Polynomial Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75c9fa55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "594af172",
   "metadata": {},
   "source": [
    "# Import Linear dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c58d4e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0.00000</td>\n",
       "      <td>4.447692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02004</td>\n",
       "      <td>5.024612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04008</td>\n",
       "      <td>5.869593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06012</td>\n",
       "      <td>4.722490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.08016</td>\n",
       "      <td>4.877101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x         y\n",
       "0  0.00000  4.447692\n",
       "1  0.02004  5.024612\n",
       "2  0.04008  5.869593\n",
       "3  0.06012  4.722490\n",
       "4  0.08016  4.877101"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('linear_data.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "539b8c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_linear = df1[['x']]\n",
    "y_linear = df1[['y']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5735a4",
   "metadata": {},
   "source": [
    "# Import Non-linear dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bda70cbc",
   "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>0.00000</td>\n",
       "      <td>-0.155043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02004</td>\n",
       "      <td>0.081809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04008</td>\n",
       "      <td>-0.014298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06012</td>\n",
       "      <td>0.143978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.08016</td>\n",
       "      <td>0.019553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x         y\n",
       "0  0.00000 -0.155043\n",
       "1  0.02004  0.081809\n",
       "2  0.04008 -0.014298\n",
       "3  0.06012  0.143978\n",
       "4  0.08016  0.019553"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('nonlinear_data.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d27b47f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_nonlinear = df2[['x']]\n",
    "y_nonlinear = df2[['y']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b804454",
   "metadata": {},
   "source": [
    "# Visual Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "226fbba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#linear\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(x_linear, y_linear, color='blue', label='Data with Linear Relationship')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Linear Relationship')\n",
    "plt.legend()\n",
    "\n",
    "#non-linear\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(x_nonlinear, y_nonlinear, color='green', label='Data with Non-linear Relationship')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Non-linear Relationship')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "470f987a",
   "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>x</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.99644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.99644</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x        y\n",
       "x  1.00000  0.99644\n",
       "y  0.99644  1.00000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr() #linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "782a85e0",
   "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>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>-0.065736</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "x  1.000000 -0.065736\n",
       "y -0.065736  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.corr() #non linear"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97672c50",
   "metadata": {},
   "source": [
    "# Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "925c27bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f5ebc1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6051be8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.fit(x_linear, y_linear) # x= x_linear.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd50f6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9928923730526016"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.score(x_linear , y_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "024739b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ba5deb50a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_linear, y_linear, color='blue', label='Data with Linear Relationship')\n",
    "plt.plot(x_linear, reg1.predict(x_linear), color='red', label='Best fit line')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Linear Relationship')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689762d2",
   "metadata": {},
   "source": [
    "# Non Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d45b7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f8bb601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.fit(x_nonlinear , y_nonlinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "028e7c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004321199719043767"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2.score(x_nonlinear, y_nonlinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "071fdea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ba5e6aebe0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_nonlinear, y_nonlinear, color='green', label='Data with Non-linear Relationship')\n",
    "plt.plot(x_nonlinear, reg2.predict(x_nonlinear), color='red', label='Best fit line')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Non-linear Relationship')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226be43c",
   "metadata": {},
   "source": [
    "# Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1f27ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "331d9736",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=6) # polynomial regression with degree 2 \n",
    "X_poly = poly.fit_transform(x_nonlinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0554a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 7)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_poly.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c6844c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_poly = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c6ed790c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_poly.fit(X_poly, y_nonlinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e6e2cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9930624844497347"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_poly.score(X_poly, y_nonlinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e71d6181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ba5e6d9970>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_nonlinear, y_nonlinear, color='green', label='Data with Non-linear Relationship')\n",
    "plt.plot(x_nonlinear, 2 * np.sin(x_nonlinear), color='orange', label='True Non-linear Relationship')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Non-linear Relationship')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c7d6bf",
   "metadata": {},
   "source": [
    "# Seperate Train Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b2b7c48e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(X_poly,y_nonlinear, test_size=.30, random_state=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b628f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b8ecd4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#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>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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e71f51a",
   "metadata": {},
   "source": [
    "# testing score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "412da579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9905664282629798"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babeb5ef",
   "metadata": {},
   "source": [
    "# training score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fdabd40f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9939293669434816"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(xtrain, ytrain) "
   ]
  }
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