01-intro/module1_study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47a168c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.metrics import root_mean_squared_error\n",
    "\n",
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f2a6681",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('https://d37ci6vzurychx.cloudfront.net/trip-data/green_tripdata_2021-01.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5654f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</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>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:15:56</td>\n",
       "      <td>2021-01-01 00:19:52</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>151</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:25:59</td>\n",
       "      <td>2021-01-01 00:34:44</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>166</td>\n",
       "      <td>239</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:45:57</td>\n",
       "      <td>2021-01-01 00:51:55</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-12-31 23:57:51</td>\n",
       "      <td>2021-01-01 00:04:56</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:16:36</td>\n",
       "      <td>2021-01-01 00:16:40</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.00</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>None</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-52.80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0         2  2021-01-01 00:15:56   2021-01-01 00:19:52                  N   \n",
       "1         2  2021-01-01 00:25:59   2021-01-01 00:34:44                  N   \n",
       "2         2  2021-01-01 00:45:57   2021-01-01 00:51:55                  N   \n",
       "3         2  2020-12-31 23:57:51   2021-01-01 00:04:56                  N   \n",
       "4         2  2021-01-01 00:16:36   2021-01-01 00:16:40                  N   \n",
       "\n",
       "   RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "0         1.0            43           151              1.0           1.01   \n",
       "1         1.0           166           239              1.0           2.53   \n",
       "2         1.0            41            42              1.0           1.12   \n",
       "3         1.0           168            75              1.0           1.99   \n",
       "4         2.0           265           265              3.0           0.00   \n",
       "\n",
       "   fare_amount  extra  mta_tax  tip_amount  tolls_amount ehail_fee  \\\n",
       "0          5.5    0.5      0.5        0.00           0.0      None   \n",
       "1         10.0    0.5      0.5        2.81           0.0      None   \n",
       "2          6.0    0.5      0.5        1.00           0.0      None   \n",
       "3          8.0    0.5      0.5        0.00           0.0      None   \n",
       "4        -52.0    0.0     -0.5        0.00           0.0      None   \n",
       "\n",
       "   improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                    0.3          6.80           2.0        1.0   \n",
       "1                    0.3         16.86           1.0        1.0   \n",
       "2                    0.3          8.30           1.0        1.0   \n",
       "3                    0.3          9.30           2.0        1.0   \n",
       "4                   -0.3        -52.80           3.0        1.0   \n",
       "\n",
       "   congestion_surcharge  \n",
       "0                  0.00  \n",
       "1                  2.75  \n",
       "2                  0.00  \n",
       "3                  0.00  \n",
       "4                  0.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83498b66-dfe5-4a1b-8fe9-7774b2187163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VendorID                     0\n",
       "lpep_pickup_datetime         0\n",
       "lpep_dropoff_datetime        0\n",
       "store_and_fwd_flag       36047\n",
       "RatecodeID               36047\n",
       "PULocationID                 0\n",
       "DOLocationID                 0\n",
       "passenger_count          36047\n",
       "trip_distance                0\n",
       "fare_amount                  0\n",
       "extra                        0\n",
       "mta_tax                      0\n",
       "tip_amount                   0\n",
       "tolls_amount                 0\n",
       "ehail_fee                76518\n",
       "improvement_surcharge        0\n",
       "total_amount                 0\n",
       "payment_type             36047\n",
       "trip_type                36047\n",
       "congestion_surcharge     36047\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f608f170-0326-406b-8e9c-c138522b3af5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime',\n",
       "       'store_and_fwd_flag', 'RatecodeID', 'PULocationID', 'DOLocationID',\n",
       "       'passenger_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax',\n",
       "       'tip_amount', 'tolls_amount', 'ehail_fee', 'improvement_surcharge',\n",
       "       'total_amount', 'payment_type', 'trip_type', 'congestion_surcharge'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14e911fb-8632-4c0a-990b-03e228af6f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76518 entries, 0 to 76517\n",
      "Data columns (total 20 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   VendorID               76518 non-null  int64         \n",
      " 1   lpep_pickup_datetime   76518 non-null  datetime64[us]\n",
