Regularaization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_breast_cancer\n",
      "load_diabetes\n",
      "load_digits\n",
      "load_files\n",
      "load_iris\n",
      "load_linnerud\n",
      "load_sample_image\n",
      "load_sample_images\n",
      "load_svmlight_file\n",
      "load_svmlight_files\n",
      "load_wine\n"
     ]
    }
   ],
   "source": [
    "# Checking all available datasets in sklearn\n",
    "for data in dir(datasets):\n",
    "     if data.startswith(\"load\"):\n",
    "        print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = datasets.make_regression(n_samples = 20 , n_features = 5 , n_informative = 3 , n_targets = 1 , random_state = 42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.10633497, -0.47917424,  0.81252582, -1.19620662, -0.18565898],\n",
       "       [-0.29900735,  0.8219025 , -1.98756891,  0.09176078,  0.08704707],\n",
       "       [-0.676922  ,  0.32408397,  1.03099952,  0.61167629, -0.38508228],\n",
       "       [ 0.32875111, -0.50175704,  0.51326743, -0.5297602 ,  0.91540212],\n",
       "       [ 0.37569802,  0.11092259, -0.29169375, -0.60063869, -1.15099358],\n",
       "       [-1.95967012, -1.22084365,  0.19686124, -1.32818605,  0.2088636 ],\n",
       "       [ 0.0675282 ,  1.46564877, -0.54438272, -1.42474819, -0.2257763 ],\n",
       "       [-0.70205309,  0.09707755, -0.39210815, -0.32766215,  0.96864499],\n",
       "       [ 0.24196227, -0.46341769, -1.72491783, -1.91328024, -0.46572975],\n",
       "       [ 0.76743473, -0.23413696,  0.54256004, -0.46947439,  1.57921282],\n",
       "       [-0.01349722, -0.60170661,  0.82254491, -1.05771093,  1.85227818],\n",
       "       [ 0.26105527, -1.46351495, -0.23458713,  0.00511346,  0.29612028],\n",
       "       [ 0.31424733, -0.56228753, -1.4123037 , -0.90802408, -1.01283112],\n",
       "       [ 1.05712223, -0.71984421, -1.76304016,  0.34361829, -0.46063877],\n",
       "       [ 0.64768854,  0.49671415, -0.23415337,  1.52302986, -0.1382643 ],\n",
       "       [ 1.0035329 ,  1.35624003, -0.64511975,  0.36163603, -0.07201012],\n",
       "       [ 1.47789404, -0.21967189, -0.8084936 , -0.51827022,  0.35711257],\n",
       "       [-0.11564828,  0.73846658, -1.47852199, -0.3011037 ,  0.17136828],\n",
       "       [-0.03582604,  0.36139561, -2.6197451 ,  1.56464366,  1.53803657],\n",
       "       [-0.30921238,  0.93128012,  0.97554513,  0.33126343, -0.83921752]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-37.38601562,  32.65643567,  -3.13930716,   3.32849217,\n",
       "       -16.43111729, -69.80999161,  56.97255362,  17.15130032,\n",
       "       -26.78749633,  34.50130044,  15.86286415, -51.38251302,\n",
       "       -42.19953322, -27.6070018 ,  25.42597962,  67.04043398,\n",
       "        16.47723408,  33.24440858,  48.81661351,  16.52659471])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size = 0.2 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.24196227, -0.46341769, -1.72491783, -1.91328024, -0.46572975],\n",
       "       [-1.95967012, -1.22084365,  0.19686124, -1.32818605,  0.2088636 ],\n",
       "       [ 0.26105527, -1.46351495, -0.23458713,  0.00511346,  0.29612028],\n",
       "       [ 0.32875111, -0.50175704,  0.51326743, -0.5297602 ,  0.91540212],\n",
       "       [-0.03582604,  0.36139561, -2.6197451 ,  1.56464366,  1.53803657],\n",
       "       [ 1.47789404, -0.21967189, -0.8084936 , -0.51827022,  0.35711257],\n",
       "       [ 1.05712223, -0.71984421, -1.76304016,  0.34361829, -0.46063877],\n",
       "       [-0.676922  ,  0.32408397,  1.03099952,  0.61167629, -0.38508228],\n",
       "       [ 0.76743473, -0.23413696,  0.54256004, -0.46947439,  1.57921282],\n",
       "       [-0.30921238,  0.93128012,  0.97554513,  0.33126343, -0.83921752],\n",
       "       [ 0.37569802,  0.11092259, -0.29169375, -0.60063869, -1.15099358],\n",
