Polynomial Regression data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import PolynomialFeatures \n",
    "from sklearn.linear_model import LinearRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Temperature</th>\n",
       "      <th>Pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>0.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100</td>\n",
       "      <td>0.2700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Pressure\n",
       "0            0    0.0002\n",
       "1           20    0.0012\n",
       "2           40    0.0060\n",
       "3           60    0.0300\n",
       "4           80    0.0900\n",
       "5          100    0.2700"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the dataset \n",
    "datas = pd.read_csv('~/Downloads/Data Science/data set/data.csv') \n",
    "datas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(datas, columns = ['Temperature'])\n",
    "y = pd.DataFrame(datas, columns = ['Pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Linear Regression\n",
    "lin = LinearRegression() \n",
    "lin.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Polynomial Regression\n",
    "poly = PolynomialFeatures(degree = 4) \n",
    "X_poly = poly.fit_transform(X) \n",
    "\n",
    "poly.fit(X_poly, y) \n",
    "lin2 = LinearRegression() \n",
    "lin2.fit(X_poly, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "# Visualise Linear Regression results \n",
    "plt.scatter(X, y, color = 'blue') \n",
    "\n",
    "plt.plot(X, lin.predict(X), color = 'red') \n",
    "plt.title('Linear Regression') \n",
    "plt.xlabel('Temperature') \n",
    "plt.ylabel('Pressure') \n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "# Visualise Polynomial Regression results \n",
    "plt.scatter(X, y, color = 'blue') \n",
    "\n",
    "plt.plot(X, lin2.predict(poly.fit_transform(X)), color = 'red') \n",
    "plt.title('Polynomial Regression') \n",
    "plt.xlabel('Temperature') \n",
    "plt.ylabel('Pressure') \n",
    "\n",
    "plt.show() "
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}