capitalbikeshare-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import zipfile\n",
    "from io import BytesIO\n",
    "from haversine import haversine, Unit\n",
    "from category_encoders import OneHotEncoder\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(\"./data/202205-capitalbikeshare-tripdata.zip\", \"r\") as f:\n",
    "    for name in f.namelist():\n",
    "        if name.endswith('.csv'):\n",
    "            with f.open(name) as zd:\n",
    "                df = pd.read_csv(zd)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type           started_at             ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike  2022-05-12 18:19:28  2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike  2022-05-27 16:24:42  2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike  2022-05-29 19:16:55  2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike  2022-05-11 12:30:58  2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike  2022-05-03 19:16:22  2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual  \n",
       "0  38.930800 -77.031500        member  \n",
       "1  38.875010 -77.002400        member  \n",
       "2  38.920669 -77.043680        member  \n",
       "3  38.919018 -77.034449        member  \n",
       "4  39.032029 -77.047350        casual  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(332821, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 332821 entries, 0 to 332820\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   ride_id             332821 non-null  object \n",
      " 1   rideable_type       332821 non-null  object \n",
      " 2   started_at          332821 non-null  object \n",
      " 3   ended_at            332821 non-null  object \n",
      " 4   start_station_name  319064 non-null  object \n",
      " 5   start_station_id    319064 non-null  float64\n",
      " 6   end_station_name    317296 non-null  object \n",
      " 7   end_station_id      317296 non-null  float64\n",
      " 8   start_lat           332821 non-null  float64\n",
      " 9   start_lng           332821 non-null  float64\n",
      " 10  end_lat             332267 non-null  float64\n",
      " 11  end_lng             332267 non-null  float64\n",
      " 12  member_casual       332821 non-null  object \n",
      "dtypes: float64(6), object(7)\n",
      "memory usage: 33.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 332821 entries, 0 to 332820\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   ride_id             332821 non-null  object        \n",
      " 1   rideable_type       332821 non-null  object        \n",
      " 2   started_at          332821 non-null  datetime64[ns]\n",
      " 3   ended_at            332821 non-null  datetime64[ns]\n",
      " 4   start_station_name  319064 non-null  object        \n",
      " 5   start_station_id    319064 non-null  float64       \n",
      " 6   end_station_name    317296 non-null  object        \n",
      " 7   end_station_id      317296 non-null  float64       \n",
      " 8   start_lat           332821 non-null  float64       \n",
      " 9   start_lng           332821 non-null  float64       \n",
      " 10  end_lat             332267 non-null  float64       \n",
      " 11  end_lng             332267 non-null  float64       \n",
      " 12  member_casual       332821 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(6), object(5)\n",
      "memory usage: 33.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.started_at = pd.to_datetime(df.started_at)\n",
    "df.ended_at = pd.to_datetime(df.ended_at)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual  \n",
       "0  38.930800 -77.031500        member  \n",
       "1  38.875010 -77.002400        member  \n",
       "2  38.920669 -77.043680        member  \n",
       "3  38.919018 -77.034449        member  \n",
       "4  39.032029 -77.047350        casual  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "      <td>7.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "      <td>27.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "      <td>4.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "      <td>34.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual   duration  \n",
       "0  38.930800 -77.031500        member   7.216667  \n",
       "1  38.875010 -77.002400        member  27.150000  \n",
       "2  38.920669 -77.043680        member   5.600000  \n",
       "3  38.919018 -77.034449        member   4.633333  \n",
       "4  39.032029 -77.047350        casual  34.133333  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"duration\"] = df.ended_at - df.started_at\n",
    "df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "310389"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df.duration >= 1) & (df.duration <= 60)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_parquet(\"./data/202205-capitalbikeshare-tripdata.parquet\", engine=\"pyarrow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "### Read parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "      <td>7.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "      <td>27.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "      <td>4.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "      <td>34.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual   duration  \n",
       "0  38.930800 -77.031500        member   7.216667  \n",
       "1  38.875010 -77.002400        member  27.150000  \n",
       "2  38.920669 -77.043680        member   5.600000  \n",
       "3  38.919018 -77.034449        member   4.633333  \n",
       "4  39.032029 -77.047350        casual  34.133333  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"./data/202205-capitalbikeshare-tripdata.parquet\", engine=\"pyarrow\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 332821 entries, 0 to 332820\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   ride_id             332821 non-null  object        \n",
      " 1   rideable_type       332821 non-null  object        \n",
      " 2   started_at          332821 non-null  datetime64[ns]\n",
      " 3   ended_at            332821 non-null  datetime64[ns]\n",
      " 4   start_station_name  319064 non-null  object        \n",
      " 5   start_station_id    319064 non-null  float64       \n",
      " 6   end_station_name    317296 non-null  object        \n",
      " 7   end_station_id      317296 non-null  float64       \n",
      " 8   start_lat           332821 non-null  float64       \n",
      " 9   start_lng           332821 non-null  float64       \n",
      " 10  end_lat             332267 non-null  float64       \n",
      " 11  end_lng             332267 non-null  float64       \n",
      " 12  member_casual       332821 non-null  object        \n",
      " 13  duration            332821 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(7), object(5)\n",
