Polynomial Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed3f0adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression,SGDRegressor\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures,StandardScaler\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90b91a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = 6 * np.random.rand(200, 1) - 3\n",
    "y = 0.8 * X**2 + 0.9 * X + 2 + np.random.randn(200, 1)\n",
    "\n",
    "# y = 0.8x^2 + 0.9x + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67fa56de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.51771042])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3452508d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,\"b.\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "32a77861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f4199ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying linear regression\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "332da389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "669179da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21212062278810828"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6760bce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_train,lr.predict(X_train),color='r')\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a79597b",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly=PolynomialFeatures(degree=2)\n",
    "x_train_trans=poly.fit_transform(X_train)\n",
    "x_test_trans=poly.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b269c30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.31415794]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4a25fabd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.31415794 0.09869521]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_trans[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55e2a1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly=PolynomialFeatures(degree=2,include_bias=False)\n",
    "x_train_trans=poly.fit_transform(X_train)\n",
    "x_test_trans=poly.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2ce00340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.31415794]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b8819cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.31415794 0.09869521]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_trans[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b25388e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train_trans,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a0e2f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6699353708506182"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(x_test_trans)\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d40ee84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.00000000e+00, -1.35442419e+00, -1.50902465e-01,\n",
       "          9.44101997e+00, -2.37091978e+00, -1.31962334e+01,\n",
       "          8.61412638e+00,  9.19256802e+00, -8.69436025e+00,\n",
       "         -3.69684632e+00,  4.28557934e+00,  9.10861442e-01,\n",
       "         -1.19609421e+00, -1.39176360e-01,  1.98746872e-01,\n",
       "          1.28203243e-02, -1.95087557e-02, -6.50316780e-04,\n",
       "          1.04551401e-03,  1.39205244e-05, -2.36057696e-05]]),\n",
       " array([2.1311943]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_,lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0845f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new=np.linspace(-3, 3, 200).reshape(200, 1)\n",
    "X_new_poly = poly.transform(X_new)\n",
    "y_new = lr.predict(X_new_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "606b0ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new, y_new, \"r-\", linewidth=2, label=\"Predictions\")\n",
    "plt.plot(X_train, y_train, \"b.\",label='Training points')\n",
    "plt.plot(X_test, y_test, \"g.\",label='Testing points')\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba54cfd9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}