B1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e22b8cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "051c5856",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('uber.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f632cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>24238194</td>\n",
       "      <td>2015-05-07 19:52:06.0000003</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015-05-07 19:52:06 UTC</td>\n",
       "      <td>-73.999817</td>\n",
       "      <td>40.738354</td>\n",
       "      <td>-73.999512</td>\n",
       "      <td>40.723217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27835199</td>\n",
       "      <td>2009-07-17 20:04:56.0000002</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2009-07-17 20:04:56 UTC</td>\n",
       "      <td>-73.994355</td>\n",
       "      <td>40.728225</td>\n",
       "      <td>-73.994710</td>\n",
       "      <td>40.750325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44984355</td>\n",
       "      <td>2009-08-24 21:45:00.00000061</td>\n",
       "      <td>12.9</td>\n",
       "      <td>2009-08-24 21:45:00 UTC</td>\n",
       "      <td>-74.005043</td>\n",
       "      <td>40.740770</td>\n",
       "      <td>-73.962565</td>\n",
       "      <td>40.772647</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25894730</td>\n",
       "      <td>2009-06-26 08:22:21.0000001</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2009-06-26 08:22:21 UTC</td>\n",
       "      <td>-73.976124</td>\n",
       "      <td>40.790844</td>\n",
       "      <td>-73.965316</td>\n",
       "      <td>40.803349</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17610152</td>\n",
       "      <td>2014-08-28 17:47:00.000000188</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2014-08-28 17:47:00 UTC</td>\n",
       "      <td>-73.925023</td>\n",
       "      <td>40.744085</td>\n",
       "      <td>-73.973082</td>\n",
       "      <td>40.761247</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                            key  fare_amount  \\\n",
       "0    24238194    2015-05-07 19:52:06.0000003          7.5   \n",
       "1    27835199    2009-07-17 20:04:56.0000002          7.7   \n",
       "2    44984355   2009-08-24 21:45:00.00000061         12.9   \n",
       "3    25894730    2009-06-26 08:22:21.0000001          5.3   \n",
       "4    17610152  2014-08-28 17:47:00.000000188         16.0   \n",
       "\n",
       "           pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0  2015-05-07 19:52:06 UTC        -73.999817        40.738354   \n",
       "1  2009-07-17 20:04:56 UTC        -73.994355        40.728225   \n",
       "2  2009-08-24 21:45:00 UTC        -74.005043        40.740770   \n",
       "3  2009-06-26 08:22:21 UTC        -73.976124        40.790844   \n",
       "4  2014-08-28 17:47:00 UTC        -73.925023        40.744085   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "0         -73.999512         40.723217                1  \n",
       "1         -73.994710         40.750325                1  \n",
       "2         -73.962565         40.772647                1  \n",
       "3         -73.965316         40.803349                3  \n",
       "4         -73.973082         40.761247                5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "017dbb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>fare_amount</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>count</th>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>199999.000000</td>\n",
       "      <td>199999.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.771250e+07</td>\n",
       "      <td>11.359955</td>\n",
       "      <td>-72.527638</td>\n",
       "      <td>39.935885</td>\n",
       "      <td>-72.525292</td>\n",
       "      <td>39.923890</td>\n",
       "      <td>1.684535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.601382e+07</td>\n",
       "      <td>9.901776</td>\n",
       "      <td>11.437787</td>\n",
       "      <td>7.720539</td>\n",
       "      <td>13.117408</td>\n",
       "      <td>6.794829</td>\n",
       "      <td>1.385997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-52.000000</td>\n",
       "      <td>-1340.648410</td>\n",
       "      <td>-74.015515</td>\n",
       "      <td>-3356.666300</td>\n",
       "      <td>-881.985513</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.382535e+07</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-73.992065</td>\n",
       "      <td>40.734796</td>\n",
       "      <td>-73.991407</td>\n",
       "      <td>40.733823</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.774550e+07</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>-73.981823</td>\n",
       "      <td>40.752592</td>\n",
       "      <td>-73.980093</td>\n",
       "      <td>40.753042</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.155530e+07</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>-73.967154</td>\n",