      "memory usage: 35.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Location Data: \"start_lat\" and \"start_lng\"\n",
    "\n",
    "**Since latitude and longitude are based on a coordinate system, a good way to visualize them is to create a scatter plot on top of a map. A great tool for this is the scatter_mapbox from the plotly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = px.scatter_mapbox(\n",
    "#     df,\n",
    "#     lat=\"start_lat\",\n",
    "#     lon=\"start_lng\",\n",
    "#     center={\"lat\": 38.91, \"lon\": -77.04},\n",
    "#     width=1000,\n",
    "#     height=1000,\n",
    "#     hover_data=[\"start_station_name\"]\n",
    "# )\n",
    "\n",
    "# fig.update_layout(mapbox_style=\"open-street-map\")\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Location Data: \"end_lat\" and \"end_lng\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = px.scatter_mapbox(\n",
    "#     df,\n",
    "#     lat=\"end_lat\",\n",
    "#     lon=\"end_lng\",\n",
    "#     center={\"lat\": 38.91, \"lon\": -77.04},\n",
    "#     width=1000,\n",
    "#     height=1000,\n",
    "#     hover_data=[\"start_station_name\"]\n",
    "# )\n",
    "\n",
    "# fig.update_layout(mapbox_style=\"open-street-map\")\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Data: \"rideable_type, member_casual and start_station_name, end_station_name\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rideable_type\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['classic_bike', 'electric_bike', 'docked_bike'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rideable_type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classic_bike     254129\n",
       "electric_bike     52933\n",
       "docked_bike       25759\n",
       "Name: rideable_type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rideable_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"rideable_type\", data=df)\n",
    "plt.title(\"Ride Type Distribution\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "ride_types = df[\"rideable_type\"].value_counts()\n",
    "sns.barplot(x=ride_types.index, y=ride_types)\n",
    "plt.title(\"Ride Type Distribution\")\n",
    "plt.xlabel(\"Ride Types\")\n",
    "plt.ylabel(\"Ride Type Count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"member_casual\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['member', 'casual'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"member_casual\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member    185200\n",
       "casual    147621\n",
       "Name: member_casual, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"member_casual\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "member_types = df[\"member_casual\"].value_counts()\n",
    "sns.barplot(x=member_types.index, y=member_types)\n",
    "plt.title(\"Member Type Distribution\")\n",
    "plt.xlabel(\"Member Types\")\n",
    "plt.ylabel(\"Member Type Count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "687"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"start_station_name\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['15th & Euclid St NW', '17th St & Massachusetts Ave NW',\n",
       "       'Windham Ln & Amherst Ave', 'King Farm Blvd & Pleasant Dr',\n",
       "       'Clarendon Blvd & Pierce St', 'C & O Canal & Wisconsin Ave NW',\n",
       "       '19th & East Capitol St SE',\n",
       "       'Penrose Sq/Columbia Pike & S Barton St', '18th & L St NW',\n",
       "       'Fairfax Dr & N Randolph St', 'Georgetown Harbor / 30th St NW',\n",
       "       'Eckington Pl & Q St NE', 'Eads St & 22nd St S',\n",
       "       '13th St & New York Ave NW',\n",
       "       'Central Library / Quincy St & 10th St N', '21st & I St NW',\n",
       "       '3rd & Tingey St SE', '3rd & M St NE',\n",
       "       'Montgomery Ave & Waverly St ', '4th & D St NW / Judiciary Square'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"start_station_name\"].unique()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lincoln Memorial                                         4008\n",
       "Jefferson Dr & 14th St SW                                3526\n",
       "Smithsonian-National Mall / Jefferson Dr & 12th St SW    3302\n",
       "Columbus Circle / Union Station                          3296\n",
       "New Hampshire Ave & T St NW                              3136\n",
       "15th & P St NW                                           3100\n",
       "4th St & Madison Dr NW                                   3084\n",
       "Henry Bacon Dr & Lincoln Memorial Circle NW              2921\n",
       "1st & M St NE                                            2752\n",
       "Eastern Market Metro / Pennsylvania Ave & 8th St SE      2426\n",
       "Name: start_station_name, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"start_station_name\"].value_counts().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lincoln Memorial                                         3973\n",
       "Jefferson Dr & 14th St SW                                3553\n",
       "Columbus Circle / Union Station                          3451\n",
       "Smithsonian-National Mall / Jefferson Dr & 12th St SW    3266\n",
       "New Hampshire Ave & T St NW                              3153\n",
       "15th & P St NW                                           3131\n",
       "4th St & Madison Dr NW                                   3033\n",
       "Henry Bacon Dr & Lincoln Memorial Circle NW              2955\n",
       "1st & M St NE                                            2807\n",
       "Massachusetts Ave & Dupont Circle NW                     2697\n",
       "Name: end_station_name, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"end_station_name\"].value_counts().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id                   0\n",
       "rideable_type             0\n",
       "started_at                0\n",
       "ended_at                  0\n",
       "start_station_name    13757\n",
       "start_station_id      13757\n",
       "end_station_name      15525\n",
       "end_station_id        15525\n",
       "start_lat                 0\n",
       "start_lng                 0\n",
       "end_lat                 554\n",
       "end_lng                 554\n",
       "member_casual             0\n",
       "duration                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_sum = df.isna().sum()\n",
    "missing_values_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ride_id               0.000000\n",
       "rideable_type         0.000000\n",
       "started_at            0.000000\n",
       "ended_at              0.000000\n",
       "start_station_name    4.133453\n",
       "start_station_id      4.133453\n",
       "end_station_name      4.664670\n",
       "end_station_id        4.664670\n",
       "start_lat             0.000000\n",
       "start_lng             0.000000\n",
