl4LQGHuvrwC5ktxYAlkbEpyJipaQFwENktzhnRMRrjSpEk/93FPlzfRYwX9IFwH1kAaTV11ckHUJ2G3At8EmA/q4DtWqx79s24Kb0P2Yo8MOI+LGku2l+HSvLUzKZmZmZFcRAupVpZmZm1tIcmJmZmZkVhAMzMzMzs4JwYGZmZmZWEA7MzMzMzArCgZmZmZlZQTgwMzMzMysIB2ZmZmZmBeHAzMzMzKwgHJiZmZmZFYQDMzMzM7OCcGBmZmZmVhAOzMzMzMwKwoGZVSTpXEk/aHY5zMysPElvlLRV0pA673etpPel5bMlfbee+7fKHJgZAJImSepodjnMrPckhaS3NLscVj/5AKmciHg8IoZFxGuNKkNEfDEi/qanfJLaJfWYz7rnwMzMrBckDW12GWxw82dwYHJg1gLSL6Z/lvSApBclXSmpTdJtkl6Q9FNJI1LeD0laKenZ9OvlHV32809pP89Juk7S6yTtDtwGHJAuiW+VdEDabGdJV6fjrJQ0oQkvgbWA9PmaLekhSVskfS99vkZIukXSUyn9FkmjctudKunR9Bl7TNJJKf0tkv4rfVaflnRdbpu3S1oiabOkRyR9NLfuKknfkHRr2ucySX+cW3902uY5Sd9Mx/ib3PpPSFqVyrpY0pty60LSDEmrgdU9vB4H5sq4UdLZKX0XSV+TtD49viZpl9xr8fMu+/nDVbDuzk3SnWmTX6Y6/LHq3z0rIknfB94I/Gd6Tz+XPg+nS3oc+JmkMSltaNqmXdKXJN2VPuM3S9q7imOdLOk3kp6R9Pku6/7QrCXV6R+kfM9Kujt9H10I/Cnw9VTWr6f8l0laJ+l5Scsl/WmX/S6o9B0jabSkG9P/jmdK+0zrKtbTlhcRfhT8AawFlgJtwEhgE3Av8C5gF+BnwDnAW4EXgfcDOwGfA9YAO+f2cxdwALA3sAr4VFo3Cejoctxzgd8BxwFDgC8BS5v9evhRzEf6fD0IjE6fr/8HXADsA/wVsBuwB/Aj4D/SNrsDzwNvS8/3Bw5My9cCnyf7Afk64P/ktlkHnAYMBQ4Fns5tdxWwGTg8rb8GmJ/W7ZuO95dp3ZnAq8DfpPXHpzrzjrT+C8D/5M4xgCXp/Hbt5rXYA9gAzExl3wM4Iq07L9Xn/YDXA/8DnJ/WnQr8vMu+AnhLT+fWNa8fA+OR6tX70vKY9B5fnerBrrm0oSlPO/AEcFDKcwPwgx6OMR7YCvwZ2XfKJUBn7rjnlvYBfBL4z1SfhwCHAXvmjv03Xfb912T/A4am+vAk8Lrcfst+x6TnvwQuTeeR/x/QbT1t9YevmLWOf4+IjRHxBPDfwLKIuC8iXgZuIgvSPgbcGhFLIuJV4KtkFfd/5/ZzeUSsj4jNZJXrkB6O+/OIWBRZ+4XvA++s83nZwPL1iFiXPl8XAidGxDMRcUNEvBQRL6T0P89t83vgIEm7RsSGiFiZ0l8F3gQcEBG/i4jSlaQPAmsj4nsR0RkR95J9+Xwkt88bI+KuiOgkC15Kn/PjgJURcWNadznZF0XJJ4EvRcSqtP6LwCFdfo1/KSI2R8Rvu3kdPgg8GREXp7K/EBHL0rqTgPMiYlNEPAX8G3ByN/vqqtK52eBxbkS82M1n8PsR8WBEvAj8C/BRdd854CPALRFxZ/pO+ReyelnOq2SB1lsi4rWIWB4Rz1facUT8IP0P6IyIi8kCv7flslT6jjmc7CLCP6dzzf8PqKaetiwHZq1jY275t2WeDyP7EP+mlBgRvye7sjAylzf/JfRS2q47XfO/Tm7XYJWtyy3/huz2+G6Svp1ukzwP3AkMlzQkfXF8DPgUsCHdont72v5zgIC70i2OT6T0NwFHpNsoz0p6lizYeUPu2JU+5wfkyxjZz+98p5c3AZfl9rs5lSFfh/LnWMlo4NcV1m1XT9PyARXyllNrHbaBp6fPYNd6uBPZ1eJKutaLF4FnKuT9PrAYmJ9uxX9F0k6VdixpZrrl+FyqU3t1KUul75jRwG9S4NVVNfW0ZTkwG1jWk31gAZAksg/3E1VsG40qlA0qo3PLbyT7TM4k+4V8RETsSXa7BLJ/pETE4oh4P9ltzIeB76T0JyPibyPiALJfyN9Mba3WAf8VEcNzj2ER8ekqyrcByLdvU/552vcnu+x714j4n1yeaurKOuCPK6zbrp6y7XWCrCnCbrny5YNNG5zKfd56+gx2rYevkt3ur2RDfhtJu5FdFdvxwBGvRsS/RcR4srsxHwROKVeu1J7sLOCjwIiIGA48R6r7PVgHvLHChYBq6mnLcmA2sCwAPiDpqPQLZibwMlkblp5sBPaRtFcjC2gD3gxJo1Jj47OB68jaV/0WeDaln1PKnBoNf0hZB5SXydq5vJbWnaBtnQS2kP3Tfw24BXhraqy8U3r8L+U6unTjVuBgScenf/gz2P5K27eA2ZIOTGXYS9IJvXgdbgHeIOmzyhr77yHpiLTuWuALkl4vaV/gX4HSeIG/BA6UdIik15G1wanFRuDNvSivFVdv3tO/ljQ+BVjnAddH98NpXA98UNL/kbRz2qZsfCDpPZIOTrdGnycL+kr77lrWPcjaqj0FDJX0r8CeVZ7DXWQB40WSdk+dDo5M6+pVTwvJgdkAEhGPkDW0/HeyX0d/AfxFRLxSxbYPk31hPJouD9dya8Ws5IfAT4BH0+MC4GtkbR2fJmv0/uNc/j8i+wGxnux2xJ8Df5fW/S9gmaStwELgzIh4LLVTOxqYmrZ7EvgyWduVbkXE08AJwFfIbtWMB+4hCwqJiJvSvuan264PAsfW+iKkMr6frA4+SdaD8z1p9QXpmA8AK8g68lyQtvsV2ZfiT9M22/XQrMK5wLxUhz/aU2ZrCV8iC+SfZft2lN35PllHkSfJGs1/prvMqV3nDLL6u4Hsh1ClcS3fQBbIPU/Wgey/2PbD4jLgI6mn5OVktzxvA35Fdkv1d1TXFIAUSP4F8Bbg8VSej6V1damnRaWsiYWZWd9IWkvWI+unzS5LtST9Edk//JMi4o5ml8esryS1k/Wg9Ej9LcpXzMxsUJF0jKThysYOO5usvcvSJhfLzAxwYGZmg8+7yXpMlm73H9/D0BdlSfpTbRuQebtHvQts1leSTqrweV3Z89bWn3wr08zMzKwgfMXMzMzMrCBaNjCT9OOec5n1nT9rZvUh6R/SYMEPSro2DYEwVtmcn6uVzd+7c8q7S3q+Jq0fk9vP7JT+iKRjcumTU9oaSbP6/wzN+q5lb2XutddeMW7cuIYf58UXX2T33Xdv+HF8/OIe/+GHH34+IlpufLd99903xowZU3ZdEV7XZh3fx66/5cuXPx0Rr+8uj6SRZMN/jI+I30paACwimybrxoiYL+lbwC8j4gpJfwf8SUR8StJU4MMR8TFJ48mG9ilN2fNTsnmCIRuW4f1kPW3vJpsS7KHuyrXvvvvG61//+qbWh/7U7LrfX4p4ntXUE6B1JzE/7LDDoj/ccccd/XIcH7+4xwfuiQJ85mt9dFdHivC6+tgD59jV1BGy6XLWkU0AP5RsEN5jyDphlCbgfjewOC0vBt6dloemfAJmA7Nz+12ctvvDtil9u3yVHocddljT60N/GiznWsTzrPa7xHMemplZw0XEE5K+SjZY6G/JBiJeDjwb2+ZD7GDbfIelQI6I6JT0HNk0QSPZfniT/DbruqQfQRmSpgPTAdra2ti6dSvt7e19Or9WMVjOtZXP04GZmZk1nKQRwBRgLPAs8CPKj9Zeal9Tbj7F6Ca9XJvpsm11ImIOMAdgwoQJMWzYMCZNmtRd8QeM9vb2QXGurXyeLdv438zMWsr7gMci4qmIeBW4kWwS7OG5iapHsW1C9w7SxNpp/V5k03b9Ib3LNpXSzVqKAzMzM+sPjwMTJe0mScBRwEPAHWybA3IacHNaXpiek9b/LLXTWQhMTb02xwLjyCa8vhsYl3p57kw2l+rCfjgvs7ryrUwzM2u4iFgm6XqySds7gfvIbifeSjYZ9QUp7cq0yZXA9yWtIbtSNjXtZ2Xq0flQ2s+MyCa8RtIZZJ0BhgBzI5uc26ylDMjAbMysW3u13dqLPlDnkpgNLK5b1hcRcQ5wTpfkR8mGvuia93fACRX2cyFwYZn0RWRDcDSV64n1hW9lmpmZmRWEAzMzMzOzgnBgZmZmZlYQDszMzMzMCsKBmZmZmVlB9BiYSRot6Q5JqyStlHRmSj9X0hOS7k+P43LbzJa0RtIjko7JpU9OaWskzcqlj5W0TNJqSdelMWjMzMzMBpVqrph1AjMj4h3ARGCGpPFp3aURcUh6LAJI66YCBwKTgW9KGiJpCPANsik4xgMn5vbz5bSvccAW4PQ6nZ+ZmZlZy+gxMIuIDRFxb1p+AVjFtgljy5kCzI+IlyPiMWAN2Rg1hwNrIuLRiHgFmA9MSSNAvxe4Pm0/Dzi+tydkZmZm1qpqGmBW0hjgXcAy4EjgDEmnAPeQXVXbQha0Lc1t1sG2QG5dl/QjgH2AZyOis0z+rsefDkwHaGtrqzhz/MyDO8um96Tc/po9Q72P3/zjm5mZ9ZeqAzNJw4AbgM9GxPOSrgDOByL9vRj4BKAymwflr85FN/l3TIyYQzaFBxMmTIhKM8ef2ttRl0/acX/NnqHex2/+8c3MzPpLVYGZpJ3IgrJrIuJGgIjYmFv/HeCW9LQDGJ3bfBSwPi2XS38aGC5paLpqls9vZmZmNmhU0ytTZJPJroqIS3Lp++eyfRh4MC0vBKZK2kXSWGAccBdwNzAu9cDcmayDwMKICOAO4CNp+2nAzX07LTMzM7PWU80VsyOBk4EVku5PaWeT9ao8hOy241rgkwARsVLSAuAhsh6dMyLiNQBJZwCLgSHA3IhYmfZ3FjBf0gXAfWSBoJmZmdmg0mNgFhE/p3w7sEXdbHMhcGGZ9EXltouIR8l6bZqZmZkNWh7538zMzKwgHJiZ1Ymkf0izYzwo6VpJr6s0q0Vqg3ldmgVjWRqKprSfmmbOMDOzgcOBmVkdSBoJfAaYEBEHkbWjnErlWS1OB7ZExFuAS1O+3s6cYWZmA4QDM7P6GQrsKmkosBuwgcqzWkxJz0nrj0o9oGuaOaMfzsnMzPpRTSP/m1l5EfGEpK8CjwO/BX4CLKfyrBYjSTNhRESnpOfIZsGodeaM7VQ7O0ZvZ1So16wazZzRwcc2syJzYGZWB5JGkF3BGgs8C/yI7LZjV6VZLSrNeFHrzBnbJ1Q5O0ZvZ1So16wazZzRwcc2syLzrUyz+ngf8FhEPBURrwI3Av+bNKtFypOf1eIPM2Sk9XsBm6k8c0Z3M2qYmdkA4cDMrD4eByZK2i21FTuKbJDlSrNaLEzPSet/lmbBqGnmjH44LzMz60e+lWlWBxGxTNL1wL1kM17cR3ZL8VbKz2pxJfB9SWvIrpRNTfvpzcwZZmY2QDgwM6uTiDgHOKdLctlZLSLid8AJFfZT08wZZmY2cPhWppmZmVlBODAzMzMzKwgHZmZmZmYF4cDMzMzMrCAcmJmZWb+QNFzS9ZIelrRK0rsl7S1piaTV6e+IlFeSLpe0RtIDkg7N7Wdayr9a0rRc+mGSVqRtLk9D15i1FAdmZmbWXy4DfhwRbwfeCawCZgG3R8Q44Pb0HLKZM8alx3TgCgBJe5P1fj6CrMfzOaVgLuWZnttucj+ck1ldOTAzM7OGk7Qn8Geksfwi4pWIeJZsKrN5Kds84Pi0PAW4OjJLyWbR2B84BlgSEZsjYguwBJic1u0ZEb9IgzVfnduXWcvwOGZmZtYf3gw8BXxP0juB5cCZQFtEbACIiA2S9kv5RwLrctt3pLTu0jvKpO9A0nSyK2u0tbXVfYL3mQd39mq7/phkfrBMZt/K59ljYCZpNNkvjzcAvwfmRMRl6XLydcAYYC3w0YjYku7pXwYcB7wEnBoR96Z9TQO+kHZ9QUTMS+mHAVcBu5INoHlm+sVjZmYDw1DgUODv00wZl7HttmU55dqHRS/Sd0yMmEM2MwcTJkyIYcOG1XWC91Nn3dqr7daeVL8yVDJYJrNv5fOs5lZmJzAzIt4BTARmSBqP2wWYmVn1OoCOiFiWnl9PFqhtTLchSX835fKPzm0/CljfQ/qoMulmLaXHwCwiNpSueEXEC2SNNUfidgFmZlaliHgSWCfpbSnpKLI5YRcCpZ6V04Cb0/JC4JTUO3Mi8Fy65bkYOFrSiPTj/mhgcVr3gqSJ6c7NKbl9mbWMmtqYSRoDvAtYRgHaBVS6f1zP+/vNvk/t4zf/+GZWN38PXCNpZ7J5ZE8ju0CwQNLpwONsm0N2EVmTmDVkzWJOA4iIzZLOB+5O+c6LiM1p+dNsaxZzW3qYtZSqAzNJw4AbgM9GxPPdDA/Tb+0CKt0/ruf9/Wbfp/bxm398M6uPiLgfmFBm1VFl8gYwo8J+5gJzy6TfAxzUx2KaNVVVw2VI2oksKLsmIm5MyW4XYGZmZlZHPQZm6V79lcCqiLgkt8rtAszMzMzqqJpbmUcCJwMrJN2f0s4GLsLtAszMzMzqpsfALCJ+Tvl2YOB2AWZmZmZ14ymZzMzMzArCgZmZmZlZQTgwMzMzMysIB2ZmZmZmBeHAzMzMzKwgHJiZ1Ymk4ZKul/SwpFWS3i1pb0lLJK1Of0ekvJJ0uaQ1kh6QdGhuP9NS/tWSpuXSD5O0Im1zubqZfsPMzFqTAzOz+rkM+HFEvB14J7AKmAXcHhHjgNvTc4BjgXHpMR24AkDS3sA5wBHA4cA5pWAu5Zme225yP5yTmZn1IwdmZnUgaU/gz8hmySAiXomIZ4EpwLyUbR5wfFqeAlwdmaXA8DS12THAkojYHBFbgCXA5LRuz4j4RRor8OrcvszMbICoehJzM+vWm4GngO9JeiewHDgTaEvTjhERGyTtl/KPBNbltu9Iad2ld5RJ346k6WRX1Whra6s4CfvWrVt7NUH7zIM7a94GdpwMvrfHrwcf28yKzIFZE42ZdWuPeWYe3MmpXfKtvegDjSqS9d5Q4FDg7yNimaTL2Hbbspxy7cOiF+nbJ0TMAeYATJgwISZNmlT24O3t7VRa152un8VqrT1p+2P19vj14GObWZH5VqZZfXQAHRGxLD2/nixQ25huQ5L+bsrlH53bfhSwvof0UWXSzcxsAHFgZlYHEfEksE7S21LSUcBDwEKg1LNyGnBzWl4InJJ6Z04Enku3PBcDR0sakRr9Hw0sTutekDQx9cY8JbcvMzMbIHwr06x+/h64RtLOwKPAaWQ/fhZIOh14HDgh5V0EHAesAV5KeYmIzZLOB+5O+c6LiM1p+dPAVcCuwG3pYWZmA4gDM7M6iYj7gQllVh1VJm8AMyrsZy4wt0z6PcBBfSymmZkVmG9lmpmZmRWEr5jVQTW9K83MzMx64sCsBfU2EPQwG2ZmZsXmwCynXMBTbhwxMzMzs0bosY2ZpLmSNkl6MJd2rqQnJN2fHsfl1s1Okyw/IumYXPrklLZG0qxc+lhJy9KEzdelHm1mZmZmg041jf+vovxkyZdGxCHpsQhA0nhgKnBg2uabkoZIGgJ8g2zi5vHAiSkvwJfTvsYBW4DT+3JCZmZmZq2qx8AsIu4ENveUL5kCzI+IlyPiMbIxmg5PjzUR8WhEvALMB6akgTLfSzZKOmw/ybOZmZnZoNKXNmZnSDoFuAeYGRFbyCZVXprLk59ouevEzEcA+wDPRkRnmfw7qHaC5t5OtFxO26713V8zj9+bCYybPfFxEY5vZmbWX3obmF0BnE82ifL5wMXAJ6g80XK5K3NVT8z8hxVVTtBcz8b6Mw/u5OIVzesjUc/jd51IuhrNnvi4CMc3MzPrL736xo+IjaVlSd8BbklPK03ATIX0p4Hhkoamq2aemNnMzMwGrV4FZpL2T5MqA3wYKPXYXAj8UNIlwAHAOOAusitj4ySNBZ4g6yDw8YgISXcAHyFrd5af5NmsZh7jzazYUmewe4AnIuKD6XthPrA3cC9wckS8ImkX4GrgMOAZ4GMRsTbtYzZZR7HXgM9ExOKUPhm4DBgCfDciLurXkzOrg2qGy7gW+AXwNkkdaTLmr0haIekB4D3APwBExEpgAfAQ8GNgRkS8lq6GnQEsBlYBC1JegLOAf5S0hqzN2ZV1PUMzMyuSM8m+B0oq9cw/HdgSEW8BLk35etv736xl9HjFLCJOLJNcMXiKiAuBC8ukLwIWlUl/lKzXppmZDWCSRgEfIPuO+Mdcz/yPpyzzgHPJ2jFPScuQ9dz/esr/h97/wGPpR33pO2RN+k5B0vyU96EGn5ZZXXnkfzMz6y9fAz4H7JGed9czfySpN39EdEp6LuWvtff/Drr28K937+/e9qTvj85Gze7p3l9a+TwdmJmZWcNJ+iCwKSKWS5pUSi6TNXpYV2vv/x0Tu/TwHzZsWF17f/d2ZIDe9JyvVbN7uveXVj5PB2ZmZtYfjgQ+lKbwex2wJ9kVtEo980u9/DskDQX2IhvsvNbe/2YtpZopmczMzPokImZHxKiIGEPWeP9nEXESUOqZD9v3zF+YnpPW/ywiIqVPlbRL6tFZ6v1/N6n3f5pzeWrKa9ZSfMXMzMya6SxgvqQLgPvY1rnsSuD7qXH/ZrJAi4hYKanU+7+T1PsfQFKp9/8QYG6u979Zy3BgZmZm/Soi2oH2tFy2Z35E/A44ocL2NfX+N2slvpVpZmZmVhAOzMzMzMwKwoGZmZmZWUE4MDOrkzQtzH2SbknPx0paJmm1pOtSTzFSb7LrJK1J68fk9jE7pT8i6Zhc+uSUtkbSrP4+NzMz6x8OzMzqx3MAmplZnzgwM6uD3ByA303PS3MAXp+yzAOOT8tT0nPS+qO6zgEYEY8BpTkADyfNARgRrwClOQDNzGyA8XAZZvVRyDkAK80V19t55Oo1B2Az57Hzsc2syByYmfVRkecArDRXXG/nkavXHIDNnMfOxzazInNgZtZ3ngPQzMzqwm3MzPrIcwCamVm9+IqZWeN4DkAzM6uJAzOzOvIcgGZm1hc93sqUNFfSJkkP5tL2lrQkDZy5RNKIlC5Jl6dBMB+QdGhum2kp/2pJ03Lph0lakba5PA0bYGb/v717j7asKu+8//01iEEUAXktEYiFHTodIjGSasCYN11KggUmwR5DO9hEwJBmmFejJuRSJG/atOaC3a1JtG0NESIYFA2aQEdsrEbPa6dbEDDIVUKJJZSUYOSipVFT5nn/WPPo9tQ+lzpnX9Y59f2MscfZe+651nzW2nvu8+w511pbkrTXWcqI2TuB/wpcOlC2Gbi2qi5oVyHfTDdtcwrdcTFH053O/zbghCSHAK8FNtCdTXZTkquq6uFW51y6ywRcTXdhzQ+tfNMkSVq+9cs8C1laiUVHzKrqY3THwQwavEDm3AtnXlqd6+jOSjsMeD6wpaoeasnYFmBTe+7Aqvp4O/j50oF1SZIk7VWWe4zZuqraAVBVO5I8uZV/+8KZzewFMhcq3z6kfKilXjxzuRfBHGbd/qNd3zTbX87FJad9Uco9bX9UF0AdbF+SpEkZ9cH/e3rhzIUuwrn7E0u8eOZyL4I5zHnH7uKNt07vHIlRtj/3Ip9LMe2LUu5p+6O6AOpg+5IkTcpyr2P2QJuGpP19sJXPd4HMhcqPGFIuSZK011luYjZ4gcy5F848s52deSLwaJvyvAY4OcnB7QzOk4Fr2nNfSXJiOxvzzIF1SZIk7VUWnSNL8h5gI3Boku10Z1deALwvyTnAvXznmkxXA6cCW4GvAS8DqKqHkrye7grmAK+rqtkTCn6R7szP/enOxvSMTEmStFdaNDGrqpfM89RJQ+oW8Ip51nMxcPGQ8huBZywWhyRJ0lrnb2VKkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPXE9C5pL73FIVUAACAASURBVM1j8IeDzzt210h/yUGSpD5zxEySJKknTMwkSWOX5MgkH01yZ5Lbk7y6lR+SZEuSu9vfg1t5krw5ydYktyQ5bmBdZ7X6dyc5a6D8R5Lc2pZ5c/tFGWlVcSpTY7PeKUhJ37ELOK+qPpnkCcBNSbYAZwPXVtUFSTYDm4HfAE4Bjm63E4C3ASckOYTuF2g2ANXWc1VVPdzqnAtcR/dLNJtYRb8ms9zPzG0XvGDEkWiaHDGTJI1dVe2oqk+2+18B7gQOB04DLmnVLgFe2O6fBlxaneuAg5IcBjwf2FJVD7VkbAuwqT13YFV9vP0KzaUD65JWDUfM9iLL+TbmwfeSRi3JeuBZwPXAuqraAV3yluTJrdrhwH0Di21vZQuVbx9SPqz9c+lG1li3bh07d+5kZmZmt3rnHbtrzzZsSobFPp/5tnWtWc3baWImSZqYJI8H3g+8pqq+vMBhYMOeqGWU715YdSFwIcCGDRvq8Y9/PBs3btyt3mr5UrrtjI1LrjszMzN0W9ea1bydTmVKkiYiyWPokrLLquoDrfiBNg1J+/tgK98OHDmw+BHA/YuUHzGkXFpVTMykEfCMM2lh7f16EXBnVb1p4KmrgNn3+VnAlQPlZ7a+ciLwaJvyvAY4OcnBrT+dDFzTnvtKkhNbW2cOrEtaNZzKlEbDM84WMPf4xj05dtEzztaM5wAvBW5NcnMr+03gAuB9Sc4B7gVe3J67GjgV2Ap8DXgZQFU9lOT1wA2t3uuq6qF2/xeBdwL70/WNVdE/pEEmZtIItG/rswcwfyXJ4BlnG1u1S4AZusTs22ecAdclmT3jbCPtjDOAltxtSjJDO+Oslc+eceY/Hq0KVfU3DD8ODOCkIfULeMU867oYuHhI+Y3AM1YQpjR1JmbSiE3zjLO5Z5vNd1bScs9YGtVZauv2X/q6Rn1m1TTP1tpb25a0dCZm0ghN+4yzuWebzXdW0nLPWBrVWWrnHbuLN966tI+fPTnjbCmmebbW3tq2pKVb0cH/Sba1g5FvTnJjKxvZwc7SauIZZ5KklRrFWZnPraofrqoN7fFmuoOdjwaubY/huw92PpfuQGYGDnY+ATgeeO1sMietFp5xJkkahXFcLmMkP68xhrikcZo94+x5bQT55iSn0p1x9pNJ7gZ+sj2G7oyze+jOOPtT4P+B7owzYPaMsxvY/Yyzd7RlPoMH/kvSmrPSY8wK+HCSAv6kHd8yqoOdd7PUA5tH+TMae3KQ8jjY/mTaX+gg+aXwjDNJ0iisNDF7TlXd35KvLUk+vUDdkf+MxnwHso7yZzT25CDlcbD9ybQ/3wHmnsUmSZqkFU1lVtX97e+DwF/SHSM2qoOdJUmS9irLTsySHNCucE6SA+gOUr6NER3svNy4JEmSVquVzBGtA/6yXadpX+DdVfU/ktzA6H5eQ5Ikaa+x7MSsqu4Bnjmk/EuM6GBnSZKkvck4LpchSZKkZTAxkyRJ6gkTM0mSpJ4wMZMkSeoJEzNJkqSeMDGTJEnqCRMzSZKknjAxkyRJ6onp/Tq1JPXQ+s0fXNZy2y54wYgjkbQ3csRMkiSpJxwxkyRpFduTUd7zjt3F2a2+o7z9ZGImqdecWpS0N3EqU5IkqSccMZOkEVjKyN7gNNIsR/YkDXLETJIkqSdMzCRJknrCqUxJa9J8U4vDphMlqS96M2KWZFOSu5JsTbJ52vFIfWQ/kRZmH9Fq14sRsyT7AG8FfhLYDtyQ5KqqumO6kUn9YT+RFmYf2TNeiqaf+jJidjywtaruqapvApcDp005Jqlv7CfSwuwjWvV6MWIGHA7cN/B4O3DClGKR+mpk/eTWzz/qcVY9MelRizU+SuL/kglY7ntokqZ1LOko+klfErMMKavdKiXnAue2hzuT3DXWqIBXwaHA34+7Hdufbvt5w7xPHQo8bdztL9Gi/WQP+she8bqu5bYXeM/OZ0VtL9LequkjsHs/ee5zn/slptgfJmnan+mTMq3tHEU/6Utith04cuDxEcD9cytV1YXAhZMKCiDJjVW1YZJt2n7v2l8/rfbnWLSfLLWP9GS/TqV9217TlvW/ZC/ZN8Des62reTv7cozZDcDRSY5Ksh9wOnDVlGOS+sZ+Ii3MPqJVrxcjZlW1K8krgWuAfYCLq+r2KYcl9Yr9RFqYfURrQS8SM4Cquhq4etpxDDHRqVPbt/2FjLCfTHu7ptm+ba9hy+wje8W+afaWbV2125mq3Y6LlCRJ0hT05RgzSZKkvZ6JGZDkyCQfTXJnktuTvHpInY1JHk1yc7v9hxHHsC3JrW3dNw55Pkne3H5m5JYkx42w7e8f2K6bk3w5yWvm1Bnp9ie5OMmDSW4bKDskyZYkd7e/B8+z7Fmtzt1Jzhph+/85yafb/v3LJAfNs+yCr1XfTesna5bSzyYQwz5J/jbJX0+h7YOSXNHeY3cmefYE2/7lts9vS/KeJN8zqbb7bC3/fNN8/W2pn7Or0dz+3U4Cub5t63vbCSH9V1V7/Q04DDiu3X8C8HfAMXPqbAT+eowxbAMOXeD5U4EP0V2n50Tg+jHFsQ/wBeBp49x+4MeB44DbBsr+E7C53d8MvGHIcocA97S/B7f7B4+o/ZOBfdv9NwxrfymvVZ9v7fX9DPB0YD/gU3Pf62Nse9F+NoEYfgV49zj78gJtXwL8Qru/H3DQhNo9HPgssH97/D7g7Elvf99u0+wLE9q+of1tKZ+zq/U2t3+39/rp7f7bgV+cdoxLuTliBlTVjqr6ZLv/FeBOug+zPjkNuLQ61wEHJTlsDO2cBHymqj43hnV/W1V9DHhoTvFpdP+8aH9fOGTR5wNbquqhqnoY2AJsGkX7VfXhqtrVHl5Hdw2ktWZqP1kz7X6W5AjgBcA7JtXmQNsH0n0ZuAigqr5ZVY9MMIR9gf2T7As8jiHX9toLremfb1qgvy3lc3bVmdu/kwR4HnBFq7JqttXEbI4k64FnAdcPefrZST6V5ENJfnDETRfw4SQ3pbsq9VzDfmpkHP/UTgfeM89z49x+gHVVtQO6DxXgyUPqTGo//DzdCOUwi71WfTap/begRfrZuPwR8OvAP02wzVlPB74I/FmbanlHkgMm0XBVfR74L8C9wA7g0ar68CTa7rle9IVJmNPflvI5uxrN7d9PAh4Z+LK9al5fE7MBSR4PvB94TVV9ec7Tn6Sb3nsm8Bbgr0bc/HOq6jjgFOAVSX58bnhDlhnpKbVt/v1ngL8Y8vS4t3+pJrEffgvYBVw2T5XFXqs+G/v+WzSAhfvZuNr8KeDBqrppEu0NsS/d1PnbqupZwFfpppHGrh1DdBpwFPBU4IAkPzeJtntu6n1hEqbR3yZtnv69al9fE7MmyWPo3ryXVdUH5j5fVV+uqp3t/tXAY5IcOqr2q+r+9vdB4C/phtkHLemnRlboFOCTVfXAkPjGuv3NA7PTs+3vg0PqjHU/tJMJfgo4o9qBCXMt4bXqs0m8j+a1WD8bo+cAP5NkG92U1fOS/PkE298ObK+q2RHCK+gStUn4CeCzVfXFqvpH4APAj06o7T6bal+YhHn621I+Z1eb3fo33QjaQW36HlbR62tixrfnoi8C7qyqN81T5ymtHkmOp9t3XxpR+wckecLsfbqD0G+bU+0q4Mx0TqSbjtgxivYHvIR5pjHHuf0DrgJmz7I8C7hySJ1rgJOTHNxGAk5uZSuWZBPwG8DPVNXX5qmzlNeqz6b2kzVL6WfjUlXnV9UR1f3u6enAR6pqYqNGVfUF4L4k39+KTgLumFDz9wInJnlcew1OojveaG+3pn++aYH+tpTP2VVlnv59BvBR4EWt2urZ1mmffdCHG/BjdEOctwA3t9upwMuBl7c6rwRupztz5zrgR0fY/tPbej/V2vitVj7YfoC30p1FdCuwYcT74HF0idYTB8rGtv10CeAO4B/pvrmeQ3dMwLXA3e3vIa3uBuAdA8v+PLC13V42wva30h1zMvseeHur+1Tg6oVeq9V0a+/tv2vvpYnFP18/m8L2b2Q6Z2X+MHBj2/6/YhlnE6+g7f8IfJruS8S7gMdOevv7eJtWX5jQts33f23o5+xauQ327/Z5/Yn22f4Xq+V975X/JUmSesKpTEmSpJ4wMZMkSeoJEzNJkqSeMDGTJEnqCRMzSZKknjAxkyRJ6gkTM0mSpJ4wMZMkSeoJEzNJkqSeMDGTJEnqCRMzSZKknjAxkyRJ6gkTM0mSpJ4wMZMkSeoJE7MpSfK9SXYm2WfE692W5CdGuc5F2nt7kt+eVHuSJK1lJmZjsliCVFX3VtXjq+pbk4xrJZKcneRvBsuq6uVV9fppxSRJ0lpiYjYFSfaddgxz9TEmaTWw70gaJROzMUjyLuB7gf/epit/PUklOSfJvcBHkqxvZfu2ZWaS/EGSTyR5NMmVSQ5ZQlsvTfK5JF9K8ltznntnkt8deLwxyfaBx9uS/EaSW4CvJtk3yeYkn0nylSR3JPk3re4PAG8Hnt226ZF52vj3SbYmeSjJVUmeOvBcJXl5kruTPJzkrUmyvL0sLS7JryV5/5yytyT5oyRPTHJRkh1JPp/kd2cPLUjyz5N8pPWrv09yWZKDBtaxW9+Z8KZJWqNMzMagql4K3Av8dFU9Hnhfe+pfAz8APH+eRc8Efh54KrALePNC7SQ5Bngb8NK2zJOAI/Yw3JcALwAOqqpdwGeA/xt4IvAfgT9PclhV3Qm8HPh4m4I9aO6KkjwP+APg3wKHAZ8DLp9T7aeAfwU8s9Wbb19Io/DnwKbZpKolUD8LvAu4hK6ffR/wLOBk4BfacqF7Lz+Vrs8eCfzOnHXP7TuStGImZpP1O1X11ar6h3mef1dV3VZVXwV+G/i3i5wc8CLgr6vqY1X1jbbMP+1hTG+uqvtmY6qqv6iq+6vqn6rqvcDdwPFLXNcZwMVV9ckWz/l0I2zrB+pcUFWPVNW9wEeBH97DeKUlq6odwMeAF7eiTcDfA9uBU4DXtD75IPCHwOltua1VtaWqvlFVXwTeRPfFatB39R1JGgUTs8m6bw+e/xzwGODQBeo/dXCZltB9aSUxJTkzyc1JHmnTlc9YJIa58XxuIJ6dLZ7DB+p8YeD+14DH72G80p66BPi5dv/n6EbLnkbXv3YMvNf/BHgyQJInJ7m8TXF+mW7kbW4/WKw/S9IeMzEbn1pi2aAjB+5/L/CPdN/u57NjcJkkj6Obzpz1VeBxA4+fslBMSZ4G/CnwSuBJbbryNrppnaXEfz/dP7zZ9R3Q4vn8IstJ4/RXwA8leQbdVPpldEnVN4BDq+qgdjuwqn6wLfMHdO/3H6qqA+kSurnHQy7WHyRpj5mYjc8DwNP3cJmfS3JMS7BeB1yxyOU0rgB+KsmPJdmvLTP4mt4MnJrkkCRPAV6zSPsH0P2z+SJAkpfRjZjNegA4orU1zLuBlyX54SSPBX4fuL6qti3SrjQ2VfV1ur7ybuAT7VI1O4APA29McmCSf9YO+J+drnwCsBN4JMnhwK9NJXhJex0Ts/H5A+D/bVMkL1riMu8C3kk33fc9wKsWqlxVtwOvoPuHswN4mO7YmcH1fQrYRvdP6L2LrO8O4I3Ax+mSsGOB/z1Q5SPA7cAXkuw2kldV19Id5/b+Fs8/px2zI03ZJXTv53cNlJ0J7AfcQdd3rqA7aQW6E1+OAx4FPgh8YGKRStqrpcrR+D5IMgP8eVW9Y9qxSGtNku8FPg08paq+PO14JGk+jphJWtOS/DPgV4DLTcok9Z0XRey5JGfQnS021+cGDlSWNEQ7AeUBurOFN005HElalFOZkiRJPeFUpiRJUk+s2qnMQw89tNavX79b+Ve/+lUOOOCAyQc0jz7F06dYYPXEc9NNN/19Vf1fUwhpRebrI9Cvfd+nWKBf8ayWWFZrH5H6aNUmZuvXr+fGG2/crXxmZoaNGzdOPqB59CmePsUCqyeeJJ/bvXb/zddHoF/7vk+xQL/iWS2xrNY+IvWRU5mSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BMmZpIkST2xas/KHIf1mz+4rOW2XfCCEUcijdetn3+Us5fxfve9Lknj5YiZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJI5Lkl5PcnuS2JO9J8j1JjkpyfZK7k7w3yX6t7mPb463t+fUD6zm/ld+V5PkD5Zta2dYkmye/hZKkcTMxk0YgyeHAq4ANVfUMYB/gdOANwB9W1dHAw8A5bZFzgIer6vuAP2z1SHJMW+4HgU3Af0uyT5J9gLcCpwDHAC9pdSVJa4iJmTQ6+wL7J9kXeBywA3gecEV7/hLghe3+ae0x7fmTkqSVX15V36iqzwJbgePbbWtV3VNV3wQub3UlSWvIvkuplOSXgV8ACrgVeBlwGN0/h0OATwIvrapvJnkscCnwI8CXgJ+tqm1tPefTjRR8C3hVVV3TyjcBf0w3yvCOqrpgVBsoTUJVfT7JfwHuBf4B+DBwE/BIVe1q1bYDh7f7hwP3tWV3JXkUeFIrv25g1YPL3Den/IS5cSQ5FzgXYN26dczMzAyNd93+cN6xu4Y+t5D51rcSO3fuHMt6l6tP8RiLtPdZNDEbmKI5pqr+Icn76KZaTqWbork8ydvpEq63MTBFk2R2Kudn50zRPBX4n0n+RWvmrcBP0v2zuSHJVVV1x0i3VBqjJAfTjWAdBTwC/AXdtONcNbvIPM/NVz5sdLt2K6i6ELgQYMOGDbVx48ah8b7lsit5461L+l72XbadMXx9KzEzM8N8cU5Dn+IxFmnvs9SpTKdopIX9BPDZqvpiVf0j8AHgR4GDWr8BOAK4v93fDhwJ0J5/IvDQYPmcZeYrlyStIYt+Ze7LFA0sbZpmJcPty5nagYWnd/o0/N+nWGDNxXMvcGKSx9H1k5OAG4GPAi+i+8JxFnBlq39Ve/zx9vxHqqqSXAW8O8mb6EaWjwY+QTeSdnSSo4DP040+/7vlBitJ6qelTGX2YooGljZNs5Lh9rM3f3BZyy00vdOn4f8+xQJrK56quj7JFXTHW+4C/pbuvfpB4PIkv9vKLmqLXAS8K8lWupGy09t6bm+HC9zR1vOKqvoWQJJXAtfQHYt5cVXdvqxgJUm9tZSDTL49RQOQ5LumaNqo2bApmu1LnKJhgXJp1aiq1wKvnVN8D910/dy6XwdePM96fg/4vSHlVwNXrzxSSVJfLeUYs29P0bRjxU6i+zY/O0UDw6doYGCKppWf3i6seRTfmaK5gTZF0y6+eXqrK0mStFdZyjFmTtFIkiRNwJLOl3eKRpIkafy88r8kSVJPmJhJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZNCJJDkpyRZJPJ7kzybOTHJJkS5K729+DW90keXOSrUluSXLcwHrOavXvTnLWQPmPJLm1LfPmJJnGdkqSxsfETBqdPwb+R1X9S+CZwJ3AZuDaqjoauLY9BjgFOLrdzgXeBpDkEOC1wAnA8cBrZ5O5VufcgeU2TWCbJEkTtKTEzJEAaWFJDgR+HLgIoKq+WVWPAKcBl7RqlwAvbPdPAy6tznXAQUkOA54PbKmqh6rqYWALsKk9d2BVfbyqCrh0YF2SpDVi3yXWmx0JeFGS/YDHAb9JNxJwQZLNdCMBv8F3jwScQPct/4SBkYANQAE3Jbmq/fOZHQm4DriabiTgQyPaRmkSng58EfizJM8EbgJeDayrqh0AVbUjyZNb/cOB+waW397KFirfPqT8uyQ5l64vsW7dOmZmZoYGu25/OO/YXXu2hTDv+lZi586dY1nvcvUpHmOR9j6LJmYDIwFnQzcSAHwzyWnAxlbtEmCGLjH79kgAcF0bbTus1d1SVQ+19c6OBMzQRgJa+exIgImZVpN9geOAX6qq65P8Md+Zthxm2KhwLaP8uwuqLgQuBNiwYUNt3LhxaONvuexK3njrUr+Xfce2M4avbyVmZmaYL85p6FM8xiLtfZbyydyLkQBY2mjASr7VLWcEARYeRejTt8w+xQJrLp7twPaqur49voIuMXsgyWGtjxwGPDhQ/8iB5Y8A7m/lG+eUz7TyI4bUlyStIUtJzHoxEgBLGw1Yybe6szd/cFnLLTSK0KdvmX2KBdZWPFX1hST3Jfn+qroLOAm4o93OAi5of69si1wFvDLJ5XRT/o+25O0a4PcHDvg/GTi/qh5K8pUkJwLXA2cCb1lWsJKk3lpKYuZIgLQ0vwRc1o7DvAd4Gd0JNu9Lcg5wL/DiVvdq4FRgK/C1VpeWgL0euKHVe93s9D/wi8A7gf3ppvqd7pekNWbRxMyRAGlpqupmupNb5jppSN0CXjHPei4GLh5SfiPwjBWGKUnqsaUe/etIgCRJ0pgtKTFzJECSJGn8vPK/JElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST5iYSZIk9cS+0w5AkjQ56zd/cFnLvXPTASOORNIwjphJkiT1hImZJElST5iYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJI5JknyR/m+Sv2+Ojklyf5O4k702yXyt/bHu8tT2/fmAd57fyu5I8f6B8UyvbmmTzpLdNkjQZJmbS6LwauHPg8RuAP6yqo4GHgXNa+TnAw1X1fcAftnokOQY4HfhBYBPw31qytw/wVuAU4BjgJa2uJGmNWXJi5miANL8kRwAvAN7RHgd4HnBFq3IJ8MJ2/7T2mPb8Sa3+acDlVfWNqvossBU4vt22VtU9VfVN4PJWV5K0xuzJlf9nRwMObI9nRwMuT/J2ulGAtzEwGpDk9FbvZ+eMBjwV+J9J/kVb11uBnwS2Azckuaqq7ljhtkmT9EfArwNPaI+fBDxSVbva4+3A4e3+4cB9AFW1K8mjrf7hwHUD6xxc5r455ScMCyLJucC5AOvWrWNmZmZosOv2h/OO3TX0uYXMt76V2Llz51jWu1x9imccsSzndR9XLJJ2t6TEbGA04PeAXxkYDfh3rcolwO/QJWantfvQjQb817mjAcBnk8yOBkAbDWhtzY4GmJhpVUjyU8CDVXVTko2zxUOq1iLPzVc+bGS7hpRRVRcCFwJs2LChNm7cOKwab7nsSt54657/Itu2M4avbyVmZmaYL85p6FM844jl7BX8JFNf9ou0li31k3nVjAas5Fvdcr9JLtRen75l9ikWWFPxPAf4mSSnAt9DN6r8R8BBSfZt/eQI4P5WfztwJLA9yb7AE4GHBspnDS4zX7kkaQ1ZNDFbbaMBK/mGudxvkguNIqz1b98rsVbiqarzgfMBWh/51ao6I8lfAC+iOybsLODKtshV7fHH2/MfqapKchXw7iRvopvuPxr4BF3fOTrJUcDn6