Capstone_project_Taxifare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bee62b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "pd.set_option(\"display.max_rows\",None)\n",
    "pd.set_option(\"display.max_columns\",None)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4ce0ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Taxifare.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07a3bae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount</th>\n",
       "      <th>longitude_of_pickup</th>\n",
       "      <th>latitude_of_pickup</th>\n",
       "      <th>longitude_of_dropoff</th>\n",
       "      <th>latitude_of_dropoff</th>\n",
       "      <th>no_of_passenger</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39972</th>\n",
       "      <td>30.5</td>\n",
       "      <td>-73.963335</td>\n",
       "      <td>40.808983</td>\n",
       "      <td>-73.933162</td>\n",
       "      <td>40.856657</td>\n",
       "      <td>1</td>\n",
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       "      <th>11234</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-74.006845</td>\n",
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       "      <td>-74.008253</td>\n",
       "      <td>40.705662</td>\n",
       "      <td>4</td>\n",
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       "      <th>39652</th>\n",
       "      <td>9.5</td>\n",
       "      <td>-73.969497</td>\n",
       "      <td>40.753849</td>\n",
       "      <td>-73.954609</td>\n",
       "      <td>40.779899</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37392</th>\n",
       "      <td>31.7</td>\n",
       "      <td>-73.776709</td>\n",
       "      <td>40.645286</td>\n",
       "      <td>-73.940009</td>\n",
       "      <td>40.613348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38451</th>\n",
       "      <td>5.3</td>\n",
       "      <td>-74.005127</td>\n",
       "      <td>40.736052</td>\n",
       "      <td>-74.006950</td>\n",
       "      <td>40.743393</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>17441</th>\n",
       "      <td>6.1</td>\n",
       "      <td>-73.990395</td>\n",
       "      <td>40.746235</td>\n",
       "      <td>-73.980563</td>\n",
       "      <td>40.751652</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>15292</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-73.965435</td>\n",
       "      <td>40.759497</td>\n",
       "      <td>-73.980545</td>\n",
       "      <td>40.738370</td>\n",
       "      <td>1</td>\n",
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       "      <th>19586</th>\n",
       "      <td>14.1</td>\n",
       "      <td>-73.986504</td>\n",
       "      <td>40.761174</td>\n",
       "      <td>-73.987796</td>\n",
       "      <td>40.710631</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10610</th>\n",
       "      <td>12.5</td>\n",
       "      <td>-74.006540</td>\n",
       "      <td>40.704040</td>\n",
       "      <td>-74.008615</td>\n",
       "      <td>40.716227</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48998</th>\n",
       "      <td>18.1</td>\n",
       "      <td>-73.925868</td>\n",
       "      <td>40.772402</td>\n",
       "      <td>-73.972690</td>\n",
       "      <td>40.752827</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32829</th>\n",
       "      <td>6.5</td>\n",
       "      <td>-73.994103</td>\n",
       "      <td>40.732230</td>\n",
       "      <td>-73.997965</td>\n",
       "      <td>40.725542</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39209</th>\n",
       "      <td>6.0</td>\n",
       "      <td>-73.966077</td>\n",
       "      <td>40.759831</td>\n",
       "      <td>-73.957846</td>\n",
       "      <td>40.770427</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6903</th>\n",
       "      <td>12.5</td>\n",
       "      <td>-73.996962</td>\n",
       "      <td>40.724002</td>\n",
       "      <td>-73.965287</td>\n",
       "      <td>40.759287</td>\n",
       "      <td>1</td>\n",
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       "      <th>35427</th>\n",
       "      <td>8.5</td>\n",
       "      <td>-73.977404</td>\n",
       "      <td>40.779576</td>\n",
       "      <td>-73.995606</td>\n",
       "      <td>40.754289</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40025</th>\n",
       "      <td>8.5</td>\n",
       "      <td>-73.949903</td>\n",
       "      <td>40.717788</td>\n",
       "      <td>-73.928547</td>\n",
       "      <td>40.706315</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>17.5</td>\n",
       "      <td>-73.977737</td>\n",
       "      <td>40.733809</td>\n",
       "      <td>-73.957989</td>\n",
       "      <td>40.784669</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21658</th>\n",
       "      <td>3.5</td>\n",
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       "      <td>40.759560</td>\n",
       "      <td>-73.971725</td>\n",
       "      <td>40.753860</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23470</th>\n",
       "      <td>8.5</td>\n",
       "      <td>-73.990218</td>\n",
       "      <td>40.740758</td>\n",
       "      <td>-74.006170</td>\n",
       "      <td>40.719303</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48342</th>\n",
       "      <td>19.7</td>\n",
       "      <td>-74.000065</td>\n",
       "      <td>40.678553</td>\n",
       "      <td>-73.974515</td>\n",
       "      <td>40.749848</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21095</th>\n",
       "      <td>16.0</td>\n",
       "      <td>-73.975898</td>\n",
       "      <td>40.754418</td>\n",
       "      <td>-73.977368</td>\n",
       "      <td>40.791957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       amount  longitude_of_pickup  latitude_of_pickup  longitude_of_dropoff  \\\n",
       "39972    30.5           -73.963335           40.808983            -73.933162   \n",
       "11234     9.0           -74.006845           40.706247            -74.008253   \n",
       "39652     9.5           -73.969497           40.753849            -73.954609   \n",
       "37392    31.7           -73.776709           40.645286            -73.940009   \n",
       "38451     5.3           -74.005127           40.736052            -74.006950   \n",
       "17441     6.1           -73.990395           40.746235            -73.980563   \n",
       "15292     6.5           -73.965435           40.759497            -73.980545   \n",
       "19586    14.1           -73.986504           40.761174            -73.987796   \n",
       "10610    12.5           -74.006540           40.704040            -74.008615   \n",
       "48998    18.1           -73.925868           40.772402            -73.972690   \n",
       "32829     6.5           -73.994103           40.732230            -73.997965   \n",
       "39209     6.0           -73.966077           40.759831            -73.957846   \n",
       "6903     12.5           -73.996962           40.724002            -73.965287   \n",
       "35427     8.5           -73.977404           40.779576            -73.995606   \n",
       "40025     8.5           -73.949903           40.717788            -73.928547   \n",
       "14978    17.5           -73.977737           40.733809            -73.957989   \n",
       "21658     3.5           -73.965240           40.759560            -73.971725   \n",
       "23470     8.5           -73.990218           40.740758            -74.006170   \n",
       "48342    19.7           -74.000065           40.678553            -73.974515   \n",
       "21095    16.0           -73.975898           40.754418            -73.977368   \n",
       "\n",
       "       latitude_of_dropoff  no_of_passenger  \n",
       "39972            40.856657                1  \n",
       "11234            40.705662                4  \n",
       "39652            40.779899                1  \n",
       "37392            40.613348                1  \n",
       "38451            40.743393                5  \n",
       "17441            40.751652                5  \n",
       "15292            40.738370                1  \n",
       "19586            40.710631                1  \n",
       "10610            40.716227                1  \n",
       "48998            40.752827                6  \n",
       "32829            40.725542                1  \n",
       "39209            40.770427                1  \n",
       "6903             40.759287                1  \n",
       "35427            40.754289                2  \n",
