Regression Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14740</th>\n",
       "      <td>4.1518</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.663073</td>\n",
       "      <td>1.075472</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>4.180593</td>\n",
       "      <td>32.58</td>\n",
       "      <td>-117.05</td>\n",
       "      <td>1.369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101</th>\n",
       "      <td>5.7796</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.107226</td>\n",
       "      <td>0.927739</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>3.020979</td>\n",
       "      <td>33.92</td>\n",
       "      <td>-117.97</td>\n",
       "      <td>2.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20566</th>\n",
       "      <td>4.3487</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.930712</td>\n",
       "      <td>1.026217</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>2.910112</td>\n",
       "      <td>38.65</td>\n",
       "      <td>-121.84</td>\n",
       "      <td>2.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2670</th>\n",
       "      <td>2.4511</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.992958</td>\n",
       "      <td>1.316901</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2.746479</td>\n",
       "      <td>33.20</td>\n",
       "      <td>-115.60</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15709</th>\n",
       "      <td>5.0049</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.319261</td>\n",
       "      <td>1.039578</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1.712401</td>\n",
       "      <td>37.79</td>\n",
       "      <td>-122.43</td>\n",
       "      <td>4.600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "14740  4.1518      22.0  5.663073   1.075472      1551.0  4.180593     32.58   \n",
       "10101  5.7796      32.0  6.107226   0.927739      1296.0  3.020979     33.92   \n",
       "20566  4.3487      29.0  5.930712   1.026217      1554.0  2.910112     38.65   \n",
       "2670   2.4511      37.0  4.992958   1.316901       390.0  2.746479     33.20   \n",
       "15709  5.0049      25.0  4.319261   1.039578       649.0  1.712401     37.79   \n",
       "\n",
       "       Longitude  MedHouseVal  \n",
       "14740    -117.05        1.369  \n",
       "10101    -117.97        2.413  \n",
       "20566    -121.84        2.007  \n",
       "2670     -115.60        0.725  \n",
       "15709    -122.43        4.600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = fetch_california_housing(as_frame=True).frame\n",
    "df = shuffle(df, random_state=0)\n",
    "df = df.head(1000)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['MedHouseVal'], axis=1)\n",
    "y = df['MedHouseVal']\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x, y, test_size=0.2, random_state=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6255168893408662"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6145211085585905"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model, x, y, cv=5).mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Regression to Predict Taxi Fares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>key</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>passenger_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-15 17:11:00.000000107</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-06-15 17:11:00+00:00</td>\n",
       "      <td>-73.995420</td>\n",
       "      <td>40.759662</td>\n",
       "      <td>-73.987607</td>\n",
       "      <td>40.751247</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-03-14 22:43:00.00000095</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2011-03-14 22:43:00+00:00</td>\n",
       "      <td>-73.993552</td>\n",
       "      <td>40.731110</td>\n",
       "      <td>-73.998497</td>\n",
       "      <td>40.737200</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-02-14 15:14:00.00000067</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2011-02-14 15:14:00+00:00</td>\n",
       "      <td>-73.972380</td>\n",
       "      <td>40.749527</td>\n",
       "      <td>-73.990638</td>\n",
       "      <td>40.745328</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-10-29 11:29:00.00000040</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2009-10-29 11:29:00+00:00</td>\n",
       "      <td>-73.973703</td>\n",
       "      <td>40.763542</td>\n",
       "      <td>-73.984253</td>\n",
       "      <td>40.758603</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-07-02 10:38:00.00000028</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2011-07-02 10:38:00+00:00</td>\n",
       "      <td>-73.921262</td>\n",
       "      <td>40.743615</td>\n",
       "      <td>-73.967383</td>\n",
       "      <td>40.765162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  fare_amount           pickup_datetime  \\\n",
       "0  2014-06-15 17:11:00.000000107          7.0 2014-06-15 17:11:00+00:00   \n",
       "1   2011-03-14 22:43:00.00000095          4.9 2011-03-14 22:43:00+00:00   \n",
       "2   2011-02-14 15:14:00.00000067          6.1 2011-02-14 15:14:00+00:00   \n",
       "3   2009-10-29 11:29:00.00000040          6.9 2009-10-29 11:29:00+00:00   \n",
       "4   2011-07-02 10:38:00.00000028         10.5 2011-07-02 10:38:00+00:00   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.995420        40.759662         -73.987607         40.751247   \n",
       "1        -73.993552        40.731110         -73.998497         40.737200   \n",