      " 2   lpep_dropoff_datetime  76518 non-null  datetime64[us]\n",
      " 3   store_and_fwd_flag     40471 non-null  object        \n",
      " 4   RatecodeID             40471 non-null  float64       \n",
      " 5   PULocationID           76518 non-null  int64         \n",
      " 6   DOLocationID           76518 non-null  int64         \n",
      " 7   passenger_count        40471 non-null  float64       \n",
      " 8   trip_distance          76518 non-null  float64       \n",
      " 9   fare_amount            76518 non-null  float64       \n",
      " 10  extra                  76518 non-null  float64       \n",
      " 11  mta_tax                76518 non-null  float64       \n",
      " 12  tip_amount             76518 non-null  float64       \n",
      " 13  tolls_amount           76518 non-null  float64       \n",
      " 14  ehail_fee              0 non-null      object        \n",
      " 15  improvement_surcharge  76518 non-null  float64       \n",
      " 16  total_amount           76518 non-null  float64       \n",
      " 17  payment_type           40471 non-null  float64       \n",
      " 18  trip_type              40471 non-null  float64       \n",
      " 19  congestion_surcharge   40471 non-null  float64       \n",
      "dtypes: datetime64[us](2), float64(13), int64(3), object(2)\n",
      "memory usage: 11.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02846c66-5ec6-4d9e-805a-18f1cc01ee8f",
   "metadata": {},
   "source": [
    "Trip type 1 => Street Hail <br>\n",
    "Trip type 2 => Dispatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd53ac63-0b69-42b7-b6ab-b855858bd5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df[\"trip_type\"]==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "697836f2-a761-4b79-81e1-860496fde1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec5dc305-05a1-4e79-95f8-1dda73ee7343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:19:46')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td = df.duration.iloc[2]\n",
    "td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d8ef86c-209a-404e-b321-e7f090dd6c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1186.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7465d337-8c52-483c-9c28-ae713ee366cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.duration = df.duration.apply(lambda td: td.total_seconds()/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03c00439-460f-4ae0-a4db-7c858218b828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:19:14</td>\n",
       "      <td>2021-01-01 00:19:21</td>\n",
       "      <td>N</td>\n",
       "      <td>5.0</td>\n",
       "      <td>265</td>\n",
       "      <td>265</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:29:05</td>\n",
       "      <td>2021-01-01 00:29:07</td>\n",
       "      <td>N</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42</td>\n",
       "      <td>264</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:35:29</td>\n",
       "      <td>2021-01-01 00:55:15</td>\n",
       "      <td>N</td>\n",
       "      <td>5.0</td>\n",
       "      <td>74</td>\n",
       "      <td>247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.64</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>13.30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 23:12:41</td>\n",
       "      <td>2021-01-02 01:13:53</td>\n",
       "      <td>N</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 01:49:29</td>\n",
       "      <td>2021-01-01 01:49:33</td>\n",
       "      <td>N</td>\n",
       "      <td>5.0</td>\n",
       "      <td>244</td>\n",
       "      <td>244</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>14.76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "6          2  2021-01-01 00:19:14   2021-01-01 00:19:21                  N   \n",
       "15         2  2021-01-01 00:29:05   2021-01-01 00:29:07                  N   \n",
       "30         2  2021-01-01 00:35:29   2021-01-01 00:55:15                  N   \n",
       "43         2  2021-01-01 23:12:41   2021-01-02 01:13:53                  N   \n",
       "45         2  2021-01-01 01:49:29   2021-01-01 01:49:33                  N   \n",
       "\n",
       "    RatecodeID  PULocationID  DOLocationID  passenger_count  trip_distance  \\\n",
       "6          5.0           265           265              1.0           0.00   \n",
       "15         5.0            42           264              1.0           0.00   \n",
       "30         5.0            74           247              1.0           3.64   \n",
       "43         5.0            95            82              1.0           0.00   \n",
       "45         5.0           244           244              5.0           0.02   \n",
       "\n",
       "    fare_amount  ...  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "6         180.0  ...      0.0       36.06           0.0       None   \n",
       "15         10.0  ...      0.0        2.06           0.0       None   \n",
       "30         13.0  ...      0.0        0.00           0.0       None   \n",
       "43          9.7  ...      0.0        2.00           0.0       None   \n",
       "45         12.0  ...      0.0        2.46           0.0       None   \n",
       "\n",
       "   improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "6                    0.3        216.36           1.0        2.0   \n",
       "15                   0.3         12.36           1.0        2.0   \n",
       "30                   0.3         13.30           2.0        2.0   \n",