       "40025            40.706315                1  \n",
       "14978            40.784669                1  \n",
       "21658            40.753860                1  \n",
       "23470            40.719303                1  \n",
       "48342            40.749848                1  \n",
       "21095            40.791957                1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d39a332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount                  0\n",
       "longitude_of_pickup     0\n",
       "latitude_of_pickup      0\n",
       "longitude_of_dropoff    0\n",
       "latitude_of_dropoff     0\n",
       "no_of_passenger         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2259349f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "amount                  float64\n",
       "longitude_of_pickup     float64\n",
       "latitude_of_pickup      float64\n",
       "longitude_of_dropoff    float64\n",
       "latitude_of_dropoff     float64\n",
       "no_of_passenger           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbfdfbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([\"unique_id\",\"date_time_of_pickup\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0cf402",
   "metadata": {},
   "source": [
    "### Linearregression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98556715",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46ae4417",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df[[\"amount\"]]\n",
    "X=df.drop([\"amount\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "51e1e89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.30,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "7b95f568",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "bf119930",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "28b30134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006109610599230875"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "8ac564ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0008268147965095896"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaa40448",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2456d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_model = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1bf189a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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>SVR()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef886f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.09305648722133797"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "430ce82c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.08281387489541947"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd8a0a2",
   "metadata": {},
   "source": [
    "### DecisionTreeRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d95f4238",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4085d0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dtr=DecisionTreeRegressor( max_depth=3,min_samples_split=3,min_samples_leaf=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3b72b623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, min_samples_split=3)</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, min_samples_split=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, min_samples_split=3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dtr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "001c883d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5172445685825964"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dtr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c86d0257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5140445720174682"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dtr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fed645",
   "metadata": {},
   "source": [
    "### BaggingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac6504b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "bedd915a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_btr=BaggingRegressor( n_estimators=12,max_samples=0.9,base_estimator=model_dtr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "5b6a65b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-71 {color: black;}#sk-container-id-71 pre{padding: 0;}#sk-container-id-71 div.sk-toggleable {background-color: white;}#sk-container-id-71 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-71 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-71 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-71 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-71 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-71 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-71 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-71 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-71 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-71 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-71 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-71 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-71 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-71 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-71 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-71 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-71 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-71 div.sk-item {position: relative;z-index: 1;}#sk-container-id-71 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-71 div.sk-item::before, #sk-container-id-71 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-71 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-71 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-71 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-71 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-71 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-71 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-71 div.sk-label-container {text-align: center;}#sk-container-id-71 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-71 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-71\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3,\n",
       "                                                      min_samples_leaf=2,\n",
       "                                                      min_samples_split=3),\n",
       "                 max_samples=0.9, n_estimators=12)</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-157\" type=\"checkbox\" ><label for=\"sk-estimator-id-157\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingRegressor</label><div class=\"sk-toggleable__content\"><pre>BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3,\n",
       "                                                      min_samples_leaf=2,\n",
       "                                                      min_samples_split=3),\n",
       "                 max_samples=0.9, n_estimators=12)</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-158\" type=\"checkbox\" ><label for=\"sk-estimator-id-158\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, min_samples_split=3)</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-159\" type=\"checkbox\" ><label for=\"sk-estimator-id-159\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, min_samples_leaf=2, min_samples_split=3)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingRegressor(base_estimator=DecisionTreeRegressor(max_depth=3,\n",
       "                                                      min_samples_leaf=2,\n",
       "                                                      min_samples_split=3),\n",
       "                 max_samples=0.9, n_estimators=12)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_btr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "6e2adf49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5276369641219172"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_btr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "0afad01b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47233455683454884"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_btr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ad260f",
   "metadata": {},
   "source": [