       "end_lat               0.166456\n",
       "end_lng               0.166456\n",
       "member_casual         0.000000\n",
       "duration              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_pct = (df.isna().sum() / len(df)) * 100\n",
    "missing_values_pct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "      <td>7.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "      <td>27.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "      <td>5.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "      <td>4.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "      <td>34.133333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual   duration  \n",
       "0  38.930800 -77.031500        member   7.216667  \n",
       "1  38.875010 -77.002400        member  27.150000  \n",
       "2  38.920669 -77.043680        member   5.600000  \n",
       "3  38.919018 -77.034449        member   4.633333  \n",
       "4  39.032029 -77.047350        casual  34.133333  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310801, 14)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 310801 entries, 0 to 332820\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   ride_id             310801 non-null  object        \n",
      " 1   rideable_type       310801 non-null  object        \n",
      " 2   started_at          310801 non-null  datetime64[ns]\n",
      " 3   ended_at            310801 non-null  datetime64[ns]\n",
      " 4   start_station_name  310801 non-null  object        \n",
      " 5   start_station_id    310801 non-null  float64       \n",
      " 6   end_station_name    310801 non-null  object        \n",
      " 7   end_station_id      310801 non-null  float64       \n",
      " 8   start_lat           310801 non-null  float64       \n",
      " 9   start_lng           310801 non-null  float64       \n",
      " 10  end_lat             310801 non-null  float64       \n",
      " 11  end_lng             310801 non-null  float64       \n",
      " 12  member_casual       310801 non-null  object        \n",
      " 13  duration            310801 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(7), object(5)\n",
      "memory usage: 35.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions\n",
    "\n",
    "### Which start_end_station combination has the longest duration time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_end_station_combination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "      <td>7.216667</td>\n",
       "      <td>15th &amp; Euclid St NW - Park Rd &amp; Holmead Pl NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW - 3rd &amp; Tingey ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>15th &amp; Euclid St NW - Columbia Rd &amp; Belmont St NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "      <td>4.633333</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW - 15th &amp; W St NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "      <td>34.133333</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave - Windham Ln &amp; Amhers...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual   duration  \\\n",
       "0  38.930800 -77.031500        member   7.216667   \n",
       "1  38.875010 -77.002400        member  27.150000   \n",
       "2  38.920669 -77.043680        member   5.600000   \n",
       "3  38.919018 -77.034449        member   4.633333   \n",
       "4  39.032029 -77.047350        casual  34.133333   \n",
       "\n",
       "                       start_end_station_combination  \n",
       "0      15th & Euclid St NW - Park Rd & Holmead Pl NW  \n",
       "1  17th St & Massachusetts Ave NW - 3rd & Tingey ...  \n",
       "2  15th & Euclid St NW - Columbia Rd & Belmont St NW  \n",
       "3    17th St & Massachusetts Ave NW - 15th & W St NW  \n",
       "4  Windham Ln & Amherst Ave - Windham Ln & Amhers...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"start_end_station_combination\"] = df[\"start_station_name\"] + \" - \" + df[\"end_station_name\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_end_station_combination\n",
       "4th & Florida Ave NE - 6035 Warehouse                                                                 41387.033333\n",
       "Washington Blvd & Walter Reed Dr  - George Mason Dr & Wilson Blvd                                     24253.333333\n",
       "8th & Eye St SE / Barracks Row - 6035 Warehouse                                                        8744.133333\n",
       "Benning Rd & East Capitol St NE / Benning Rd Metro - Clarendon Metro / Wilson Blvd & N Highland St     5562.066667\n",
       "Reston Town Center Transit Station - Reston YMCA                                                       5501.908333\n",
       "National Harbor Carousel - Braddock Rd Metro South                                                     5079.250000\n",
       "19th St & Constitution Ave NW - US Dept of State / Virginia Ave & 21st St NW                           3834.450000\n",
       "37th & O St NW / Georgetown University - 6035 Warehouse                                                2133.533333\n",
       "Tysons Corner Station - Jones Branch & Westbranch Dr                                                   1988.000000\n",
       "National Airport - Crystal Dr & 23rd St S                                                              1895.233333\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_duration_by_start_end_station = (\n",
    "    df.groupby(\"start_end_station_combination\")[\"duration\"]\n",
    "    .mean()\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "mean_duration_by_start_end_station.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "mean_duration_by_start_end_station.head(10).plot(\n",
    "    kind=\"bar\",\n",
    "    xlabel=\"Start End Station\",\n",
    "    ylabel=\"Mean Duration Time (Mins)\",\n",
    "    title=\"Mean Duration Time By Start End Station Combination\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>start_station_id</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485921</td>\n",
       "      <td>0.035452</td>\n",
       "      <td>0.171886</td>\n",
       "      <td>0.056575</td>\n",
       "      <td>0.079817</td>\n",
       "      <td>0.005281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_station_id</th>\n",
       "      <td>0.485921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044197</td>\n",
       "      <td>0.079893</td>\n",
       "      <td>0.032274</td>\n",
       "      <td>0.177052</td>\n",
       "      <td>0.015025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lat</th>\n",
       "      <td>0.035452</td>\n",
       "      <td>0.044197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077845</td>\n",
       "      <td>0.818460</td>\n",
       "      <td>-0.064425</td>\n",
       "      <td>-0.025452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lng</th>\n",
       "      <td>0.171886</td>\n",