       "       [ 0.31424733, -0.56228753, -1.4123037 , -0.90802408, -1.01283112],\n",
       "       [-0.70205309,  0.09707755, -0.39210815, -0.32766215,  0.96864499],\n",
       "       [-0.01349722, -0.60170661,  0.82254491, -1.05771093,  1.85227818],\n",
       "       [ 0.64768854,  0.49671415, -0.23415337,  1.52302986, -0.1382643 ],\n",
       "       [ 0.0675282 ,  1.46564877, -0.54438272, -1.42474819, -0.2257763 ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.10633497, -0.47917424,  0.81252582, -1.19620662, -0.18565898],\n",
       "       [-0.11564828,  0.73846658, -1.47852199, -0.3011037 ,  0.17136828],\n",
       "       [ 1.0035329 ,  1.35624003, -0.64511975,  0.36163603, -0.07201012],\n",
       "       [-0.29900735,  0.8219025 , -1.98756891,  0.09176078,  0.08704707]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-26.78749633, -69.80999161, -51.38251302,   3.32849217,\n",
       "        48.81661351,  16.47723408, -27.6070018 ,  -3.13930716,\n",
       "        34.50130044,  16.52659471, -16.43111729, -42.19953322,\n",
       "        17.15130032,  15.86286415,  25.42597962,  56.97255362])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-37.38601562,  33.24440858,  67.04043398,  32.65643567])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = lr.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([-37.38601562,  33.24440858,  67.04043398,  32.65643567])"
      ]
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    "ypred"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displaying the regression line\n",
    "plt.plot(xtest,ypred,color = 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using ridge and lasso\n",
    "from sklearn.linear_model import Ridge , Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmodel = Lasso()\n",
    "lmodel.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmodel = Ridge()\n",
    "rmodel.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9822010721661684"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmodel.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9943749364100763"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lmodel.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rypred = rmodel.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lypred = lmodel.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-37.38601562,  33.24440858,  67.04043398,  32.65643567])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-37.38601562,  33.24440858,  67.04043398,  32.65643567])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-34.18125096,  28.68057599,  59.68797944,  28.44465534])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-34.06689983,  31.79121928,  62.80313925,  31.45814313])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ridge curve\n",
    "plt.plot(xtest,rypred,color = 'g')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasso curve\n",
    "plt.plot(xtest,lypred,color = 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the ridge model is score is 0.9999976783938865 while the alpha value is 0.01 and the error array is [ 0.03562357 -0.0529091  -0.08350739 -0.04877973]\n",
      "the lasso model is score is 0.9999994375031381 while the alpha value is 0.01 and the error array is [ 0.03318626 -0.01453592 -0.04237328 -0.01198812]\n",
      "the ridge model is score is 0.9999907403588116 while the alpha value is 0.02 and the error array is [ 0.07116587 -0.1056482  -0.16678441 -0.09740342]\n",
      "the lasso model is score is 0.999997750050292 while the alpha value is 0.02 and the error array is [ 0.06633211 -0.02910721 -0.08475195 -0.02402154]\n",
      "the ridge model is score is 0.9999792258002949 while the alpha value is 0.03 and the error array is [ 0.10662722 -0.15821813 -0.24983209 -0.14587183]\n",