Q4JmB2tliStIUsZMXM0QFqe3wAuT/K7wN8CF7Xyi4B3ten8h+gSLarq9iTvo5vG3wW8oqq+BZDklcA1wD7AxVV1+0S3RJI0EYsmZo4GSEtXVTPATLt/D985jnKwzteBF8+z/O/RHcs5t/xq4OoRhipJ6qE9P/r3OxwNkCRJGqE9SswcDZAkSRofr/wvSZLUEyZmkiRJPWFiJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BP7TjuAtWD95g/O+9x5x+7i7Hme33bBC8YVkiRJWoUWHTFLcmSSjya5M8ntSV7dyg9JsiXJ3e3vwa08Sd6cZGuSW5IcN7Cus1r9u5OcNVD+I0lubcu8OUnGsbHSuNhPJEmjsJSpzF3AeVX1A8CJwCuSHANsBq6tqqOBa9tjgFOAo9vtXOBt0P2DAl4LnAAcD7x29p9Uq3PuwHKbVr5p0kTZTyRJK7ZoYlZVO6rqk+3+V4A7gcOB04BLWrVLgBe2+6cBl1bnOuCgJIcBzwe2VNVDVfUwsAXY1J47sKo+XlUFXDqwLmlVsJ9IkkZhj44xS7IeeBZwPbCuqnZA908pyZNbtcOB+wYW297KFirfPqR8WPvn0o0YsG7dOmZmZnars3PnzqHlS3HesbuWtdxC1u0//3qXG+dyrWTfjMNajWea/WQpfQQWfl8uZByv11p9H4zCOGJZ7udcn/aLtJYtOTFL8njg/cBrqurLCxzeMuyJWkb57oVVFwIXAmzYsKE2bty4W52ZmRmGlS/FfAfpr8R5x+7ijbcO383bztg48vYWspJ9Mw5rMZ5p95Ol9BGAt1x25bzvy4WM4z27Ft8HozKOWJb7OffOTQf0Zr9Ia9mSLpeR5DF0/2wuq6oPtOIH2vQK7e+DrXw7cOTA4kcA9y9SfsSQcmlVsZ9IklZq0a/M7cyvi4A7q+pNA09dBZwFXND+XjlQ/sokl9MdwPxom8K5Bvj9gQOZTwbOr6qHknwlyYl0Uz9nAm9ZyUYtdPkKaRxWYz+RJPXPUuYyngO8FLg1yc2t7Dfp/tG8L8k5wL3Ai9tzVwOnAluBrwEvA2j/WF4P3NDqva6qHmr3fxF4J7A/8KF2k1YT+4kkacUWTcyq6m8YfnwLwElD6hfwinnWdTFw8ZDyG4FnLBaL1Ff2E0nSKPiTTJIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiMBVg4gAAB7pJREFUJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9YWImSZLUEyZmkiRJPWFiJkmS1BMmZpIkST1hYiZJktQTJmaSJEk9se+0A9ibrd/8wWUtt+2CF4w4EkmS1AeOmEmSJPVEbxKzJJuS3JVka5LN045H6iP7iSStbb1IzJLsA7wVOAU4BnhJkmOmG5XUL/YTSVr7+nKM2fHA1qq6ByDJ5cBpwB1Tjaqnlnts2js3HTDiSDRh9hNJWuP6kpgdDtw38Hg7cMLcSknOBc5tD3cmuWvIug4F/n7kES7Tq3oUz3Pf0J9YmtUSz9MmHcg8Fu0nS+wjsMx9nzfs6RJLslreB9PQm1gW+fzoSx+RVr2+JGYZUla7FVRdCFy44IqSG6tqw6gCW6k+xdOnWMB4lmHRfrKUPgL92tY+xQL9isdYpL1PL44xo/vmf+TA4yOA+6cUi9RX9hNJWuP6kpjdAByd5Kgk+wGnA1dNOSapb+wnkrTG9WIqs6p2JXklcA2wD3BxVd2+zNUtOo0zYX2Kp0+xgPHskTXcT/oUC/QrHmOR9jKp2u1QLkmSJE1BX6YyJUmS9nomZpIkST2xphKzaf9cTZJtSW5NcnOSG1vZIUm2JLm7/T14jO1fnOTBJLcNlA1tP503t311S5LjJhTP7yT5fNtHNyc5deC581s8dyV5/ohjOTLJR5PcmeT2JK9u5VPbP2OO/18m+XiSbyT51Tnr2u19OqS9ebc/yVlJPpvka0nun1A8Z7Q4bknyf5I8c+C5+5LsTPL1JP8wgVg2Jnl04D38Hwae+7m2X76Z5IEJxPJrA3HcluRbSQ4Zw345KMkVST7d3oPPHhLLYu+Zu9vtrGHbIqmpqjVxozsY+jPA04H9gE8Bx0w4hm3AoXPK/hOwud3fDLxhjO3/OHAccNti7QOnAh+iuzbWicD1E4rnd4BfHVL3mPaaPRY4qr2W+4wwlsOA49r9JwB/19qc2v4Zc/xPBv4V8Htz9/ew9+mQ9oZuP3AIcA/wA8C/bvePnEA8Pwoc3O6fMvh60F1093kT3Dcbgb8eUr5PW/6n6T6Dbm2PxxbLnPo/DXxkTPvlEuAX2v39gIOW8Z45BDi43T94mv3Jm7c+39bSiNm3f66mqr4JzP5czbSdRvehRvv7wnE1VFUfAx5aYvunAZdW5zrgoCSHTSCe+ZwGXF5V36iqzwJb6V7TUcWyo6o+2e5/BbiT7kr6U9s/e2JP46+qB6vqBuAfl9nkfNv/fGBLVd1ZVf8fsAX4sXHHU1X/p6oebg+vo7uG26xvAbe0epPYN/M5Hrirqv57+wx6N/C1CcbyEuA9A49Hsl+SHEj3JeuiVu+bVfXIkPYXe8881F7DLcCmEWyvtCatpcRs2M/VHD7hGAr4cJKb0v00DsC6qtoB3T9Xum+lkzRf+9PcX69sUx0X5ztTuxOLJ8l64FnA9fRz/yxoifEvZNj7dK75tn9Y+TMmEM+gc+hGZoYtf/6EYnl2kk8l+VCSH2xlc/fN1+lGE8e+X5I8ji7Zef88y69kvzwd+CLwZ0n+Nsk7kgz74d09ec/0oi9JfdSL65iNyJJ+1mnMnlNV9yd5MrAlyacn3P6emNb+ehvw+tbW64E3Aj8/qXiSPJ7un9drqurLybBmu6qTiGdP7UH8C9ntfdpGN7+rqSHL1ZDy/YAzgVePOZ4uqOS5dInZjw1Zfj3dD7r//phj+STwtKrame4Yyb8CjmZg37TX6VXAxyaxX+imMf93VT00ZPn1rGy/7Et3SMIvVdX1Sf6Ybgr0t+fUW+p7ZrZc0hBracRs6j9XU1X3t78PAn9JN7XxwOwUWPv74CRjWqD9qeyvqnqgqr5VVf8E/Cnfma4cezxJHkOX1FxWVR9oxb3aPwvZw/jnNc/7dK75tv/b5S2es4GZCcRDkh8C3gGcVlVfGly+xfInwP+iG6kaWyxV9eWq2tnuXw08JsmhtH0z8DrdAfzNOGMZcDrfPY05yv2yHdheVde3x1fQJWrD6i34nplTLmmItZSYTfXnapIckOQJs/eBk4HbWgyzZyGdBVw5qZia+dq/CjiznUl1IvDo7PTGOM05Tuvf0O2j2XhOT/LYJEfRjUB8YoTthu4YmTur6k0DT/Vq/8xnGfHPt5753qdzzbf91wAntynodwEHAq8edzxJvhf4APDSqvq7IctfBNxNd5D74HtqHLE8pb0eJDme7nP0S7TPIOC9wF10CcjsZ9C4XieSPJHuRIwrhyy/4v1SVV8A7kvy/a3oJLqkc65F3zPtfXNyK5M0zHxnBazGG91ZQX9Hd0bfb0247afTnVX4KeD22faBJwHX0n04XgscMsYY3gPsoDt4dzvdlM/Q9ummF97a9tWtwIYJxfOu1t4tdB/khw3U/60Wz13AKSOO5cfopk9uAW5ut1OnuX/GHP9T2j7/MvBIu3/gfO/TtszLgZcvtv10U8/bWzz3TSiedwAPD7R140C/29pi+Xp7v407lle25z9FdyLCjw7U+9UWyzcmEUt7fDbdiTNzP49Gsl/acz8M3Ej3/vsrvnOG7J68Z7a228um2Ze8eev7zZ9kkiRJ6om1NJUpSZK0qpmYSZIk9YSJmSRJUk+YmEmSJPWEiZkkSVJPmJhJkiT1hImZJElST/z/UybrV+NZPuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check distribution of features\n",
    "X.hist(figsize = (10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Training and Testing sets\n",
    "X_train, y_train, X_test, y_test = train_test_split(X_encoded, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Data results (2015 data set)----\n",
      "RMSE: $5.3\n",
      "R2: 0.81\n",
      "\n",
      "----Test Data results (2016 data set)----\n",
      "RMSE: $5.1\n",
      "R2: 0.83\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Multivariate Linear Model\n",
    "lm = LinearRegression().fit(X_train,y_train)\n",
    "model_results(X_train, y_train, X_test, y_test, lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering results\n",
    "- Through trial and error found that adding dropoff_geohash is leading to overfit so drop this from the features list \n",