       "      <td>40.767158</td>\n",
       "      <td>-73.963658</td>\n",
       "      <td>40.768001</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.542357e+07</td>\n",
       "      <td>499.000000</td>\n",
       "      <td>57.418457</td>\n",
       "      <td>1644.421482</td>\n",
       "      <td>1153.572603</td>\n",
       "      <td>872.697628</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0    fare_amount  pickup_longitude  pickup_latitude  \\\n",
       "count  2.000000e+05  200000.000000     200000.000000    200000.000000   \n",
       "mean   2.771250e+07      11.359955        -72.527638        39.935885   \n",
       "std    1.601382e+07       9.901776         11.437787         7.720539   \n",
       "min    1.000000e+00     -52.000000      -1340.648410       -74.015515   \n",
       "25%    1.382535e+07       6.000000        -73.992065        40.734796   \n",
       "50%    2.774550e+07       8.500000        -73.981823        40.752592   \n",
       "75%    4.155530e+07      12.500000        -73.967154        40.767158   \n",
       "max    5.542357e+07     499.000000         57.418457      1644.421482   \n",
       "\n",
       "       dropoff_longitude  dropoff_latitude  passenger_count  \n",
       "count      199999.000000     199999.000000    200000.000000  \n",
       "mean          -72.525292         39.923890         1.684535  \n",
       "std            13.117408          6.794829         1.385997  \n",
       "min         -3356.666300       -881.985513         0.000000  \n",
       "25%           -73.991407         40.733823         1.000000  \n",
       "50%           -73.980093         40.753042         1.000000  \n",
       "75%           -73.963658         40.768001         2.000000  \n",
       "max          1153.572603        872.697628       208.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e69c1748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "947a2883",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Unnamed: 0','key'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f22901be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fare_amount          0\n",
       "pickup_datetime      0\n",
       "pickup_longitude     0\n",
       "pickup_latitude      0\n",
       "dropoff_longitude    1\n",
       "dropoff_latitude     1\n",
       "passenger_count      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07fedcf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88f77e5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199999, 7)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31ff0eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df[\"pickup_longitude\"]>=-180]\n",
    "df = df.loc[df[\"pickup_longitude\"]<=180]\n",
    "df = df.loc[df[\"pickup_latitude\"]>=-90]\n",
    "df = df.loc[df[\"pickup_latitude\"]<=90]\n",
    "df = df.loc[df[\"dropoff_longitude\"]>=-180]\n",
    "df = df.loc[df[\"dropoff_longitude\"]<=180]\n",
    "df = df.loc[df[\"dropoff_latitude\"]>=-90]\n",
    "df = df.loc[df[\"dropoff_latitude\"]<=90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c054be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199987, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6b7f363",
   "metadata": {},
   "outputs": [],
   "source": [
    "#haversine distance\n",
    "def distance_transform(longitude1,latitude1,longitude2,latitude2):\n",
    "    long1,lati1,long2,lati2=map(np.radians,[longitude1,latitude1,longitude2,latitude2])\n",
    "    dist_longitude=long2-long1\n",
    "    dist_latitude=lati2-lati1\n",
    "    a=np.sin(dist_latitude/2)**2 + np.cos(lati1)*np.cos(lati2)*np.sin(dist_longitude/2)**2\n",
    "    c=2*6371*np.arcsin(np.sqrt(a))\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f3734857",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Distance']= distance_transform( df['pickup_longitude'], df['pickup_latitude'], \n",
    "                                   df['dropoff_longitude'],df['dropoff_latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11e682c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>2015-05-07 19:52:06 UTC</td>\n",
       "      <td>-73.999817</td>\n",
       "      <td>40.738354</td>\n",
       "      <td>-73.999512</td>\n",
       "      <td>40.723217</td>\n",
       "      <td>1</td>\n",
       "      <td>1.683323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2009-07-17 20:04:56 UTC</td>\n",
       "      <td>-73.994355</td>\n",
       "      <td>40.728225</td>\n",
       "      <td>-73.994710</td>\n",
       "      <td>40.750325</td>\n",
       "      <td>1</td>\n",
       "      <td>2.457590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.9</td>\n",
       "      <td>2009-08-24 21:45:00 UTC</td>\n",
       "      <td>-74.005043</td>\n",
       "      <td>40.740770</td>\n",
       "      <td>-73.962565</td>\n",
       "      <td>40.772647</td>\n",
       "      <td>1</td>\n",
       "      <td>5.036377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.3</td>\n",