    "### AdaboostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "d3bbe19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "1d677dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_adr=AdaBoostRegressor(n_estimators=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "3348b639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-49 {color: black;}#sk-container-id-49 pre{padding: 0;}#sk-container-id-49 div.sk-toggleable {background-color: white;}#sk-container-id-49 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-49 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-49 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-49 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-49 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-49 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-49 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-49 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-49 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-49 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-49 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-49 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-49 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-49 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-49 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-49 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-49 div.sk-item {position: relative;z-index: 1;}#sk-container-id-49 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-49 div.sk-item::before, #sk-container-id-49 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-49 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-49 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-49 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-49 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-49 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-49 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-49 div.sk-label-container {text-align: center;}#sk-container-id-49 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-49 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-49\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(n_estimators=60)</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-115\" type=\"checkbox\" checked><label for=\"sk-estimator-id-115\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(n_estimators=60)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostRegressor(n_estimators=60)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "2b26e680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40660454378844646"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "65a7f669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3765303456596606"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_adr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490d045a",
   "metadata": {},
   "source": [
    "### GradientboostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "235cc877",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "145a4e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_gbr= GradientBoostingRegressor(n_estimators=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "c4e768ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-64 {color: black;}#sk-container-id-64 pre{padding: 0;}#sk-container-id-64 div.sk-toggleable {background-color: white;}#sk-container-id-64 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-64 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-64 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-64 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-64 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-64 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-64 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-64 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-64 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-64 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-64 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-64 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-64 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-64 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-64 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-64 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-64 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-64 div.sk-item {position: relative;z-index: 1;}#sk-container-id-64 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-64 div.sk-item::before, #sk-container-id-64 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-64 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-64 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-64 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-64 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-64 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-64 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-64 div.sk-label-container {text-align: center;}#sk-container-id-64 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-64 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-64\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(n_estimators=2)</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-138\" type=\"checkbox\" checked><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(n_estimators=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(n_estimators=2)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gbr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "da1f0372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17814836328665562"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gbr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "2cb40d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16137487801552486"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gbr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d242ab8",
   "metadata": {},
   "source": [
    "### RandomForestregressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "fadeb381",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "a1895523",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_rbr=RandomForestRegressor(n_estimators=20,max_depth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "bd1ee8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-82 {color: black;}#sk-container-id-82 pre{padding: 0;}#sk-container-id-82 div.sk-toggleable {background-color: white;}#sk-container-id-82 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-82 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-82 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-82 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-82 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-82 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-82 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-82 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-82 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-82 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-82 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-82 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-82 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-82 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-82 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-82 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-82 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-82 div.sk-item {position: relative;z-index: 1;}#sk-container-id-82 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-82 div.sk-item::before, #sk-container-id-82 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-82 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-82 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-82 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-82 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-82 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-82 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-82 div.sk-label-container {text-align: center;}#sk-container-id-82 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-82 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-82\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=1, n_estimators=20)</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-170\" type=\"checkbox\" checked><label for=\"sk-estimator-id-170\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=1, n_estimators=20)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=1, n_estimators=20)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rbr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "fc81e043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15350905254699465"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rbr.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "84c579c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18056671789810375"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rbr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b477fa75",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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