       "      <td>0.079893</td>\n",
       "      <td>-0.077845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059892</td>\n",
       "      <td>0.827099</td>\n",
       "      <td>-0.021167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_lat</th>\n",
       "      <td>0.056575</td>\n",
       "      <td>0.032274</td>\n",
       "      <td>0.818460</td>\n",
       "      <td>-0.059892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073288</td>\n",
       "      <td>-0.021575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_lng</th>\n",
       "      <td>0.079817</td>\n",
       "      <td>0.177052</td>\n",
       "      <td>-0.064425</td>\n",
       "      <td>0.827099</td>\n",
       "      <td>-0.073288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.005281</td>\n",
       "      <td>0.015025</td>\n",
       "      <td>-0.025452</td>\n",
       "      <td>-0.021167</td>\n",
       "      <td>-0.021575</td>\n",
       "      <td>-0.022142</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  start_station_id  end_station_id  start_lat  start_lng  \\\n",
       "start_station_id          1.000000        0.485921   0.035452   0.171886   \n",
       "end_station_id            0.485921        1.000000   0.044197   0.079893   \n",
       "start_lat                 0.035452        0.044197   1.000000  -0.077845   \n",
       "start_lng                 0.171886        0.079893  -0.077845   1.000000   \n",
       "end_lat                   0.056575        0.032274   0.818460  -0.059892   \n",
       "end_lng                   0.079817        0.177052  -0.064425   0.827099   \n",
       "duration                  0.005281        0.015025  -0.025452  -0.021167   \n",
       "\n",
       "                   end_lat   end_lng  duration  \n",
       "start_station_id  0.056575  0.079817  0.005281  \n",
       "end_station_id    0.032274  0.177052  0.015025  \n",
       "start_lat         0.818460 -0.064425 -0.025452  \n",
       "start_lng        -0.059892  0.827099 -0.021167  \n",
       "end_lat           1.000000 -0.073288 -0.021575  \n",
       "end_lng          -0.073288  1.000000 -0.022142  \n",
       "duration         -0.021575 -0.022142  1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>start_station_id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485921</td>\n",
       "      <td>0.035452</td>\n",
       "      <td>0.171886</td>\n",
       "      <td>0.056575</td>\n",
       "      <td>0.079817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_station_id</th>\n",
       "      <td>0.485921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044197</td>\n",
       "      <td>0.079893</td>\n",
       "      <td>0.032274</td>\n",
       "      <td>0.177052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lat</th>\n",
       "      <td>0.035452</td>\n",
       "      <td>0.044197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077845</td>\n",
       "      <td>0.818460</td>\n",
       "      <td>-0.064425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_lng</th>\n",
       "      <td>0.171886</td>\n",
       "      <td>0.079893</td>\n",
       "      <td>-0.077845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059892</td>\n",
       "      <td>0.827099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_lat</th>\n",
       "      <td>0.056575</td>\n",
       "      <td>0.032274</td>\n",
       "      <td>0.818460</td>\n",
       "      <td>-0.059892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_lng</th>\n",
       "      <td>0.079817</td>\n",
       "      <td>0.177052</td>\n",
       "      <td>-0.064425</td>\n",
       "      <td>0.827099</td>\n",
       "      <td>-0.073288</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  start_station_id  end_station_id  start_lat  start_lng  \\\n",
       "start_station_id          1.000000        0.485921   0.035452   0.171886   \n",
       "end_station_id            0.485921        1.000000   0.044197   0.079893   \n",
       "start_lat                 0.035452        0.044197   1.000000  -0.077845   \n",
       "start_lng                 0.171886        0.079893  -0.077845   1.000000   \n",
       "end_lat                   0.056575        0.032274   0.818460  -0.059892   \n",
       "end_lng                   0.079817        0.177052  -0.064425   0.827099   \n",
       "\n",
       "                   end_lat   end_lng  \n",
       "start_station_id  0.056575  0.079817  \n",
       "end_station_id    0.032274  0.177052  \n",
       "start_lat         0.818460 -0.064425  \n",
       "start_lng        -0.059892  0.827099  \n",
       "end_lat           1.000000 -0.073288  \n",
       "end_lng          -0.073288  1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.drop(columns=\"duration\").corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_end_station_combination</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>933DB56B508F5DCD</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-12 18:19:28</td>\n",
       "      <td>2022-05-12 18:26:41</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Park Rd &amp; Holmead Pl NW</td>\n",
       "      <td>31602.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.930800</td>\n",
       "      <td>-77.031500</td>\n",
       "      <td>member</td>\n",
       "      <td>7.216667</td>\n",
       "      <td>15th &amp; Euclid St NW - Park Rd &amp; Holmead Pl NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48B50E20E9B24CA7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-27 16:24:42</td>\n",
       "      <td>2022-05-27 16:51:51</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>3rd &amp; Tingey St SE</td>\n",
       "      <td>31634.0</td>\n",
       "      <td>38.908142</td>\n",
       "      <td>-77.038359</td>\n",
       "      <td>38.875010</td>\n",
       "      <td>-77.002400</td>\n",
       "      <td>member</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW - 3rd &amp; Tingey ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96444E8B2485BA6D</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-29 19:16:55</td>\n",
       "      <td>2022-05-29 19:22:31</td>\n",
       "      <td>15th &amp; Euclid St NW</td>\n",
       "      <td>31117.0</td>\n",
       "      <td>Columbia Rd &amp; Belmont St NW</td>\n",
       "      <td>31113.0</td>\n",
       "      <td>38.923330</td>\n",
       "      <td>-77.035200</td>\n",
       "      <td>38.920669</td>\n",
       "      <td>-77.043680</td>\n",
       "      <td>member</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>15th &amp; Euclid St NW - Columbia Rd &amp; Belmont St NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37FE6E9EB6768E2A</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2022-05-11 12:30:58</td>\n",
       "      <td>2022-05-11 12:35:36</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>15th &amp; W St NW</td>\n",
       "      <td>31125.0</td>\n",