      "the lasso model is score is 0.9999949376441584 while the alpha value is 0.03 and the error array is [ 0.0995312  -0.04363099 -0.12712239 -0.03599424]\n",
      "the ridge model is score is 0.9999631742675877 while the alpha value is 0.04 and the error array is [ 0.14200791 -0.21061969 -0.33265142 -0.19418573]\n",
      "the lasso model is score is 0.9999910001328697 while the alpha value is 0.04 and the error array is [ 0.13272676 -0.05815926 -0.16949504 -0.04797249]\n",
      "the ridge model is score is 0.9999426249572687 while the alpha value is 0.05 and the error array is [ 0.17730827 -0.26285369 -0.41524339 -0.24234586]\n",
      "the lasso model is score is 0.9999859375916574 while the alpha value is 0.05 and the error array is [ 0.16592233 -0.07268752 -0.21186768 -0.05995073]\n",
      "the ridge model is score is 0.9999176167167971 while the alpha value is 0.06 and the error array is [ 0.2125286  -0.31492094 -0.49760899 -0.29035296]\n",
      "the lasso model is score is 0.9999797500205214 while the alpha value is 0.06 and the error array is [ 0.1991179  -0.08721579 -0.25424033 -0.07192898]\n",
      "the ridge model is score is 0.9998881880480256 while the alpha value is 0.07 and the error array is [ 0.2476692  -0.36682222 -0.57974919 -0.33820778]\n",
      "the lasso model is score is 0.9999724374194617 while the alpha value is 0.07 and the error array is [ 0.23231347 -0.10174406 -0.29661298 -0.08390723]\n",
      "the ridge model is score is 0.9998543771106733 while the alpha value is 0.08 and the error array is [ 0.28273039 -0.41855834 -0.66166498 -0.38591105]\n",
      "the lasso model is score is 0.9999639997884784 while the alpha value is 0.08 and the error array is [ 0.26550903 -0.11627232 -0.33898562 -0.09588548]\n",
      "the ridge model is score is 0.9998162217257589 while the alpha value is 0.09 and the error array is [ 0.31771246 -0.47013007 -0.74335733 -0.43346351]\n",
      "the lasso model is score is 0.9999544371275714 while the alpha value is 0.09 and the error array is [ 0.2987046  -0.13080059 -0.38135827 -0.10786372]\n",
      "the ridge model is score is 0.9997737593789953 while the alpha value is 0.1 and the error array is [ 0.35261572 -0.5215382  -0.8248272  -0.48086587]\n",
      "the lasso model is score is 0.9999437494367407 while the alpha value is 0.1 and the error array is [ 0.33190017 -0.14532885 -0.42373092 -0.11984197]\n",
      "the ridge model is score is 0.999120266727726 while the alpha value is 0.2 and the error array is [ 0.69737865 -1.02678981 -1.62749745 -0.94679107]\n",
      "the lasso model is score is 0.9997749974082093 while the alpha value is 0.2 and the error array is [ 0.66382529 -0.29063604 -0.84745872 -0.23965617]\n",
      "the ridge model is score is 0.9980749469789836 while the alpha value is 0.3 and the error array is [ 1.03457985 -1.51650538 -2.408939   -1.39847284]\n",
      "the lasso model is score is 0.9994937438855456 while the alpha value is 0.3 and the error array is [ 0.99574245 -0.43595037 -1.27118781 -0.35947949]\n"
     ]
    }
   ],
   "source": [
    "# changing alpha value for ridge and lasso\n",
    "alpha_lst = [0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.2,0.3]\n",
    "for i in alpha_lst:\n",
    "    r1model = Ridge(alpha = i)\n",
    "    l1model = Lasso(alpha = i)\n",
    "    ridge_model = r1model.fit(xtrain,ytrain)\n",
    "    lasso_model = l1model.fit(xtrain,ytrain)\n",
    "    rmpred = r1model.predict(xtest)\n",
    "    lmpred = l1model.predict(xtest)\n",
    "    print(f\"the ridge model is score is {r1model.score(xtest,ytest)} while the alpha value is {i} and the error array is {rmpred-ypred}\")\n",
    "    print(f\"the lasso model is score is {l1model.score(xtest,ytest)} while the alpha value is {i} and the error array is {lmpred-ypred}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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