support_vector_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1]\n",
      " [ 2]\n",
      " [ 3]\n",
      " [ 4]\n",
      " [ 5]\n",
      " [ 6]\n",
      " [ 7]\n",
      " [ 8]\n",
      " [ 9]\n",
      " [10]]\n",
      "[[  45000]\n",
      " [  50000]\n",
      " [  60000]\n",
      " [  80000]\n",
      " [ 110000]\n",
      " [ 150000]\n",
      " [ 200000]\n",
      " [ 300000]\n",
      " [ 500000]\n",
      " [1000000]]\n",
      "            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",
      "[[-1.5666989 ]\n",
      " [-1.21854359]\n",
      " [-0.87038828]\n",
      " [-0.52223297]\n",
      " [-0.17407766]\n",
      " [ 0.17407766]\n",
      " [ 0.52223297]\n",
      " [ 0.87038828]\n",
      " [ 1.21854359]\n",
      " [ 1.5666989 ]]\n",
      "[[-0.72004253]\n",
      " [-0.70243757]\n",
      " [-0.66722767]\n",
      " [-0.59680786]\n",
      " [-0.49117815]\n",
      " [-0.35033854]\n",
      " [-0.17428902]\n",
      " [ 0.17781001]\n",
      " [ 0.88200808]\n",
      " [ 2.64250325]]\n",
      "prediction \n",
      "[170370.0204065]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu007/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/ubuntu007/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'svr model')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "x = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2:].values\n",
    "print(x)\n",
    "print(y)\n",
    "print(dataset)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x = StandardScaler()\n",
    "x = sc_x.fit_transform(x)\n",
    "print(x)\n",
    "sc_y = StandardScaler()\n",
    "y = sc_y.fit_transform(y)\n",
    "print(y)\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "regressor = SVR(kernel='rbf')\n",
    "regressor.fit(x,y)\n",
    "\n",
    "y_pred = sc_y.inverse_transform(\n",
    "    regressor.predict(sc_x.transform(np.array([[6.5]]))))\n",
    "print('prediction ')\n",
    "print(y_pred)\n",
    "\n",
    "x_grid = np.arange(min(x), max(x), 0.1)\n",
    "x_grid = x_grid.reshape((len(x_grid), 1))\n",
    "\n",
    "plt.scatter(x,y,color='red')\n",
    "plt.plot(x_grid,regressor.predict(x_grid), color='blue')\n",
    "\n",
    "plt.xlabel('position')\n",
    "plt.ylabel('salary')\n",
    "plt.title('svr model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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