       "43                   0.3         12.00           1.0        2.0   \n",
       "45                   0.3         14.76           1.0        2.0   \n",
       "\n",
       "    congestion_surcharge    duration  \n",
       "6                    0.0    0.116667  \n",
       "15                   0.0    0.033333  \n",
       "30                   0.0   19.766667  \n",
       "43                   0.0  121.200000  \n",
       "45                   0.0    0.066667  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edbf21d8-aa01-45ad-b3e3-dec26663182e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_27732/3426635498.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df.duration)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2cd6314-22cd-4a71-951f-cf47c0c25461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1238.000000\n",
       "mean       18.305493\n",
       "std       108.364382\n",
       "min         0.000000\n",
       "50%         1.666667\n",
       "95%        35.604167\n",
       "98%        59.339333\n",
       "99%       149.837833\n",
       "max      1416.100000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duration.describe(percentiles=[0.95, 0.98, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "819e62b9-163b-4a51-bc72-971629e2c49c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5169628432956381"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.duration >= 1) & (df.duration <= 60)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f567161-d8fe-4b10-a1df-18ef4cd478da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9798061389337641"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.duration >= 0 ) & (df.duration <= 60)).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076ad762-8f51-40fa-a11b-8f4f041322db",
   "metadata": {},
   "source": [
    "Trips between duration between 1 min and 60 mins are approximately over 51%.<br>\n",
    "Trips under duration of 60 mins are about 98%. <br>\n",
    "That means a lot of trips are less than 1 minutes.<br>\n",
    "Lecturer tested again without filtering trip type 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db06a7b8-4a1e-49ba-a35d-ff31a5a8e77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('https://d37ci6vzurychx.cloudfront.net/trip-data/green_tripdata_2021-01.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb92b3df-f998-44fe-985b-75cf817af4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6bf8e3ef-2493-48e7-be3b-6315404de791",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.duration = df.duration.apply(lambda td: td.total_seconds()/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f8e97189-3ed7-4f4c-b048-056ad88e1849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76518.000000\n",
       "mean        19.927896\n",
       "std         59.338594\n",
       "min          0.000000\n",
       "50%         13.883333\n",
       "95%         44.000000\n",
       "98%         56.000000\n",
       "99%         67.158167\n",
       "max       1439.600000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duration.describe(percentiles=[0.95, 0.98, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6da0e5da-fd04-4060-aef5-c03526c21c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_27732/3426635498.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df.duration)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='duration', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5147e57-d736-4a5c-ad3a-a76e0d337e2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9658903787344154"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.duration >= 1) & (df.duration <= 60)).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77342620-dc29-451d-8f76-9aa96440e4f0",
   "metadata": {},
   "source": [
    "As for features, PULocationID, DOLocationID and trip distance are choosen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fd3a3bd-0472-476e-aa60-fc6312192e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa29e92f-c5c8-4b50-baef-34cedb8ef9ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[categorical] = df[categorical].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "adcddd29-3f4c-4ccd-a962-d375fb385821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'PULocationID': '43', 'DOLocationID': '151', 'trip_distance': 1.01},\n",
       " {'PULocationID': '166', 'DOLocationID': '239', 'trip_distance': 2.53},\n",
       " {'PULocationID': '41', 'DOLocationID': '42', 'trip_distance': 1.12},\n",
       " {'PULocationID': '168', 'DOLocationID': '75', 'trip_distance': 1.99},\n",
       " {'PULocationID': '265', 'DOLocationID': '265', 'trip_distance': 0.0},\n",
       " {'PULocationID': '265', 'DOLocationID': '265', 'trip_distance': 0.0},\n",
       " {'PULocationID': '265', 'DOLocationID': '265', 'trip_distance': 0.0},\n",
       " {'PULocationID': '75', 'DOLocationID': '75', 'trip_distance': 0.45},\n",
       " {'PULocationID': '225', 'DOLocationID': '225', 'trip_distance': 0.0},\n",
       " {'PULocationID': '225', 'DOLocationID': '265', 'trip_distance': 12.19}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[categorical + numerical].iloc[:10].to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b20a30f-8938-44cb-ab1d-740bcec3f5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dataframe(filename):\n",
    "    df = pd.read_parquet(filename)\n",
    "    df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime\n",
    "    df.duration = df.duration.apply(lambda td: td.total_seconds()/60)\n",