    "- Multivariate linear model has improved the RMSE to 5.1\n",
    "- Since the Training Data still has high RMSE, the linear model is suffering from high Bias\n",
    "- Try Boosting models to improve the Bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Data results (2015 data set)----\n",
      "RMSE: $3.9\n",
      "R2: 0.90\n",
      "\n",
      "----Test Data results (2016 data set)----\n",
      "RMSE: $4.0\n",
      "R2: 0.89\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Gradient boosting regressor to reduce bias\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01, 'loss': 'ls'}\n",
    "gbr = ensemble.GradientBoostingRegressor(**params)\n",
    "gbr.fit(X_train, y_train)\n",
    "model_results(X_train, y_train, X_test, y_test, gbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define helper function\n",
    "def importances(X_train, model):\n",
    "    \"\"\"Plots the top 20 features of importance to the model.\n",
    "    \"\"\"\n",
    "\n",
    "    feature_importance = model.feature_importances_\n",
    "    # make importances relative to max importance\n",
    "    feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "    sorted_idx = np.argsort(feature_importance)[::-1][:20][::-1]\n",
    "    names = [X_train.columns[index] for index in sorted_idx]\n",
    "    pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "    plt.yticks(pos, names)\n",
    "    plt.xlabel('Relative Importance')\n",
    "    plt.title('Variable Importance')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances(X_train, gbr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Model results\n",
    "- Boosting model has significantly improved the RMSE to 4\n",
    "- Try Data Augmentation to drive RMSE lower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Augmentation\n",
    "- Weather might play a role in taxi fare\n",
    "- Big Query has weather data\n",
    "- Choose 1 station within new york to augment weather data to original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>prcp</th>\n",
       "      <th>sndp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>35.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>33.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>46.9</td>\n",
       "      <td>60.1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>44.1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  min_temp  max_temp  prcp   sndp\n",
       "0  2016      4    3      35.1      61.0  0.24  999.9\n",
       "1  2016      3   21      33.1      50.0  0.14  999.9\n",
       "2  2016      8    5      70.0      86.0  0.00  999.9\n",
       "3  2016     10   31      46.9      60.1  0.76  999.9\n",
       "4  2016      3   25      44.1      73.0  0.00  999.9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the weather data\n",
    "weather_df = pd.read_csv('weather_data.csv')\n",
    "weather_df.drop(['Unnamed: 0'], axis = 1, inplace = True)\n",
    "weather_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>lat_dif</th>\n",
       "      <th>lon_dif</th>\n",
       "      <th>pickup_geohash</th>\n",
       "      <th>dropoff_geohash</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>prcp</th>\n",
       "      <th>sndp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-07-18 11:25:58</td>\n",
       "      <td>2015-07-18 11:43:47</td>\n",
       "      <td>1</td>\n",
       "      <td>7.21</td>\n",
       "      <td>-73.862762</td>\n",
       "      <td>40.769028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.949203</td>\n",
       "      <td>...</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.046444</td>\n",
       "      <td>0.086441</td>\n",
       "      <td>dr5rz</td>\n",
       "      <td>dr5rt</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-18 05:45:28</td>\n",
       "      <td>2015-07-18 06:10:13</td>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>-74.011505</td>\n",
       "      <td>40.702785</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-74.088554</td>\n",
       "      <td>...</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>5</td>\n",
       "      <td>0.103264</td>\n",
       "      <td>0.077049</td>\n",
       "      <td>dr5re</td>\n",
       "      <td>dr5qf</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-18 00:22:24</td>\n",
       "      <td>2015-07-18 00:50:07</td>\n",
       "      <td>1</td>\n",
       "      <td>6.10</td>\n",
       "      <td>-74.008011</td>\n",
       "      <td>40.741375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.963081</td>\n",
       "      <td>...</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.062584</td>\n",
       "      <td>-0.044930</td>\n",
       "      <td>dr5rg</td>\n",
       "      <td>dr72h</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-18 21:58:53</td>\n",
