Linear_regression Lasso.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "# Suppress warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_data(sample_size):\n",
    "    X = np.sort(5 * np.random.rand(sample_size))\n",
    "    y = 0.5 * X**2 + 2 * X + 1 + np.random.normal(0, 1, sample_size)\n",
    "    return X, y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 100\n",
    "X, y = generate_data(sample_size)\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(X, y, label='Noisy Data')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>X</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.027611</td>\n",
       "      <td>1.142649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.102922</td>\n",
       "      <td>0.912134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.127096</td>\n",
       "      <td>1.354029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.171943</td>\n",
       "      <td>-0.628902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.226136</td>\n",
       "      <td>1.258170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X         y\n",
       "0  0.027611  1.142649\n",
       "1  0.102922  0.912134\n",
       "2  0.127096  1.354029\n",
       "3  0.171943 -0.628902\n",
       "4  0.226136  1.258170"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'X': X, 'y': y})\n",
    "\n",
    "# Display the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df[['X']],df[['y']],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 1)\n",
      "(20, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train.head()\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polynomial_features(X, degree):\n",
    "    features = np.column_stack([X**i for i in range(1, degree + 1)])\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_poly=polynomial_features(X_train,11)\n",
    "X_test_poly=polynomial_features(X_test,11)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 11)\n",
      "(20, 11)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_poly.shape)\n",
    "print(X_test_poly.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_poly, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.970684241562439"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test_poly,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15.60065601],\n",
       "       [17.22561012],\n",
       "       [16.03921855],\n",
       "       [22.21041895],\n",
       "       [ 0.79633585],\n",
       "       [15.90746144],\n",
       "       [14.26093628],\n",
       "       [17.50523214],\n",
       "       [20.26733731],\n",