    "\n",
    "    df = df[(df.duration >= 1) & (df.duration <= 60)]\n",
    "\n",
    "    categorical = ['PULocationID', 'DOLocationID']\n",
    "    numerical = ['trip_distance']\n",
    "    df[categorical] = df[categorical].astype(str)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a9034256-355c-45ed-ac5a-9aaf4a606ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = read_dataframe('https://d37ci6vzurychx.cloudfront.net/trip-data/green_tripdata_2021-01.parquet')\n",
    "df_val = read_dataframe('https://d37ci6vzurychx.cloudfront.net/trip-data/green_tripdata_2021-02.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "08429829-3f2a-48ba-9187-3495a8049d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73908, 61921)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "efadc1d8-1da5-4b4e-9408-eb7f9ac72e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:15:56</td>\n",
       "      <td>2021-01-01 00:19:52</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>151</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>5.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:25:59</td>\n",
       "      <td>2021-01-01 00:34:44</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>166</td>\n",
       "      <td>239</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.86</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:45:57</td>\n",
       "      <td>2021-01-01 00:51:55</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-12-31 23:57:51</td>\n",
       "      <td>2021-01-01 00:04:56</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-01-01 00:26:31</td>\n",
       "      <td>2021-01-01 00:28:50</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.76</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.316667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0         2  2021-01-01 00:15:56   2021-01-01 00:19:52                  N   \n",
       "1         2  2021-01-01 00:25:59   2021-01-01 00:34:44                  N   \n",
       "2         2  2021-01-01 00:45:57   2021-01-01 00:51:55                  N   \n",
       "3         2  2020-12-31 23:57:51   2021-01-01 00:04:56                  N   \n",
       "7         2  2021-01-01 00:26:31   2021-01-01 00:28:50                  N   \n",
       "\n",
       "   RatecodeID PULocationID DOLocationID  passenger_count  trip_distance  \\\n",
       "0         1.0           43          151              1.0           1.01   \n",
       "1         1.0          166          239              1.0           2.53   \n",
       "2         1.0           41           42              1.0           1.12   \n",
       "3         1.0          168           75              1.0           1.99   \n",
       "7         1.0           75           75              6.0           0.45   \n",
       "\n",
       "   fare_amount  ...  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0          5.5  ...      0.5        0.00           0.0       None   \n",
       "1         10.0  ...      0.5        2.81           0.0       None   \n",
       "2          6.0  ...      0.5        1.00           0.0       None   \n",
       "3          8.0  ...      0.5        0.00           0.0       None   \n",
       "7          3.5  ...      0.5        0.96           0.0       None   \n",
       "\n",
       "  improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                   0.3          6.80           2.0        1.0   \n",
       "1                   0.3         16.86           1.0        1.0   \n",
       "2                   0.3          8.30           1.0        1.0   \n",
       "3                   0.3          9.30           2.0        1.0   \n",
       "7                   0.3          5.76           1.0        1.0   \n",
       "\n",
       "   congestion_surcharge  duration  \n",
       "0                  0.00  3.933333  \n",
       "1                  2.75  8.750000  \n",
       "2                  0.00  5.966667  \n",
       "3                  0.00  7.083333  \n",
       "7                  0.00  2.316667  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9f72e094-214d-44d8-8721-105e8ea7b8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VendorID</th>\n",
       "      <th>lpep_pickup_datetime</th>\n",
       "      <th>lpep_dropoff_datetime</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>...</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>ehail_fee</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>congestion_surcharge</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 00:34:03</td>\n",
       "      <td>2021-02-01 00:51:58</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130</td>\n",
       "      <td>205</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.66</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>25.30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 00:04:00</td>\n",
       "      <td>2021-02-01 00:10:30</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152</td>\n",
       "      <td>244</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>6.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>7.80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 00:18:51</td>\n",
       "      <td>2021-02-01 00:34:06</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.93</td>\n",
       "      <td>16.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.55</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>15.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 00:53:27</td>\n",