       "      <td>38.908131</td>\n",
       "      <td>-77.038435</td>\n",
       "      <td>38.919018</td>\n",
       "      <td>-77.034449</td>\n",
       "      <td>member</td>\n",
       "      <td>4.633333</td>\n",
       "      <td>17th St &amp; Massachusetts Ave NW - 15th &amp; W St NW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8FD653C76B951755</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2022-05-03 19:16:22</td>\n",
       "      <td>2022-05-03 19:50:30</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave</td>\n",
       "      <td>32066.0</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>39.032029</td>\n",
       "      <td>-77.047350</td>\n",
       "      <td>casual</td>\n",
       "      <td>34.133333</td>\n",
       "      <td>Windham Ln &amp; Amherst Ave - Windham Ln &amp; Amhers...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type          started_at            ended_at  \\\n",
       "0  933DB56B508F5DCD   classic_bike 2022-05-12 18:19:28 2022-05-12 18:26:41   \n",
       "1  48B50E20E9B24CA7   classic_bike 2022-05-27 16:24:42 2022-05-27 16:51:51   \n",
       "2  96444E8B2485BA6D   classic_bike 2022-05-29 19:16:55 2022-05-29 19:22:31   \n",
       "3  37FE6E9EB6768E2A  electric_bike 2022-05-11 12:30:58 2022-05-11 12:35:36   \n",
       "4  8FD653C76B951755   classic_bike 2022-05-03 19:16:22 2022-05-03 19:50:30   \n",
       "\n",
       "               start_station_name  start_station_id  \\\n",
       "0             15th & Euclid St NW           31117.0   \n",
       "1  17th St & Massachusetts Ave NW           31267.0   \n",
       "2             15th & Euclid St NW           31117.0   \n",
       "3  17th St & Massachusetts Ave NW           31267.0   \n",
       "4        Windham Ln & Amherst Ave           32066.0   \n",
       "\n",
       "              end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0      Park Rd & Holmead Pl NW         31602.0  38.923330 -77.035200   \n",
       "1           3rd & Tingey St SE         31634.0  38.908142 -77.038359   \n",
       "2  Columbia Rd & Belmont St NW         31113.0  38.923330 -77.035200   \n",
       "3               15th & W St NW         31125.0  38.908131 -77.038435   \n",
       "4     Windham Ln & Amherst Ave         32066.0  39.032029 -77.047350   \n",
       "\n",
       "     end_lat    end_lng member_casual   duration  \\\n",
       "0  38.930800 -77.031500        member   7.216667   \n",
       "1  38.875010 -77.002400        member  27.150000   \n",
       "2  38.920669 -77.043680        member   5.600000   \n",
       "3  38.919018 -77.034449        member   4.633333   \n",
       "4  39.032029 -77.047350        casual  34.133333   \n",
       "\n",
       "                       start_end_station_combination  \n",
       "0      15th & Euclid St NW - Park Rd & Holmead Pl NW  \n",
       "1  17th St & Massachusetts Ave NW - 3rd & Tingey ...  \n",
       "2  15th & Euclid St NW - Columbia Rd & Belmont St NW  \n",
       "3    17th St & Massachusetts Ave NW - 15th & W St NW  \n",
       "4  Windham Ln & Amherst Ave - Windham Ln & Amhers...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(filepath):\n",
    "    \"\"\"\n",
    "    Returns cleaned dataframe\n",
    "    \"\"\"\n",
    "    # Read csv zip file into dataframe\n",
    "    with zipfile.ZipFile(filepath, \"r\") as f:\n",
    "        for name in f.namelist():\n",
    "            if name.endswith('.csv'):\n",
    "                with f.open(name) as zd:\n",
    "                    df = pd.read_csv(zd)\n",
    "                break\n",
    "    \n",
    "    \n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_parquet(df, filepath):\n",
    "    \"\"\"\n",
    "    Saves dataframe to parquet format\n",
    "    \"\"\"\n",
    "    # Generate destination parquet path\n",
    "    dest_path = filepath.split(\".\")\n",
    "    dest_path = f\".{dest_path[1]}.parquet\"\n",
    "    \n",
    "    # Save file destination path\n",
    "    df.to_parquet(dest_path, engine=\"pyarrow\", index=False)\n",
    "    \n",
    "    return dest_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(row):\n",
    "    \"\"\"\n",
    "    Returns distance between two geo-location coordinates\n",
    "    \"\"\"\n",
    "    start = (row[\"start_lat\"], row[\"start_lng\"])\n",
    "    end = (row[\"end_lat\"], row[\"end_lng\"])\n",
    "    distance = haversine(start, end, unit=Unit.MILES)\n",
    "    \n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(dest_path):\n",
    "    \"\"\"\n",
    "    Wrangle and return cleaned dataframe\n",
    "    \"\"\"\n",
    "    # Read parquet to dataframe\n",
    "    df = pd.read_parquet(dest_path)\n",
    "    \n",
    "    # Change datetime columns to datatime datatype\n",
    "    df.started_at = pd.to_datetime(df.started_at)\n",
    "    df.ended_at = pd.to_datetime(df.ended_at)\n",
    "    \n",
    "    df[\"duration\"] = df.ended_at - df.started_at\n",
    "    df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)\n",
    "    \n",
    "    mask_duration = (df.duration >= 1) & (df.duration <= 60)\n",
    "    df = df[mask_duration]\n",
    "    \n",
    "    # Drop columns\n",
    "    drop_columns = ['ride_id', 'started_at', 'ended_at',\n",
    "       'start_station_name', 'end_station_name', 'member_casual']\n",
    "    df.drop(columns=drop_columns, inplace=True)\n",
    "    \n",
    "    # Calculate trip distance from start and end coordinates\n",
    "    df[\"trip_distance\"] = df.apply(distance, axis=1)\n",
    "    df.drop(columns=['start_lat', 'start_lng', 'end_lat', 'end_lng'], inplace=True)\n",
    "    \n",
    "    # start_end_id\n",
    "    df[[\"start_station_id\", \"end_station_id\"]] = df[[\"start_station_id\", \"end_station_id\"]].astype(str)\n",
    "    df[\"start_end_id\"] = df[\"start_station_id\"] + \"_\" + df[\"end_station_id\"]\n",
    "    df.drop(columns=[\"start_station_id\", \"end_station_id\"], inplace=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"./data/202205-capitalbikeshare-tripdata.zip\"\n",
    "df = read_data(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dest_parquet_path = save_to_parquet(df, filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>rideable_type</th>\n",
       "      <th>duration</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>start_end_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>7.216667</td>\n",
       "      <td>0.553119</td>\n",
       "      <td>31117.0_31602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>27.150000</td>\n",
       "      <td>2.996669</td>\n",
       "      <td>31267.0_31634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.491523</td>\n",
       "      <td>31117.0_31113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>electric_bike</td>\n",
       "      <td>4.633333</td>\n",
       "      <td>0.782209</td>\n",
       "      <td>31267.0_31125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>classic_bike</td>\n",
