polynomial_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Position  Level   Salary\n",
      "0   Business Analyst      1    45000\n",
      "1  Junior Consultant      2    50000\n",
      "2  Senior Consultant      3    60000\n",
      "3            Manager      4    80000\n",
      "4    Country Manager      5   110000\n",
      "5     Region Manager      6   150000\n",
      "6            Partner      7   200000\n",
      "7     Senior Partner      8   300000\n",
      "8            C-level      9   500000\n",
      "9                CEO     10  1000000\n",
      "[[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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([158862.45265153])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "x = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:,2].values\n",
    "print(dataset)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(x,y)\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree=4)\n",
    "x_poly = poly_reg.fit_transform(x)\n",
    "print(x_poly)\n",
    "\n",
    "lin_reg2 = LinearRegression()\n",
    "lin_reg2.fit(x_poly, y)\n",
    "\n",
    "\n",
    "plt.scatter(x,y,color='red')\n",
    "plt.plot(x, lin_reg.predict(x), color='blue')\n",
    "plt.title('linear regression')\n",
    "plt.xlabel('position level')\n",
    "plt.ylabel('salary')\n",
    "plt.show()\n",
    "\n",
    "x_grid = np.arange(min(x), max(x), 0.1)\n",
    "x_grid = x_grid.reshape(len(x_grid), 1)\n",
    "plt.scatter(x,y,color='red')\n",
    "plt.plot(x_grid, lin_reg2.predict(poly_reg.fit_transform(x_grid)), color='blue')\n",
    "plt.title('polynomial regression')\n",
    "plt.xlabel('position level')\n",
    "plt.ylabel('salary')\n",
    "plt.show()\n",
    "\n",
    "lin_reg.predict(6.5)\n",
    "lin_reg2.predict(poly_reg.fit_transform(6.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}