       "2        -73.972380        40.749527         -73.990638         40.745328   \n",
       "3        -73.973703        40.763542         -73.984253         40.758603   \n",
       "4        -73.921262        40.743615         -73.967383         40.765162   \n",
       "\n",
       "   passenger_count  \n",
       "0                1  \n",
       "1                5  \n",
       "2                1  \n",
       "3                5  \n",
       "4                1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/taxi-fares.csv', parse_dates=['pickup_datetime'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='passenger_count', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['passenger_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['passenger_count'] == 1]"
   ]
  },
  {
   "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>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-06-15 17:11:00+00:00</td>\n",
       "      <td>-73.995420</td>\n",
       "      <td>40.759662</td>\n",
       "      <td>-73.987607</td>\n",
       "      <td>40.751247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2011-02-14 15:14:00+00:00</td>\n",
       "      <td>-73.972380</td>\n",
       "      <td>40.749527</td>\n",
       "      <td>-73.990638</td>\n",
       "      <td>40.745328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>2011-07-02 10:38:00+00:00</td>\n",
       "      <td>-73.921262</td>\n",
       "      <td>40.743615</td>\n",
       "      <td>-73.967383</td>\n",
       "      <td>40.765162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.3</td>\n",
       "      <td>2011-12-09 20:03:00+00:00</td>\n",
       "      <td>-73.973500</td>\n",
       "      <td>40.792610</td>\n",
       "      <td>-73.936035</td>\n",
       "      <td>40.856548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2011-04-02 01:05:15+00:00</td>\n",
       "      <td>-73.979564</td>\n",
       "      <td>40.735405</td>\n",
       "      <td>-73.955686</td>\n",
       "      <td>40.768065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0          7.0 2014-06-15 17:11:00+00:00        -73.995420        40.759662   \n",
       "2          6.1 2011-02-14 15:14:00+00:00        -73.972380        40.749527   \n",
       "4         10.5 2011-07-02 10:38:00+00:00        -73.921262        40.743615   \n",
       "5         15.3 2011-12-09 20:03:00+00:00        -73.973500        40.792610   \n",
       "8          7.7 2011-04-02 01:05:15+00:00        -73.979564        40.735405   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  \n",
       "0         -73.987607         40.751247  \n",
       "2         -73.990638         40.745328  \n",
       "4         -73.967383         40.765162  \n",
       "5         -73.936035         40.856548  \n",
       "8         -73.955686         40.768065  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['key', 'passenger_count'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38233, 6)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fare_amount          1.000000\n",
       "dropoff_longitude    0.020438\n",
       "pickup_longitude     0.015742\n",
       "pickup_latitude     -0.015915\n",
       "dropoff_latitude    -0.021711\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix['fare_amount'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in df.iterrows():\n",
    "    dt = row['pickup_datetime']\n",
    "    df.at[i, 'day_of_week'] = dt.weekday()\n",
    "    df.at[i, 'pickup_time'] = dt.hour\n",
    "    x = (row['dropoff_longitude'] - row['pickup_longitude']) * 54.6\n",
    "    y = (row['dropoff_latitude'] - row['pickup_latitude']) * 69.0\n",
    "    distance = sqrt(x ** 2 + y ** 2)\n",
    "    df.at[i, 'distance'] = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>fare_amount</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-06-15 17:11:00+00:00</td>\n",
       "      <td>-73.995420</td>\n",
       "      <td>40.759662</td>\n",
       "      <td>-73.987607</td>\n",
       "      <td>40.751247</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.720497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2011-02-14 15:14:00+00:00</td>\n",
       "      <td>-73.972380</td>\n",
       "      <td>40.749527</td>\n",
       "      <td>-73.990638</td>\n",
       "      <td>40.745328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.038136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>2011-07-02 10:38:00+00:00</td>\n",
       "      <td>-73.921262</td>\n",
       "      <td>40.743615</td>\n",
       "      <td>-73.967383</td>\n",
       "      <td>40.765162</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.924341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.3</td>\n",
       "      <td>2011-12-09 20:03:00+00:00</td>\n",
       "      <td>-73.973500</td>\n",
       "      <td>40.792610</td>\n",
       "      <td>-73.936035</td>\n",
       "      <td>40.856548</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.862893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2011-04-02 01:05:15+00:00</td>\n",
       "      <td>-73.979564</td>\n",
       "      <td>40.735405</td>\n",
       "      <td>-73.955686</td>\n",
       "      <td>40.768065</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.603493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0          7.0 2014-06-15 17:11:00+00:00        -73.995420        40.759662   \n",
       "2          6.1 2011-02-14 15:14:00+00:00        -73.972380        40.749527   \n",