       "      <td>34.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32066.0_32066.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rideable_type   duration  trip_distance     start_end_id\n",
       "0   classic_bike   7.216667       0.553119  31117.0_31602.0\n",
       "1   classic_bike  27.150000       2.996669  31267.0_31634.0\n",
       "2   classic_bike   5.600000       0.491523  31117.0_31113.0\n",
       "3  electric_bike   4.633333       0.782209  31267.0_31125.0\n",
       "4   classic_bike  34.133333       0.000000  32066.0_32066.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = prepare_data(dest_parquet_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>duration</th>\n",
       "      <th>trip_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>310389.000000</td>\n",
       "      <td>310356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.481160</td>\n",
       "      <td>1.206502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.611080</td>\n",
       "      <td>0.889049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.116667</td>\n",
       "      <td>0.597681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.033333</td>\n",
       "      <td>1.010457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.300000</td>\n",
       "      <td>1.601602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>11.667898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            duration  trip_distance\n",
       "count  310389.000000  310356.000000\n",
       "mean       15.481160       1.206502\n",
       "std        11.611080       0.889049\n",
       "min         1.000000       0.000000\n",
       "25%         7.116667       0.597681\n",
       "50%        12.033333       1.010457\n",
       "75%        20.300000       1.601602\n",
       "max        60.000000      11.667898"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310389, 4)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 310389 entries, 0 to 332820\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   rideable_type  310389 non-null  object \n",
      " 1   duration       310389 non-null  float64\n",
      " 2   trip_distance  310356 non-null  float64\n",
      " 3   start_end_id   310389 non-null  object \n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rideable_type', 'duration', 'trip_distance', 'start_end_id'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rideable_type     0\n",
       "duration          0\n",
       "trip_distance    33\n",
       "start_end_id      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"duration\"\n",
    "features = list(df.drop(columns=target).columns)\n",
    "X = df[features]\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310389, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(310389,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(248311, 3)\n",
      "(248311,)\n",
      "(62078, 3)\n",
      "(62078,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model\n",
    "\n",
    "### Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean apt price: 15.48\n",
      "Baseline MAE: 8.87\n",
      "Baseline RMSE: 11.61\n"
     ]
    }
   ],
   "source": [
    "y_mean = y_train.mean()\n",
    "y_pred_baseline = [y_mean] * len(y_train)\n",
    "\n",
    "print(\"Mean apt price:\", round(y_mean, 2))\n",
    "\n",
    "print(\"Baseline MAE:\", mean_absolute_error(y_train, y_pred_baseline).round(2))\n",
    "print(\"Baseline RMSE:\", (mean_squared_error(y_train, y_pred_baseline) ** .5).round(2) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterate\n",
    "\n",
    "###  Ridge Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'rideable_type': 'classic_bike',\n",
       "  'duration': 7.216666666666667,\n",
       "  'trip_distance': 0.5531190266114605,\n",
       "  'start_end_id': '31117.0_31602.0'},\n",
       " {'rideable_type': 'classic_bike',\n",
       "  'duration': 27.15,\n",
       "  'trip_distance': 2.996669093244379,\n",
       "  'start_end_id': '31267.0_31634.0'}]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_dict(orient=\"records\")[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()), (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()), (&#x27;ridge&#x27;, Ridge())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                ('simpleimputer', SimpleImputer()), ('ridge', Ridge())])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build and Train model\n",
    "model = make_pipeline(\n",
    "    DictVectorizer(),\n",
    "    SimpleImputer(),\n",
    "    Ridge()\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()), (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()), (&#x27;ridge&#x27;, Ridge())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                ('simpleimputer', SimpleImputer()), ('ridge', Ridge())])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train.to_dict(orient=\"records\"), y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MAE: 4.41\n",
      "Training RMSE: 7.11\n"
     ]
    }
   ],
   "source": [
    "y_pred_training = model.predict(X_train.to_dict(orient=\"records\"))\n",
    "mae_training = mean_absolute_error(y_train, y_pred_training)\n",
    "rmse_training = (mean_squared_error(y_train, y_pred_training)) ** 0.5\n",
    "\n",
    "print(\"Training MAE:\", round(mae_training, 2))\n",
    "print(\"Training RMSE:\", round(rmse_training, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 5.17\n",
      "Test RMSE: 5.17\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test.to_dict(orient=\"records\"))\n",
    "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
    "rmse_test = (mean_squared_error(y_test, y_pred_test)) ** 0.5\n",
    "\n",
    "print(\"Test MAE:\", round(mae_test, 2))\n",
    "print(\"Test RMSE:\", round(mae_test, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.65804349,  5.7125158 , 22.83842452, 10.36956981, 31.77688307])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestRegressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                ('simpleimputer', SimpleImputer()),\n",
       "                ('randomforestregressor',\n",
       "                 RandomForestRegressor(random_state=42))])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgr = make_pipeline(\n",
    "    DictVectorizer(),\n",
    "    SimpleImputer(),\n",
    "    RandomForestRegressor(random_state=42)\n",
    ")\n",
    "rgr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'simpleimputer__strategy': ['mean', 'median'],\n",
       " 'randomforestregressor__n_estimators': range(25, 100, 25),\n",
       " 'randomforestregressor__max_depth': range(10, 50, 10)}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"simpleimputer__strategy\": [\"mean\", \"median\"],\n",
    "    \"randomforestregressor__n_estimators\": range(25, 100, 25),\n",
    "    \"randomforestregressor__max_depth\": range(10, 50, 10)\n",
