12_L1_L2_Regularization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "underfit_overfit.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L1 and L2 Regularization\n",
    "\n",
    "- To deal with issue of ovefitting in ML we can use L1 and L2 regularization.\n",
    "- accuracy of overfit model can be improved.\n",
    "\n",
    "    ![underfit_overfit.png](attachment:underfit_overfit.png)\n",
    "        \n",
    "**How to reduce Overfitting**\n",
    "- overfit equation = $\\theta_0$ + $\\theta_1$ * age + $\\theta_2$ * $age^2$ + $\\theta_3$ * $age^3$ + $\\theta_4$ * $age^4$\n",
    "- Try to make $\\theta_3$ and $\\theta_4$ almost close to zero\n",
    "- new equation = $\\theta_0$ + $\\theta_1$ * age + $\\theta_2$ * $age^2$\n",
    "- this is a balanced fit equation\n",
    "- MSE = $\\frac{1}{n}\\Sigma_{i=1}^n({y}-\\hat{y})^2$\n",
    "- y_predicted = $\\hat{y}$ = $h_\\theta(x_i)$\n",
    "- $h_\\theta(x_i) = \\theta_0 + \\theta_1x_1 + \\theta_2x_2^2 + \\theta_3x_3^3$   ->  (x is age in this case)\n",
    "- MSE = $\\frac{1}{n}\\Sigma_{i=1}^n({y}-h_\\theta(x_i))^2$\n",
    "- we want to minimize the MSE\n",
    "- add $\\lambda\\Sigma_{i=1}^n\\theta i^2$ to equation\n",
    "- MSE = $\\frac{1}{n}\\Sigma_{i=1}^n({y}-h_\\theta(x_i))^2 + \\lambda\\Sigma_{i=1}^n\\theta_i^2$ \n",
    "- $\\lambda$ is a free controllabe parameter, its like a tuning knob. And we are making square of each $\\theta_i$ parameter.\n",
    "- if the $\\theta$ gets bigger the value of $\\lambda\\Sigma_{i=1}^n\\theta_i^2$ will get bigger, it acts as a penalty, when the $\\theta$ gets higher we are adding a penalty. So the $\\theta$ don't go too high. Also we can fine tune this with $\\lambda$\n",
    "- **L2 Regularisation** we use $\\theta_i^2$\n",
    "- **L1 Regularisation** we use $|\\theta_i|$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppress warning for clean notebook\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Suburb</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Distance</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb  Rooms Type      Price Method SellerG             Regionname  \\\n",
       "0  Abbotsford      2    h        NaN     SS  Jellis  Northern Metropolitan   \n",
       "1  Abbotsford      2    h  1480000.0      S  Biggin  Northern Metropolitan   \n",
       "2  Abbotsford      2    h  1035000.0      S  Biggin  Northern Metropolitan   \n",
       "\n",
       "   Distance         CouncilArea  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "0       2.5  Yarra City Council       2.0       1.0  1.0     126.0   \n",
       "1       2.5  Yarra City Council       2.0       1.0  1.0     202.0   \n",
       "2       2.5  Yarra City Council       2.0       1.0  0.0     156.0   \n",
       "\n",
       "   BuildingArea  Propertycount  \n",
       "0           NaN         4019.0  \n",
       "1           NaN         4019.0  \n",
       "2          79.0         4019.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read dataset\n",
    "dataset = pd.read_csv(\"ML_Algo_Datasets\\Melbourne_housing_FULL.csv\")\n",
    "# usefull columns\n",
    "dataset = dataset[['Suburb', 'Rooms', 'Type', 'Price', 'Method', 'SellerG','Regionname', 'Distance','CouncilArea', 'Bedroom2', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'Propertycount']]\n",
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price             7610\n",
       "Method               0\n",
       "SellerG              0\n",
       "Regionname           3\n",
       "Distance             1\n",
       "CouncilArea          3\n",
       "Bedroom2          8217\n",
       "Bathroom          8226\n",
       "Car               8728\n",
       "Landsize         11810\n",
       "BuildingArea     21115\n",
       "Propertycount        3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price             7610\n",
       "Method               0\n",
       "SellerG              0\n",
       "Regionname           3\n",
       "Distance             0\n",
       "CouncilArea          3\n",
       "Bedroom2             0\n",
       "Bathroom             0\n",
       "Car                  0\n",
       "Landsize         11810\n",
       "BuildingArea     21115\n",
       "Propertycount        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill na of the following columns with 0\n",
    "dataset[[\"Propertycount\", \"Distance\", \"Bedroom2\", \"Bathroom\", \"Car\"]] = dataset[[\"Propertycount\", \"Distance\", \"Bedroom2\", \"Bathroom\", \"Car\"]].fillna(0)\n",
    "\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb              0\n",
       "Rooms               0\n",
       "Type                0\n",
       "Price            7610\n",
       "Method              0\n",
       "SellerG             0\n",
       "Regionname          3\n",
       "Distance            0\n",
       "CouncilArea         3\n",
       "Bedroom2            0\n",
       "Bathroom            0\n",
       "Car                 0\n",
       "Landsize            0\n",
       "BuildingArea        0\n",
       "Propertycount       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna Landsize and BuildingArea with mean\n",
    "dataset[\"Landsize\"] = dataset[\"Landsize\"].fillna(dataset.Landsize.mean())\n",
    "dataset[\"BuildingArea\"] = dataset[\"BuildingArea\"].fillna(dataset.BuildingArea.mean())\n",
    "\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb           0\n",
       "Rooms            0\n",
       "Type             0\n",
       "Price            0\n",
       "Method           0\n",
       "SellerG          0\n",
       "Regionname       0\n",
       "Distance         0\n",
       "CouncilArea      0\n",
       "Bedroom2         0\n",
       "Bathroom         0\n",
       "Car              0\n",
       "Landsize         0\n",
       "BuildingArea     0\n",
       "Propertycount    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dropna(inplace=True)\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Suburb_Aberfeldie</th>\n",
       "      <th>...</th>\n",
       "      <th>CouncilArea_Moorabool Shire Council</th>\n",
       "      <th>CouncilArea_Moreland City Council</th>\n",
       "      <th>CouncilArea_Nillumbik Shire Council</th>\n",