       "4         10.5 2011-07-02 10:38:00+00:00        -73.921262        40.743615   \n",
       "5         15.3 2011-12-09 20:03:00+00:00        -73.973500        40.792610   \n",
       "8          7.7 2011-04-02 01:05:15+00:00        -73.979564        40.735405   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  day_of_week  pickup_time  distance  \n",
       "0         -73.987607         40.751247          6.0         17.0  0.720497  \n",
       "2         -73.990638         40.745328          0.0         15.0  1.038136  \n",
       "4         -73.967383         40.765162          5.0         10.0  2.924341  \n",
       "5         -73.936035         40.856548          4.0         20.0  4.862893  \n",
       "8         -73.955686         40.768065          5.0          1.0  2.603493  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\n",
    "    columns=[\n",
    "        'pickup_datetime', 'pickup_longitude', 'pickup_latitude',\n",
    "        'dropoff_longitude', 'dropoff_latitude'\n",
    "    ], inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>fare_amount</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.720497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.038136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.924341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.862893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.603493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount  day_of_week  pickup_time  distance\n",
       "0          7.0          6.0         17.0  0.720497\n",
       "2          6.1          0.0         15.0  1.038136\n",
       "4         10.5          5.0         10.0  2.924341\n",
       "5         15.3          4.0         20.0  4.862893\n",
       "8          7.7          5.0          1.0  2.603493"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fare_amount    1.000000\n",
       "distance       0.045873\n",
       "day_of_week    0.009196\n",
       "pickup_time   -0.019722\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix['fare_amount'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>fare_amount</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>pickup_time</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38233.000000</td>\n",
       "      <td>38233.000000</td>\n",
       "      <td>38233.000000</td>\n",
       "      <td>38233.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.214115</td>\n",
       "      <td>2.951534</td>\n",
       "      <td>13.387989</td>\n",
       "      <td>12.018397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.703149</td>\n",
       "      <td>1.932809</td>\n",
       "      <td>6.446519</td>\n",
       "      <td>217.357022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-22.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.762116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.331326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.402226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>4923.837280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fare_amount   day_of_week   pickup_time      distance\n",
       "count  38233.000000  38233.000000  38233.000000  38233.000000\n",
       "mean      11.214115      2.951534     13.387989     12.018397\n",
       "std        9.703149      1.932809      6.446519    217.357022\n",
       "min      -22.100000      0.000000      0.000000      0.000000\n",
       "25%        6.000000      1.000000      9.000000      0.762116\n",
       "50%        8.500000      3.000000     14.000000      1.331326\n",
       "75%       12.500000      5.000000     19.000000      2.402226\n",
       "max      256.000000      6.000000     23.000000   4923.837280"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['distance'] > 1.0) & (df['distance'] < 10.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['fare_amount'] > 0.0) & (df['fare_amount'] < 50.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fare_amount    1.000000\n",
       "distance       0.851913\n",
       "day_of_week   -0.003570\n",
       "pickup_time   -0.023085\n",
       "Name: fare_amount, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "corr_matrix['fare_amount'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['fare_amount'], axis=1)\n",
    "y = df['fare_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.725884506191032"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "cross_val_score(model, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.706157807448991"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor(random_state=0)\n",
    "cross_val_score(model, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.750496262408626"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "model = GradientBoostingRegressor(random_state=0)\n",
    "cross_val_score(model, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(random_state=0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.49105571])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\n",
    "    pd.DataFrame({\n",
    "        'day_of_week': [4],\n",
    "        'pickup_time': [17],\n",
    "        'distance': [2.0]\n",
    "    })\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.95309995])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\n",
    "    pd.DataFrame({\n",
    "        'day_of_week': [5],\n",
    "        'pickup_time': [17],\n",
    "        'distance': [2.0]\n",
    "    })\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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