       "      <td>2015-07-18 22:16:51</td>\n",
       "      <td>1</td>\n",
       "      <td>7.50</td>\n",
       "      <td>-73.970001</td>\n",
       "      <td>40.785599</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.933266</td>\n",
       "      <td>...</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.071918</td>\n",
       "      <td>-0.036736</td>\n",
       "      <td>dr72h</td>\n",
       "      <td>dr72m</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-07-18 11:19:37</td>\n",
       "      <td>2015-07-18 11:46:54</td>\n",
       "      <td>2</td>\n",
       "      <td>10.00</td>\n",
       "      <td>-73.862732</td>\n",
       "      <td>40.769016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>-73.987709</td>\n",
       "      <td>...</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.047882</td>\n",
       "      <td>0.124977</td>\n",
       "      <td>dr5rz</td>\n",
       "      <td>dr5rs</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   vendor_id     pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "0          2 2015-07-18 11:25:58 2015-07-18 11:43:47                1   \n",
       "1          1 2015-07-18 05:45:28 2015-07-18 06:10:13                1   \n",
       "2          1 2015-07-18 00:22:24 2015-07-18 00:50:07                1   \n",
       "3          1 2015-07-18 21:58:53 2015-07-18 22:16:51                1   \n",
       "4          1 2015-07-18 11:19:37 2015-07-18 11:46:54                2   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  rate_code  \\\n",
       "0           7.21        -73.862762        40.769028        1.0   \n",
       "1          14.00        -74.011505        40.702785        1.0   \n",
       "2           6.10        -74.008011        40.741375        1.0   \n",
       "3           7.50        -73.970001        40.785599        1.0   \n",
       "4          10.00        -73.862732        40.769016        1.0   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  ...  day_of_week  hour_of_day  \\\n",
       "0                  N         -73.949203  ...     Saturday           11   \n",
       "1                  N         -74.088554  ...     Saturday            5   \n",
       "2                  N         -73.963081  ...     Saturday            0   \n",
       "3                  N         -73.933266  ...     Saturday           21   \n",
       "4                  N         -73.987709  ...     Saturday           11   \n",
       "\n",
       "    lat_dif   lon_dif  pickup_geohash  dropoff_geohash  min_temp  max_temp  \\\n",
       "0  0.046444  0.086441           dr5rz            dr5rt      68.0      86.0   \n",
       "1  0.103264  0.077049           dr5re            dr5qf      68.0      86.0   \n",
       "2 -0.062584 -0.044930           dr5rg            dr72h      68.0      86.0   \n",
       "3 -0.071918 -0.036736           dr72h            dr72m      68.0      86.0   \n",
       "4  0.047882  0.124977           dr5rz            dr5rs      68.0      86.0   \n",
       "\n",
       "   prcp   sndp  \n",
       "0   0.0  999.9  \n",
       "1   0.0  999.9  \n",
       "2   0.0  999.9  \n",
       "3   0.0  999.9  \n",
       "4   0.0  999.9  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge weather data with trips data\n",
    "df_augmented = df_filtered.merge(weather_df, how='inner', on = ['year', 'month', 'day'])\n",
    "df_augmented.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check distribution of newly appended columns\n",
    "df_augmented[['min_temp','max_temp', 'prcp', 'sndp']].hist(figsize = (20,5), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prcp and sndp seem to have mainly 2 classes. Manually encode them\n",
    "df_augmented['prcp_class'] = np.where(df_augmented['prcp']>=90, 'rain', 'dry')\n",
    "df_augmented['sndp_class'] = np.where(df_augmented['sndp']>=900, 'snow', 'dry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split response (y) and features (X)\n",
    "y = df_augmented[['fare_amount', 'year']]\n",
    "X = df_augmented[['passenger_count', 'trip_distance', 'trip_duration', 'year', 'month', 'day_of_week', 'hour_of_day', 'lat_dif', 'lon_dif', 'pickup_geohash', 'min_temp', 'max_temp', 'prcp_class', 'sndp_class']]\n",
    "X_encoded = pd.get_dummies(X, columns=['month', 'day_of_week', 'hour_of_day', 'pickup_geohash', 'prcp_class', 'sndp_class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Training and Testing sets\n",
    "X_train, y_train, X_test, y_test = train_test_split(X_encoded, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Data results (2015 data set)----\n",
      "RMSE: $3.9\n",
      "R2: 0.90\n",
      "\n",
      "----Test Data results (2016 data set)----\n",
      "RMSE: $4.0\n",
      "R2: 0.89\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Gradient boosting regressor\n",