       "      <td>2009-06-26 08:22:21 UTC</td>\n",
       "      <td>-73.976124</td>\n",
       "      <td>40.790844</td>\n",
       "      <td>-73.965316</td>\n",
       "      <td>40.803349</td>\n",
       "      <td>3</td>\n",
       "      <td>1.661683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2014-08-28 17:47:00 UTC</td>\n",
       "      <td>-73.925023</td>\n",
       "      <td>40.744085</td>\n",
       "      <td>-73.973082</td>\n",
       "      <td>40.761247</td>\n",
       "      <td>5</td>\n",
       "      <td>4.475450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fare_amount          pickup_datetime  pickup_longitude  pickup_latitude  \\\n",
       "0          7.5  2015-05-07 19:52:06 UTC        -73.999817        40.738354   \n",
       "1          7.7  2009-07-17 20:04:56 UTC        -73.994355        40.728225   \n",
       "2         12.9  2009-08-24 21:45:00 UTC        -74.005043        40.740770   \n",
       "3          5.3  2009-06-26 08:22:21 UTC        -73.976124        40.790844   \n",
       "4         16.0  2014-08-28 17:47:00 UTC        -73.925023        40.744085   \n",
       "\n",
       "   dropoff_longitude  dropoff_latitude  passenger_count  Distance  \n",
       "0         -73.999512         40.723217                1  1.683323  \n",
       "1         -73.994710         40.750325                1  2.457590  \n",
       "2         -73.962565         40.772647                1  5.036377  \n",
       "3         -73.965316         40.803349                3  1.661683  \n",
       "4         -73.973082         40.761247                5  4.475450  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1221de1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare_amount')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Distance'],df['fare_amount'])\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"fare_amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b8fa79dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193857, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[df['fare_amount']>0]\n",
    "df = df.loc[df['Distance']>0]\n",
    "df = df.loc[df['Distance']<70]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "19bdd83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare_amount')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Distance'],df['fare_amount'])\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"fare_amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "960abfcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_39101/3065595379.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr(), annot=True, fmt=\".2f\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d6648e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Distance'].values.reshape(-1,1)\n",
    "y = df['fare_amount'].values.reshape(-1,1)\n",
    "\n",
    "ss = StandardScaler()\n",
    "x_st = ss.fit_transform(x)\n",
    "y_st = ss.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "986f82ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x_st, y_st, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6351040f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2aeb3a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_39101/1446996702.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(xtrain,ytrain)\n"
     ]
    },
    {
     "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>RandomForestRegressor()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(xtrain,ytrain)\n",
    "rf.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "308d07e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_pred = lr.predict(xtest)\n",
    "rf_pred = rf.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "66681c0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:[[0.8694432]] Intercept:[-0.00038648]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Coefficient:{lr.coef_} Intercept:{lr.intercept_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8f6e8e41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression\n",
      "MAE: 0.24242896988112872\n",
      "MSE: 0.2379950077864864\n",
      "RMSE 0.48784732015917276 \n",
      "\n",
      "Random Forest Regression\n",
      "MAE: 0.2914608013258209\n",
      "MSE: 0.30912530714259784\n",
      "RMSE 0.5559903840378877\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression\")\n",
    "print(\"MAE:\", mean_absolute_error(ytest,lr_pred))\n",
    "print(\"MSE:\", mean_squared_error(ytest,lr_pred))\n",
    "print(\"RMSE\", np.sqrt(mean_squared_error(ytest,lr_pred)),\"\\n\")\n",
    "\n",
    "print(\"Random Forest Regression\")\n",
    "print(\"MAE:\", mean_absolute_error(ytest,rf_pred))\n",
    "print(\"MSE:\", mean_squared_error(ytest,rf_pred))\n",
    "print(\"RMSE\", np.sqrt(mean_squared_error(ytest,rf_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c88dfd86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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