       "       [ 8.19993025],\n",
       "       [ 5.88554396],\n",
       "       [ 8.62494081],\n",
       "       [12.52768777],\n",
       "       [18.20190233],\n",
       "       [ 5.47784929],\n",
       "       [ 7.91803769],\n",
       "       [ 1.10841841],\n",
       "       [ 1.72250832],\n",
       "       [13.70486612],\n",
       "       [16.37244499]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(X_test_poly)\n",
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>15.162749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>17.909355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>18.127489</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.518950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.642937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>8.419584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>12.641203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>19.145031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.794900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10.351153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.258170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.730381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>17.031859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y\n",
       "76  15.162749\n",
       "83  17.909355\n",
       "80  18.127489\n",
       "98  22.734853\n",
       "2    1.354029\n",
       "77  16.371805\n",
       "71  13.027354\n",
       "84  16.492149\n",
       "89  20.124239\n",
       "50   7.518950\n",
       "40   5.642937\n",
       "51   8.419584\n",
       "67  12.641203\n",
       "86  19.145031\n",
       "37   7.794900\n",
       "49  10.351153\n",
       "4    1.258170\n",
       "10   2.730381\n",
       "69  14.476339\n",
       "81  17.031859"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbe0a745e90>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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iIm3evFkPP/xwj+eDwWBUMPH7/fL5fGpqalJeXt5ASwMAAAb5/X55vd4+fX/Hve9rxIgRKioq0smTJ3t93uVyyeVyxbsMAACQIOK+ydilS5fU2NioESNGxPtXAQCAJNDvno8vvvhCDQ0NketTp07pyJEjGjZsmIYNG6bVq1fru9/9rkaMGKHTp0/rJz/5ie644w799V//dUwLBwD0FAi1avyzuySFV5wwuROJqN//r6ytrdVDDz0UuV65cqUkqaKiQhs3btTRo0f16quv6sqVKxoxYoQeeughbdmyRR6PJ3ZVAwCApNXv8DF9+nTdbI7qrl27bqsgAED/BUKtHee2bm1dj2/YA2Lb0umD0uj7w7e3BwygPw4AUkDnUEt3nZt7SdLp9XN6/8GGPdJrC6Tvb5XGzohXeUAU7moLAOmsvjr6DBhAzwcApIA+b2fe3i7VviRdv9Lxg9u6zkOLwo8HDZFKFksZ/H2K+CB8AEAK6G1OR+d25lFamqX31krXLkuyJKsjYISapb1rJdlS7lBp0kLJxUIBxAexFgDSicsjLTkg+UrD13Zb9NlXKi09SPBAXN3W9urx0J/tWQEAA9Qakn5WJLUEutqy3dKqM1JmtnN1IWn15/ubng8ASEdn66KDhxS+PlvnTD1IK4QPAEhHJ3aEz3fPlZ44LI3rWIp7fLtzNSFtMOEUAFJNXzYOGzdbyi+WiheEX7PwNenoG9IQn9lakZbo+QCAVNOwR9o0V2p498avGTVFmvhIVzixrPD1qClmakRaI3wAQKph4zAkOIZdACDZsXEYkgzhAwCSHRuHIckQgQEg2bFxGJIM4QMAUsEQn1TxtpSdG92enSstqpG8I52pC+gF4QMAUgUbhyFJED4AIFWwcRiSBBNOASBVsHEYkgThAwBSxagp0ZuEdW4cBiQYhl0AIE0FQq0avapGo1fVKBBqdbocpBHCBwAAMIphFwBIM529HIFQW7e2rsfuHL4aEF/8PwwA0sz4Z3f1aCtZsyfy+PT6OSbLQRpi2AUAABhFzwcApJn6ylmSwkMtnT0etc/MkDsn08mykEYIHwBgWCDUGhn6qK+cZXyORW+/z52TyVwPGMOwCwA4wtaUjHrJtp0uBDCOmAsAhnRfZTI949/1Ss5zajoxQYFxMyWZX2XizslicikcQfgAAEO6rzL5WdZHkqQdW/6nVrW2S2KVCdIH4QMATGhv1+OZ7yhP4bvOzs78beTcaA8Pv+ajz6WSxVIGI+JIbZZtJ9aAo9/vl9frVVNTk/Ly8pwuBwBiI3hV9i+LZV2/LFuW2mxLWVa7bCtTsttlyZZyh0orjkouj9PVAv3Wn+9v4jUAmODyyFp6QPKVSrKVZYWHWiy7TZYUbl96kOCBtED4AABThvikirelbHd0e3autKhG8o50pi7AMMIHAJh0tk5WSyC6rSUgna1zph7AAYQPADDpxI7w+e650hOHpXEdK1yOb3euJsAwVrsAgEnjZkv5xVLxAsmypIWvSUffCA/JAGmC8AEAJo2aEj46WZY08RHn6gEcwLALAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAG4oEGrV6FU1Gr2qRoFQq9PlAEgRhA8AAGBUltMFAEg8nb0cgVBbt7aux+4c/ukAMHD8CwKgh/HP7urRVrJmT+Tx6fVzTJYDIMUw7AIAAIzqd/jYv3+/5s2bp8LCQlmWperq6qjnbdvW6tWrVVhYqNzcXE2fPl3Hjh2LVb0ADKivnKX6ylmqfWZGpK32mRmRdgC4Hf0OH83NzZo0aZI2bNjQ6/PPPfecfvGLX2jDhg06dOiQCgoKNHPmTF29evW2iwVghjsnq+PI7NaWGWkHgNvR739FysvLVV5e3utztm3r+eef19NPP62HH35YkrRp0ybl5+fr9ddf15IlS26vWgAAkPRiOufj1KlTOn/+vMrKyiJtLpdLDz74oD744INY/ioABrhzsnR6/RydXj+HHg8AMRPTf03Onz8vScrPz49qz8/P16efftrrzwSDQQWDwci13++PZUkAACDBxGW1i2VZUde2bfdo61RVVSWv1xs5fD5fPEoCAAAJIqbho6CgQFJXD0inCxcu9OgN6fTUU0+pqakpcjQ2NsayJAAAkGBiGj7GjBmjgoIC7d69O9IWCoW0b98+TZs2rdefcblcysvLizoAAEDq6vecjy+++EINDQ2R61OnTunIkSMaNmyYRo0apRUrVmjdunUaO3asxo4dq3Xr1sntduuxxx6LaeEAACA59Tt81NbW6qGHHopcr1y5UpJUUVGhV155RU8++aSuXbumH/zgB7p8+bJKS0v1zjvvyOPxxK5qAACQtCzbtm2ni+jO7/fL6/WqqamJIRgAAJJEf76/ubcLAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8ADDDtqVTB8JnAGmN8AHAjIY90qa5UsO7TlcCwGGEDwBm1FdHnwGkrSynCwCQotrbpdqXpOtXwtf127rOQ4vCjwcNkUoWSxn8HQSkE8IHgPhoaZbeWytduyxbltpsS1mWZIeaZe1dK8mWcodKkxZKLo/T1QIwiD83AMSHyyMtOSD5SiXZyrLaJUmW3RZ+3lcqLT1I8ADSEOEDwMDdYgVLwD1CgceqpWx39I9l5yrw2DbJO9JAkQASDeEDwMDdYgXL+Gd36W8qN8pqCUS1Wy0BPV650USFABIQ4QPAwPVhBcuMzMOSpJ1tJXog+EvtapssSZqZ+bs4FwcgUTHhFEDf9XMFS33lLGU0DtHVP83W0q15kixNfnK7gg3VWpTnc+A/AEAiIHwAuDnblk4flEbfH7WCRbIkq6PzNNQs9bKCxZ2TJd35F2r3lUpbd0mS3K4suf58oWP/OQCcx7ALgJvrPq8jagWLpM6VK6xgAdAP9HwAuLnu8zrGzpCG+KSKt6WfFUndJ5Jm50qLaqTM7B5v4c7J0un1c4yUCyDxET6ADoFQq8Y/Gx4aqK+cFR4ySEd9mdcR+FN08JDC12frpFFTjJUKIDml6b+uAG6oL/M6slzhtrvnSmV/L+16RjpeIx3fTvgAcEuED6S9QKi149zWra3rcdr1gHTO69i6WGr86EvzOqzwvI6py6XWoFS8QLIsaeFr0tE3wkMyAHALlm3fYGtCh/j9fnm9XjU1NSkvL8/pcpAGRq+quenzaTtXoTXUy7wOt7TqTK/zOgCkt/58f7PaBUDvztbdeF4HANyGNOtPBnqqr5wlKTzUUrJmjySp9pkZcudkOlmW807sCJ9vMK+DCboABop/LZD2evvSdOdk8mU6braUX8y8DgAxl+b/ugK4oVFToleuWJY08ZHwBN1QKxN0AQwYE04B9AsTdAH0hgmnAAAgYdE/CqBfmKAL4HYRPgD0CxN0Adwuhl0AAIBR/KkCYEC4Uy2AgaLnAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPoL9sWzp1IHwGAPQb4QPor4Y90qa5UsO7TlcCAEmJ8AH0V3119BkA0C9ZThcAJLz2dqn2Jen6lfB1/bau89Ci8ONBQ6SSxVIGeR4AboXwAdxKS7P03lrp2mXZstRmW8qyJDvULGvvWkm2lDtUmrRQcnmcrhYAEh5/pgG34vJISw5IvlJJtrKsdkmSZbeFn/eVSksPEjwAoI8IH0B3N1jJEnCPUOCxainbHf3y7FwFHtsmeUcaLBIAkhvhA+juBitZxj+7S39TuVFWSyCq3WoJ6PHKjSYrBICkR/gAurvJSpYZmYclSTvbSvRA8Jfa1TZZkjQz83eGigOA1MCEU6S3Pq5kqf9phTI+G6Krf5qtpVvzJFma/OR2BRuqtSjP50TlAJC0CB9Ib91WskiWZHV0BoaapW4rWdyTFkp3/oXafaXS1l2SJLcrS64/X+hY6QCQrBh2QXqLWskiqXMFCytZACBuLNtOrBtU+P1+eb1eNTU1KS8vz+lykC5aQ9LPiqTuE0qz3dKqM1JmtnN1AUCS6M/3Nz0fgCSdrYsOHlL4+mydM/UAQAqLefhYvXq1LMuKOgoKCmL9a4DYOrEjfL57rvTEYWncnPD18e3O1QQAKSouE07vvfde7dmzJ3KdmZkZj18DxM642VJ+sVS8QLIsaeFr0tE3pCGsZAGAWItL+MjKyqK3A8ll1JTw0cmypImPOFcPAKSwuMz5OHnypAoLCzVmzBgtXLhQn3zyyQ1fGwwG5ff7ow4AAJC6Yh4+SktL9eqrr2rXrl361a9+pfPnz2vatGm6dOlSr6+vqqqS1+uNHD4f3dwAAKSyuC+1bW5u1p133qknn3xSK1eu7PF8MBhUMBiMXPv9fvl8PpbaAgCQRPqz1DbuO5wOHjxYxcXFOnnyZK/Pu1wuuVyueJcBAAASRNz3+QgGg/r44481YsSIeP8qAACQBGIePn784x9r3759OnXqlH77299qwYIF8vv9qqioiPWvAgAASSjmwy6fffaZvve97+nixYv62te+pilTpujDDz9UUVFRrH8VAABIQjEPH5s3b471WwIAgBTCvV0AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET5iIBBq1ehVNRq9qkaBUKvT5QAAkNAIHwAAwKgspwtIZp29HIFQW7e2rsfuHD5eAAC+jG/H2zD+2V092krW7Ik8Pr1+jslyAABICgy7AAAAo+j5uA31lbMkhYdaOns8ap+ZIXdOppNlAQCQ0Agft6G3OR3unEzmegAAcBMMuwAAAKP4Ez0G3DlZTC4FAKCP6PkAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPmLFtqVTB8JnAABwQ4SPWGnYI22aKzW863QlAAAkNMJHrNRXR58BAECv2GRsoNrbpdqXpOtXwtf127rOQ4vCjwcNkUoWSxlkPAAAOhE+BqqlWXpvrXTtsiRLsjoCRqhZ2rtWki3lDpUmLZRcHicrBQAgofAn+UC5PNKSA5KvNHxtt0WffaXS0oMEDwAAvoTwcTuG+KSKt6Xs3Oj27FxpUY3kHelMXQAAJLC0CR+BUKtGr6rR6FU1CoRaY/fGZ+uklkB0W0sg3A4AAHpIm/DRFwMKKCd2hM93z5WeOCyN67i77fHt8SkSAIAkl/ITTjtDRCDU1q2t67E75zY/gnGzpfxiqXiBZFnSwteko2+Eh2QAAEAPKR8+xj+7q0dbyZo9kcen18/5UkCxNSXjYwWC34m85qYBZdSU8NHJsqSJj9x23QAApKqUDx990T2gTM/4d72S85wqqlq0r32SpHBAAQAAsZHy4aO+cpakcK9GZ49H7TMz5M7J7PX15RkfdZx/GwkfAAAgdlI+fPQ2ZOLOyexqb2/Xif/UKAWb1NJmq/3AbyVJ/9ldp4en/t/waz76nJ1KAQCIkZQPH9HC8zlkl3U1tTQr58B66dplZctSmyxJktUSUM6+KrFTKQAAsZU2f8q7c7J0erFLm3PWyH1mX9cTUTuV2sqy2iVJFjuVAgAQF2kTPiTd+M6zHTuVWtnu6HZ2KgUAIOZSe9ilP3eevdlOpd2X0gIAgNuS2uGjP3ee7b5TadnfS7uekY7XhHcqJXwAABAzqR0+OudzbF0sNX70pTvPWuH5HAv+Jfw6dioFAMAIy7Zt2+kiuvP7/fJ6vWpqalJeXl5s3rQ1JP2sKHpYJdstrTojZWbH5ncAAJDG+vP9nR4TTrnzLAAACSM9wgd3ngUAIGGk9pyPTsznAAAgYaRH+ODOswAAJIz0GHYBAAAJg/ABAACMilv4eOGFFzRmzBgNGjRIkydP1oEDB+L1qwAAQBKJS/jYsmWLVqxYoaefflqHDx/Wt771LZWXl+vMmTPx+HUAACCJxGWTsdLSUt13333auHFjpO2ee+7R/PnzVVVVddOfjcsmYwAAIK4c3WQsFAqprq5OZWVlUe1lZWX64IMPYv3rAABAkon5UtuLFy+qra1N+fn5Ue35+fk6f/58j9cHg0EFg8HItd/vj3VJAAAggcRtwqllWVHXtm33aJOkqqoqeb3eyOHzsfEXAACpLObh44477lBmZmaPXo4LFy706A2RpKeeekpNTU2Ro7GxMdYlAQCABBLz8JGTk6PJkydr9+7dUe27d+/WtGnTerze5XIpLy8v6gAAAKkrLturr1y5Uo8//rhKSko0depUvfjiizpz5oyWLl16y5/tXHzD3A8AAJJH5/d2XxbRxiV8PProo7p06ZIqKyt17tw5TZgwQdu3b1dRUdEtf/bq1auSxNwPAACS0NWrV+X1em/6mrjs83E72tvb9fnnn8vj8fQ6QbU7v98vn8+nxsZGhmsM47N3Fp+/c/jsncXn75xbffa2bevq1asqLCxURsbNZ3Uk3F1tMzIyNHLkyH79DHNFnMNn7yw+f+fw2TuLz985N/vsb9Xj0YkbywEAAKMIHwAAwKikDh8ul0t/93d/J5fL5XQpaYfP3ll8/s7hs3cWn79zYvnZJ9yEUwAAkNqSuucDAAAkH8IHAAAwivABAACMInwAAACjkjZ8vPDCCxozZowGDRqkyZMn68CBA06XlBb279+vefPmqbCwUJZlqbq62umS0kZVVZW+8Y1vyOPxaPjw4Zo/f76OHz/udFlpY+PGjZo4cWJkg6WpU6dqx44dTpeVlqqqqmRZllasWOF0KWlh9erVsiwr6igoKLit90zK8LFlyxatWLFCTz/9tA4fPqxvfetbKi8v15kzZ5wuLeU1Nzdr0qRJ2rBhg9OlpJ19+/Zp2bJl+vDDD7V79261traqrKxMzc3NTpeWFkaOHKn169ertrZWtbW1+va3v62/+qu/0rFjx5wuLa0cOnRIL774oiZOnOh0KWnl3nvv1blz5yLH0aNHb+v9knKpbWlpqe677z5t3Lgx0nbPPfdo/vz5qqqqcrCy9GJZlt566y3Nnz/f6VLS0h//+EcNHz5c+/bt0wMPPOB0OWlp2LBh+od/+ActXrzY6VLSwhdffKH77rtPL7zwgtasWaM/+7M/0/PPP+90WSlv9erVqq6u1pEjR2L2nknX8xEKhVRXV6eysrKo9rKyMn3wwQcOVQWY19TUJCn8BQiz2tratHnzZjU3N2vq1KlOl5M2li1bpjlz5mjGjBlOl5J2Tp48qcLCQo0ZM0YLFy7UJ598clvvl3A3lruVixcvqq2tTfn5+VHt+fn5On/+vENVAWbZtq2VK1fq/vvv14QJE5wuJ20cPXpUU6dO1fXr1/WVr3xFb731lsaPH+90WWlh8+bN+t3vfqdDhw45XUraKS0t1auvvqq77rpLf/jDH7RmzRpNmzZNx44d01e/+tUBvWfShY9OlmVFXdu23aMNSFXLly/X73//ex08eNDpUtLKuHHjdOTIEV25ckVbt25VRUWF9u3bRwCJs8bGRv3oRz/SO++8o0GDBjldTtopLy+PPC4uLtbUqVN15513atOmTVq5cuWA3jPpwscdd9yhzMzMHr0cFy5c6NEbAqSiH/7wh/rNb36j/fv3a+TIkU6Xk1ZycnL09a9/XZJUUlKiQ4cO6R//8R/1T//0Tw5Xltrq6up04cIFTZ48OdLW1tam/fv3a8OGDQoGg8rMzHSwwvQyePBgFRcX6+TJkwN+j6Sb85GTk6PJkydr9+7dUe27d+/WtGnTHKoKiD/btrV8+XK9+eab2rt3r8aMGeN0SWnPtm0Fg0Gny0h53/nOd3T06FEdOXIkcpSUlOj73/++jhw5QvAwLBgM6uOPP9aIESMG/B5J1/MhSStXrtTjjz+ukpISTZ06VS+++KLOnDmjpUuXOl1ayvviiy/U0NAQuT516pSOHDmiYcOGadSoUQ5WlvqWLVum119/Xdu2bZPH44n0/nm9XuXm5jpcXer7yU9+ovLycvl8Pl29elWbN2/W+++/r507dzpdWsrzeDw95jYNHjxYX/3qV5nzZMCPf/xjzZs3T6NGjdKFCxe0Zs0a+f1+VVRUDPg9kzJ8PProo7p06ZIqKyt17tw5TZgwQdu3b1dRUZHTpaW82tpaPfTQQ5HrzvG+iooKvfLKKw5VlR46l5ZPnz49qv3ll1/WokWLzBeUZv7whz/o8ccf17lz5+T1ejVx4kTt3LlTM2fOdLo0IK4+++wzfe9739PFixf1ta99TVOmTNGHH354W9+5SbnPBwAASF5JN+cDAAAkN8IHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAo/4/rQYSTWshPSQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test,marker='+')\n",