       "      <td>2021-02-01 01:11:41</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>152</td>\n",
       "      <td>241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>22.30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-01 00:57:46</td>\n",
       "      <td>2021-02-01 01:06:44</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.89</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   VendorID lpep_pickup_datetime lpep_dropoff_datetime store_and_fwd_flag  \\\n",
       "0         2  2021-02-01 00:34:03   2021-02-01 00:51:58                  N   \n",
       "1         2  2021-02-01 00:04:00   2021-02-01 00:10:30                  N   \n",
       "2         2  2021-02-01 00:18:51   2021-02-01 00:34:06                  N   \n",
       "3         2  2021-02-01 00:53:27   2021-02-01 01:11:41                  N   \n",
       "4         2  2021-02-01 00:57:46   2021-02-01 01:06:44                  N   \n",
       "\n",
       "   RatecodeID PULocationID DOLocationID  passenger_count  trip_distance  \\\n",
       "0         1.0          130          205              5.0           3.66   \n",
       "1         1.0          152          244              1.0           1.10   \n",
       "2         1.0          152           48              1.0           4.93   \n",
       "3         1.0          152          241              1.0           6.70   \n",
       "4         1.0           75           42              1.0           1.89   \n",
       "\n",
       "   fare_amount  ...  mta_tax  tip_amount  tolls_amount  ehail_fee  \\\n",
       "0         14.0  ...      0.5       10.00           0.0       None   \n",
       "1          6.5  ...      0.5        0.00           0.0       None   \n",
       "2         16.5  ...      0.5        0.00           0.0       None   \n",
       "3         21.0  ...      0.5        0.00           0.0       None   \n",
       "4          8.5  ...      0.5        2.45           0.0       None   \n",
       "\n",
       "  improvement_surcharge  total_amount  payment_type  trip_type  \\\n",
       "0                   0.3         25.30           1.0        1.0   \n",
       "1                   0.3          7.80           2.0        1.0   \n",
       "2                   0.3         20.55           2.0        1.0   \n",
       "3                   0.3         22.30           2.0        1.0   \n",
       "4                   0.3         12.25           1.0        1.0   \n",
       "\n",
       "   congestion_surcharge   duration  \n",
       "0                  0.00  17.916667  \n",
       "1                  0.00   6.500000  \n",
       "2                  2.75  15.250000  \n",
       "3                  0.00  18.233333  \n",
       "4                  0.00   8.966667  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9d1c7dd3-379b-4ee2-8994-f3d9ae71c731",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7eb0815e-eb5c-4cd7-8ab4-3c15f750396a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer()\n",
    "\n",
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a264522-948f-41b9-8bcd-d8fefaaf851f",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'duration'\n",
    "y_train = df_train[target].values\n",
    "y_val = df_val[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f56a1592-8b26-4809-ba17-cb445fb84b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.499110712717727"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d831060b-1359-4be5-b4d5-1900b17b56d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.481255832596219"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = Lasso(alpha=0.001)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0378079a-db93-48f8-83a0-fac1af68965a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.47641000263633"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = Ridge(alpha=0.01)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ccd7fa22-b37f-485a-bb1f-4193a238020c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['PU_DO'] = df_train['PULocationID'] + '_' + df_train['DOLocationID']\n",
    "df_val['PU_DO'] = df_val['PULocationID'] + '_' + df_val['DOLocationID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d444b5f9-eea5-471e-a618-bc75dd9cca29",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PU_DO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "389dc091-0e95-4821-acf8-d6205872398d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c9803a75-5452-461c-aecf-6f8c3c2ff766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.233436225720547"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = Lasso(alpha=0.001)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8b304fab-28f4-47cc-917f-9cf2ab2f637a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.51044109720127"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = Ridge(alpha=0.001)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4b419bc3-cc7c-49ed-9162-62242c1c1213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.758715209663881"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "root_mean_squared_error(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9e953261-51e9-49d5-9155-4040e2bab590",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('models', exist_ok=True)\n",
    "\n",
    "with open('models/lin_reg.bin','wb') as fout:\n",
    "    pickle.dump((dv,lr),fout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8c7abf5-2b6c-4a6c-a380-c94e79210dd0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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