Machine Learning A-Z (Codes and Datasets)/Part 2 - Regression/Section 6 - Polynomial Regression/Python/polynomial_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vN99YjPTDena"
   },
   "source": [
    "# Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZIx_naXnDyHd"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6c8YExmOD5x5"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "X = dataset.iloc[:,1:-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Le8SEL-YEOLb"
   },
   "source": [
    "## Training the Linear Regression model on the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rb5nWuSHEfBV"
   },
   "source": [
    "## Training the Polynomial Regression model on the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00]\n",
      " [1.000e+00 2.000e+00 4.000e+00 8.000e+00 1.600e+01]\n",
      " [1.000e+00 3.000e+00 9.000e+00 2.700e+01 8.100e+01]\n",
      " [1.000e+00 4.000e+00 1.600e+01 6.400e+01 2.560e+02]\n",
      " [1.000e+00 5.000e+00 2.500e+01 1.250e+02 6.250e+02]\n",
      " [1.000e+00 6.000e+00 3.600e+01 2.160e+02 1.296e+03]\n",
      " [1.000e+00 7.000e+00 4.900e+01 3.430e+02 2.401e+03]\n",
      " [1.000e+00 8.000e+00 6.400e+01 5.120e+02 4.096e+03]\n",
      " [1.000e+00 9.000e+00 8.100e+01 7.290e+02 6.561e+03]\n",
      " [1.000e+00 1.000e+01 1.000e+02 1.000e+03 1.000e+04]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree = 4) # degree is number of features that are powed.\n",
    "X_poly = poly_reg.fit_transform(X)\n",
    "lin_reg_2 = LinearRegression()\n",
    "lin_reg_2.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0O8R0tzbEpvy"
   },
   "source": [
    "## Visualising the Linear Regression results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "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.scatter(X,y,color = 'red')\n",
    "plt.plot(X,lin_reg.predict(X),'b')\n",
    "plt.title('Linear Regression')\n",
    "plt.xlabel('Levels')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "stOnSo74E52m"
   },
   "source": [
    "## Visualising the Polynomial Regression results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.scatter(X,y,color='red')\n",
    "plt.plot(X,lin_reg_2.predict(X_poly),'b')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.xlabel('Level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U_qsAMKnE-PJ"
   },
   "source": [
    "## Visualising the Polynomial Regression results (for higher resolution and smoother curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": [
    "X_grid = np.arange(min(X), max(X), 0.1) # increase numbers of X axis.(not only integer)\n",
    "X_grid = X_grid.reshape((len(X_grid),1))\n",
    "plt.scatter(X,y,color='red')\n",
    "plt.plot(X_grid,lin_reg_2.predict(poly_reg.fit_transform(X_grid)),'b')\n",
    "plt.title('Polynomial Regression 3')\n",
    "plt.xlabel('Level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "diyJFZHhFFeK"
   },
   "source": [
    "## Predicting a new result with Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[330378.78787879]\n"
     ]
    }
   ],
   "source": [
    "#print(lin_reg.predict([[6.5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DW7I7ZVDFNkk"
   },
   "source": [
    "## Predicting a new result with Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[158862.45265153]\n"
     ]
    }
   ],
   "source": [
    "print(lin_reg_2.predict(poly_reg.fit_transform([[6.5]])))\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMLrtB0Kmy82otxgYKZqyGa",
   "collapsed_sections": [],
   "name": "polynomial_regression.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}