    "    \n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;randomforestregressor&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;randomforestregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;randomforestregressor&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;randomforestregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                                       ('simpleimputer', SimpleImputer()),\n",
       "                                       ('randomforestregressor',\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestregressor__max_depth': range(10, 50, 10),\n",
       "                         'randomforestregressor__n_estimators': range(25, 100, 25),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    rgr,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;randomforestregressor&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;randomforestregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;randomforestregressor&#x27;,\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;randomforestregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;randomforestregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;randomforestregressor&#x27;,\n",
       "                 RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                                       ('simpleimputer', SimpleImputer()),\n",
       "                                       ('randomforestregressor',\n",
       "                                        RandomForestRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestregressor__max_depth': range(10, 50, 10),\n",
       "                         'randomforestregressor__n_estimators': range(25, 100, 25),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model\n",
    "model.fit(X_train.to_dict(orient=\"records\"), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'randomforestregressor__max_depth': 40,\n",
       " 'randomforestregressor__n_estimators': 75,\n",
       " 'simpleimputer__strategy': 'median'}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.453780772309862"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MAE: 5.3\n",
      "Training RMSE: 8.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_training = model.predict(X_train.to_dict(orient=\"records\"))\n",
    "mae_training = mean_absolute_error(y_train, y_pred_training)\n",
    "rmse_training = (mean_squared_error(y_train, y_pred_training)) ** 0.5\n",
    "\n",
    "print(\"Training MAE:\", round(mae_training, 2))\n",
    "print(\"Training RMSE:\", round(rmse_training, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 5.57\n",
      "Test RMSE: 5.57\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = model.predict(X_test.to_dict(orient=\"records\"))\n",
    "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
    "rmse_test = (mean_squared_error(y_test, y_pred_test)) ** 0.5\n",
    "\n",
    "print(\"Test MAE:\", round(mae_test, 2))\n",
    "print(\"Test RMSE:\", round(mae_test, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;gradientboostingregressor&#x27;,\n",
       "                 GradientBoostingRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;gradientboostingregressor&#x27;,\n",
       "                 GradientBoostingRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                ('simpleimputer', SimpleImputer()),\n",
       "                ('gradientboostingregressor',\n",
       "                 GradientBoostingRegressor(random_state=42))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr = make_pipeline(\n",
    "    DictVectorizer(),\n",
    "    SimpleImputer(),\n",
    "    GradientBoostingRegressor(random_state=42)\n",
    ")\n",
    "gbr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'simpleimputer__strategy': ['mean', 'median'],\n",
       " 'gradientboostingregressor__n_estimators': range(25, 100, 25),\n",
       " 'gradientboostingregressor__max_depth': range(10, 50, 10)}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"simpleimputer__strategy\": [\"mean\", \"median\"],\n",
    "    \"gradientboostingregressor__n_estimators\": range(25, 100, 25),\n",
    "    \"gradientboostingregressor__max_depth\": range(10, 50, 10),\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;gradientboostingregressor&#x27;,\n",
       "                                        GradientBoostingRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;gradientboostingregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                                       (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                                       (&#x27;gradientboostingregressor&#x27;,\n",
       "                                        GradientBoostingRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;gradientboostingregressor__max_depth&#x27;: range(10, 50, 10),\n",
       "                         &#x27;gradientboostingregressor__n_estimators&#x27;: range(25, 100, 25),\n",
       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;]},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;dictvectorizer&#x27;, DictVectorizer()),\n",
       "                (&#x27;simpleimputer&#x27;, SimpleImputer()),\n",
       "                (&#x27;gradientboostingregressor&#x27;,\n",
       "                 GradientBoostingRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DictVectorizer</label><div class=\"sk-toggleable__content\"><pre>DictVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('dictvectorizer', DictVectorizer()),\n",
       "                                       ('simpleimputer', SimpleImputer()),\n",
       "                                       ('gradientboostingregressor',\n",
       "                                        GradientBoostingRegressor(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingregressor__max_depth': range(10, 50, 10),\n",
       "                         'gradientboostingregressor__n_estimators': range(25, 100, 25),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    gbr,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model\n",
    "# model.fit(X_train.to_dict(orient=\"records\"), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_training = model.predict(X_train.to_dict(orient=\"records\"))\n",
    "mae_training = mean_absolute_error(y_train, y_pred_training)\n",
    "rmse_training = (mean_squared_error(y_train, y_pred_training)) ** 0.5\n",
    "\n",
    "print(\"Training MAE:\", round(mae_training, 2))\n",
    "print(\"Training RMSE:\", round(rmse_training, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = model.predict(X_test.to_dict(orient=\"records\"))\n",
    "mae_test = mean_absolute_error(y_test, y_pred_test)\n",
    "rmse_test = (mean_squared_error(y_test, y_pred_test)) ** 0.5\n",
    "\n",
    "print(\"Test MAE:\", round(mae_test, 2))\n",
    "print(\"Test RMSE:\", round(mae_test, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [\n",