    "params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.01, 'loss': 'ls'}\n",
    "gbr = ensemble.GradientBoostingRegressor(**params)\n",
    "gbr.fit(X_train, y_train)\n",
    "\n",
    "model_results(X_train, y_train, X_test, y_test, gbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances(X_train, gbr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Augmentation Results\n",
    "- No difference in RMSE with additional weather features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "- Try tuning hyperparameters to reduce RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split response (y) and features (X)\n",
    "y = df_filtered[['fare_amount', 'year']]\n",
    "X = df_filtered[['passenger_count', 'trip_distance', 'trip_duration', 'year', 'month', 'day_of_week', 'hour_of_day', 'lat_dif', 'lon_dif', 'pickup_geohash']]\n",
    "X_encoded = pd.get_dummies(X, columns=['month', 'day_of_week', 'hour_of_day', 'pickup_geohash'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split Training and Testing sets\n",
    "X_train, y_train, X_test, y_test = train_test_split(X_encoded, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter Tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {\n",
    "    'learning_rate': [0.1, 0.15, 0.2],\n",
    "    'min_samples_split': [2, 10],\n",
    "    'min_samples_leaf': [2, 10],\n",
    "    'max_depth':[4, 5],\n",
    "    'n_estimators':[500, 1000]\n",
    "    }\n",
    "\n",
    "gbr_tuned = GridSearchCV(ensemble.GradientBoostingRegressor(), parameters, cv=5, n_jobs=4)\n",
    "gbr_tuned.fit(X_train, y_train)\n",
    "\n",
    "print(gbr_tuned.best_params_)\n",
    "model_results(X_train, y_train, X_test, y_test, gbr_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Training Data results (2015 data set)----\n",
      "RMSE: $2.4\n",
      "R2: 0.96\n",
      "\n",
      "----Test Data results (2016 data set)----\n",
      "RMSE: $3.5\n",
      "R2: 0.92\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Best parameters from Hyperparameter tuning\n",
    "params = {\n",
    "    'learning_rate':0.1,\n",
    "    'min_samples_split':10,\n",
    "    'min_samples_leaf':10,\n",
    "    'max_depth':4,\n",
    "    'n_estimators':1000}\n",
    "gbr_tuned = ensemble.GradientBoostingRegressor(**params)\n",
    "gbr_tuned.fit(X_train, y_train)\n",
    "model_results(X_train, y_train, X_test, y_test, gbr_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Actual vs Predicted to visually check the correlation\n",
    "y_train_pred = gbr_tuned.predict(X_train)\n",
    "y_test_pred = gbr_tuned.predict(X_test)\n",
    "test_data_fit = LinearRegression().fit(y_test_pred.reshape(-1, 1),y_test)\n",
    "slope = test_data_fit.coef_[0]\n",
    "intercept = test_data_fit.intercept_\n",
    "line = slope * y_test_pred + intercept\n",
    "\n",
    "#plt.scatter(y_train_pred, y_train, alpha = 0.5, label = 'Training Data (2015)')\n",
    "plt.scatter(y_test_pred, y_test, alpha = 0.5, label = 'Test Data (2016)')\n",
    "plt.plot(y_test_pred, line, 'r', label='y = ({:.2f}) x + ({:.2f})'.format(slope,intercept))\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.xlabel('Predicted Fare ($)')\n",
    "plt.ylabel('Actual Fare ($)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning Results\n",
    "- Hyperparameter tuning has reduced the RMSE to 3.5\n",
    "- There is a pretty strong correlation between predicted fare and actual fare on test data set\n",
    "- However, there is overfitting as the training dataset has lower RMSE than test data set indicating the model can be simplified a bit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "- Various methods were tried out to predict the fare amount of individual trips\n",
    "- Data from 2015 was used to build the models and data from 2016 was used to test the model performance\n",
    "- RMSE (root mean square error) was chosen as a metric to evaluate model performance as it can directly give us the error in dollars\n",
    "- A gradient boosting regressor with hyperparameter tuning provided the best result of RMSE = &#36; 3.5 for testing data\n",
    "- The gradient boosting regressor RMSE is &#36; 2 better than a simple intuitive modeling approach\n",
    "\n",
    "## Model RMSE on test data compilation\n",
    "- Simple bivariate linear regression RMSE: &#36; 5.5\n",
    "- Multivariate linear regression with new features engineered RMSE: &#36; 5.1\n",
    "- Gradient boosting regression RMSE: &#36; 4.0\n",
    "- Gradient boosting regression with Weather data augmented RMSE: &#36; 4.0\n",
    "- Gradient boosting regression with Hyper Parameters tuning RMSE: &#36; 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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