       "      <th>CouncilArea_Port Phillip City Council</th>\n",
       "      <th>CouncilArea_Stonnington City Council</th>\n",
       "      <th>CouncilArea_Whitehorse City Council</th>\n",
       "      <th>CouncilArea_Whittlesea City Council</th>\n",
       "      <th>CouncilArea_Wyndham City Council</th>\n",
       "      <th>CouncilArea_Yarra City Council</th>\n",
       "      <th>CouncilArea_Yarra Ranges Shire Council</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 745 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms      Price  Distance  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "1      2  1480000.0       2.5       2.0       1.0  1.0     202.0   \n",
       "2      2  1035000.0       2.5       2.0       1.0  0.0     156.0   \n",
       "4      3  1465000.0       2.5       3.0       2.0  0.0     134.0   \n",
       "5      3   850000.0       2.5       3.0       2.0  1.0      94.0   \n",
       "6      4  1600000.0       2.5       3.0       1.0  2.0     120.0   \n",
       "\n",
       "   BuildingArea  Propertycount  Suburb_Aberfeldie  ...  \\\n",
       "1      160.2564         4019.0              False  ...   \n",
       "2       79.0000         4019.0              False  ...   \n",
       "4      150.0000         4019.0              False  ...   \n",
       "5      160.2564         4019.0              False  ...   \n",
       "6      142.0000         4019.0              False  ...   \n",
       "\n",
       "   CouncilArea_Moorabool Shire Council  CouncilArea_Moreland City Council  \\\n",
       "1                                False                              False   \n",
       "2                                False                              False   \n",
       "4                                False                              False   \n",
       "5                                False                              False   \n",
       "6                                False                              False   \n",
       "\n",
       "   CouncilArea_Nillumbik Shire Council  CouncilArea_Port Phillip City Council  \\\n",
       "1                                False                                  False   \n",
       "2                                False                                  False   \n",
       "4                                False                                  False   \n",
       "5                                False                                  False   \n",
       "6                                False                                  False   \n",
       "\n",
       "   CouncilArea_Stonnington City Council  CouncilArea_Whitehorse City Council  \\\n",
       "1                                 False                                False   \n",
       "2                                 False                                False   \n",
       "4                                 False                                False   \n",
       "5                                 False                                False   \n",
       "6                                 False                                False   \n",
       "\n",
       "   CouncilArea_Whittlesea City Council  CouncilArea_Wyndham City Council  \\\n",
       "1                                False                             False   \n",
       "2                                False                             False   \n",
       "4                                False                             False   \n",
       "5                                False                             False   \n",
       "6                                False                             False   \n",
       "\n",
       "   CouncilArea_Yarra City Council  CouncilArea_Yarra Ranges Shire Council  \n",
       "1                            True                                   False  \n",
       "2                            True                                   False  \n",
       "4                            True                                   False  \n",
       "5                            True                                   False  \n",
       "6                            True                                   False  \n",
       "\n",
       "[5 rows x 745 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.get_dummies(dataset, drop_first=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(\"Price\", axis=1)\n",
    "Y = dataset[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression().fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1385368316157114"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6827792395792723"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As you can see the score for test data is very less and score for train data is fairly nice. i.e. the model is very overfitted for training data and not generalized for data not seen.\n",
    "\n",
    "#### Lasso Regression\n",
    "- Lasso regression is L1 regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=50, max_iter=100, tol=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=50, max_iter=100, tol=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lasso_reg = linear_model.Lasso(alpha=50, max_iter=100, tol=0.1)\n",
    "lasso_reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6636248009953578"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6766982470577766"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ridge Regression\n",
    "- Ridge regression is L2 regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=50, max_iter=100, tol=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Ridge<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=50, max_iter=100, tol=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_reg = Ridge(alpha=50, max_iter=100, tol=0.1)\n",
    "ridge_reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6670848945194959"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6622376739684328"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(X_train, Y_train)"
   ]
  }
 ],
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  "kernelspec": {
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  "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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