    "        (\"202206-capitalbikeshare-tripdata.zip\", \"./data\"),\n",
    "        (\"202205-capitalbikeshare-tripdata.zip\", \"./data\"),\n",
    "    ]\n",
    "train_filepath = (\"/\").join((files[0][1], files[0][0])\n",
    "train_filepath = (\"/\").join((files[0][1], files[0][0])                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "from mlflow.tracking import MlflowClient\n",
    "from mlflow.entities import ViewType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MLFLOW_TRACKING_URI = \"http://127.0.0.1:5000\"\n",
    "EXPERIMENT_NAME = \"citibikes-experiment-2022-07-31\"\n",
    "\n",
    "client = MlflowClient(tracking_uri=MLFLOW_TRACKING_URI)\n",
    "candidates = client.search_runs(\n",
    "    experiment_ids=client.get_experiment_by_name(EXPERIMENT_NAME).experiment_id,\n",
    "    # filter_string='metrics.rmse_valid < 6.5 and metrics.inference_time < 20e-6',\n",
    "    run_view_type=ViewType.ACTIVE_ONLY,\n",
    "    max_results=5,\n",
    "    order_by=[\"metrics.rmse_valid ASC\"],\n",
    ")\n",
    "\n",
    "# candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Run: data=<RunData: metrics={'inference_time': 5.8704143593324586e-06,\n",
       " 'mae_train': 4.428574629029708,\n",
       " 'mae_valid': 5.450461825419659,\n",
       " 'rmse_train': 7.156376019478943,\n",
       " 'rmse_valid': 8.616102120671593,\n",
       " 'training_mae': 4.428574629029708,\n",
       " 'training_mse': 51.21371773217327,\n",
       " 'training_r2_score': 0.6284647219622371,\n",
       " 'training_rmse': 7.156376019478943,\n",
       " 'training_score': 0.6284647219622371}, params={'dictvectorizer': 'DictVectorizer()',\n",
       " 'dictvectorizer__dtype': \"<class 'numpy.float64'>\",\n",
       " 'dictvectorizer__separator': '=',\n",
       " 'dictvectorizer__sort': 'True',\n",
       " 'dictvectorizer__sparse': 'True',\n",
       " 'memory': 'None',\n",
       " 'ridge': 'Ridge(random_state=42)',\n",
       " 'ridge__alpha': '1.0',\n",
       " 'ridge__copy_X': 'True',\n",
       " 'ridge__fit_intercept': 'True',\n",
       " 'ridge__max_iter': 'None',\n",
       " 'ridge__normalize': 'deprecated',\n",
       " 'ridge__positive': 'False',\n",
       " 'ridge__random_state': '42',\n",
       " 'ridge__solver': 'auto',\n",
       " 'ridge__tol': '0.001',\n",
       " 'simpleimputer': 'SimpleImputer()',\n",
       " 'simpleimputer__add_indicator': 'False',\n",
       " 'simpleimputer__copy': 'True',\n",
       " 'simpleimputer__fill_value': 'None',\n",
       " 'simpleimputer__missing_values': 'nan',\n",
       " 'simpleimputer__strategy': 'mean',\n",
       " 'simpleimputer__verbose': 'deprecated',\n",
       " 'steps': \"[('dictvectorizer', DictVectorizer()), ('simpleimputer', \"\n",
       "          \"SimpleImputer()), ('ridge', Ridge(random_state=42))]\",\n",
       " 'verbose': 'False'}, tags={'Model': \"<class 'sklearn.linear_model._ridge.Ridge'>\",\n",
       " 'author/developer': 'PatrickCmd',\n",
       " 'estimator_class': 'sklearn.pipeline.Pipeline',\n",
       " 'estimator_name': 'Pipeline',\n",
       " 'mlflow.log-model.history': '[{\"run_id\": \"e51b2dbb72514425bd2710ecff006456\", '\n",
       "                             '\"artifact_path\": \"model\", \"utc_time_created\": '\n",
       "                             '\"2022-07-31 20:29:33.708842\", \"flavors\": '\n",
       "                             '{\"python_function\": {\"model_path\": \"model.pkl\", '\n",
       "                             '\"loader_module\": \"mlflow.sklearn\", '\n",
       "                             '\"python_version\": \"3.9.12\", \"env\": '\n",
       "                             '\"conda.yaml\"}, \"sklearn\": {\"pickled_model\": '\n",
       "                             '\"model.pkl\", \"sklearn_version\": \"1.1.1\", '\n",
       "                             '\"serialization_format\": \"cloudpickle\", \"code\": '\n",
       "                             'null}}, \"model_uuid\": '\n",
       "                             '\"7031004c587745a789cfdd7c4771ca99\", '\n",
       "                             '\"mlflow_version\": \"1.27.0\"}]',\n",
       " 'mlflow.source.name': 'main.py',\n",
       " 'mlflow.source.type': 'LOCAL',\n",
       " 'mlflow.user': 'patrickwalukagga'}>, info=<RunInfo: artifact_uri='s3://mlflow-models-artifact-store-cmd/1/e51b2dbb72514425bd2710ecff006456/artifacts', end_time=1659299442666, experiment_id='1', lifecycle_stage='active', run_id='e51b2dbb72514425bd2710ecff006456', run_uuid='e51b2dbb72514425bd2710ecff006456', start_time=1659299365154, status='FINISHED', user_id='patrickwalukagga'>>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Register and stage best model\n",
    "best_model = candidates[0]\n",
    "best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<RunInfo: artifact_uri='s3://mlflow-models-artifact-store-cmd/1/e51b2dbb72514425bd2710ecff006456/artifacts', end_time=1659299442666, experiment_id='1', lifecycle_stage='active', run_id='e51b2dbb72514425bd2710ecff006456', run_uuid='e51b2dbb72514425bd2710ecff006456', start_time=1659299365154, status='FINISHED', user_id='patrickwalukagga'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e51b2dbb72514425bd2710ecff006456'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.info.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.info.experiment_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': 'None',\n",
       " 'steps': \"[('dictvectorizer', DictVectorizer()), ('simpleimputer', SimpleImputer()), ('ridge', Ridge(random_state=42))]\",\n",
       " 'verbose': 'False',\n",
       " 'dictvectorizer': 'DictVectorizer()',\n",
       " 'simpleimputer': 'SimpleImputer()',\n",
       " 'ridge': 'Ridge(random_state=42)',\n",
       " 'dictvectorizer__dtype': \"<class 'numpy.float64'>\",\n",
       " 'dictvectorizer__separator': '=',\n",
       " 'dictvectorizer__sort': 'True',\n",
       " 'dictvectorizer__sparse': 'True',\n",
       " 'simpleimputer__add_indicator': 'False',\n",
       " 'simpleimputer__copy': 'True',\n",
       " 'simpleimputer__fill_value': 'None',\n",
       " 'simpleimputer__missing_values': 'nan',\n",
       " 'simpleimputer__strategy': 'mean',\n",
       " 'simpleimputer__verbose': 'deprecated',\n",
       " 'ridge__alpha': '1.0',\n",
       " 'ridge__copy_X': 'True',\n",
       " 'ridge__fit_intercept': 'True',\n",
       " 'ridge__max_iter': 'None',\n",
       " 'ridge__normalize': 'deprecated',\n",
       " 'ridge__positive': 'False',\n",
       " 'ridge__random_state': '42',\n",
       " 'ridge__solver': 'auto',\n",
       " 'ridge__tol': '0.001'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.data.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlops-project-uq6XT4KV",
   "language": "python",
   "name": "mlops-project-uq6xt4kv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "nbconvert_exporter": "python",
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