    "plt.scatter(X_test,y_pred,marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE) on test data: 1.0773694513135212\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(f\"Root Mean Squared Error (RMSE) on test data: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.0001)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.0001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.0001)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import  linear_model\n",
    "alpha=0.0001\n",
    "lasso_reg=linear_model.Lasso(alpha, fit_intercept=True)\n",
    "lasso_reg.fit(X_train_poly, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9703145090809837"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(X_test_poly,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.8154748 , 17.27599639, 16.24928752, 22.51835731,  0.94427311,\n",
       "       16.12275416, 14.27252284, 17.49754834, 19.80380398,  8.40181973,\n",
       "        5.61536089,  8.87143831, 12.34069771, 18.03472926,  5.2722003 ,\n",
       "        8.05860481,  1.20221196,  1.59417448, 13.60217855, 16.55626834])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lasso=lasso_reg.predict(X_test_poly)\n",
    "y_pred_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE) on test data: 1.0841420949641418\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rmse_lasso = np.sqrt(mean_squared_error(y_test, y_pred_lasso))\n",
    "print(f\"Root Mean Squared Error (RMSE) on test data: {rmse_lasso}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbe0a746b90>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test,marker='+')\n",
    "plt.scatter(X_test,y_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "alphas = np.logspace(-4, 1, 50)\n",
    "rmse_lasso = []  # Initialize as an empty list\n",
    "\n",
    "for alpha in alphas:\n",
    "    lasso_reg = linear_model.Lasso(alpha=alpha, fit_intercept=True)\n",
    "    lasso_reg.fit(X_train_poly, y_train)\n",
    "    \n",
    "    y_pred_lasso = lasso_reg.predict(X_test_poly)\n",
    "    rmse_lasso.append(np.sqrt(mean_squared_error(y_test, y_pred_lasso)))\n",
    "\n",
    "# Plot the variation of RMSE on the test dataset versus alpha\n",
    "plt.plot(alphas, rmse_lasso, label='LASSO Regression')\n",
    "plt.axhline(y=rmse, color='r', linestyle='--', label='Linear Regression')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha (Regularization Coefficient)')\n",
    "plt.ylabel('RMSE on Test Dataset')\n",
    "plt.title('LASSO Regression with Varying Alpha')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}