Support_vector_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e362beb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "936afa54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Position_Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16f440ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,1:-1].values\n",
    "y = df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4756c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aaaa97d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3ad7ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=y.reshape(len(y),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fcac7efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  45000],\n",
       "       [  50000],\n",
       "       [  60000],\n",
       "       [  80000],\n",
       "       [ 110000],\n",
       "       [ 150000],\n",
       "       [ 200000],\n",
       "       [ 300000],\n",
       "       [ 500000],\n",
       "       [1000000]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e58ce4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "x = sc_x.fit_transform(x)\n",
    "y = sc_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cec4c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-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 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30f4957c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-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]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60c6684b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training the SVR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b92977a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bsingh\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: 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": [
       "SVR()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "reg = SVR(kernel='rbf')\n",
    "reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76c11aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting a new result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f3d6c40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a = reg.predict(sc_x.transform([[6.5]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6265ca64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[131106.77303274]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reverse the scaling\n",
    "sc_y.inverse_transform([a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ab5057c2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[-0.6197853  -0.62924694 -0.56788994 -0.49732233 -0.44690338 -0.36617206\n -0.14115569  0.28557658  0.78226531  1.08559794].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [29]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Visualizing the Results\u001b[39;00m\n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(sc_x\u001b[38;5;241m.\u001b[39minverse_transform(x),sc_y\u001b[38;5;241m.\u001b[39minverse_transform(y),color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(sc_x\u001b[38;5;241m.\u001b[39minverse_transform(x),\u001b[43msc_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m, color \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSupport Vector Regression\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPosition\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_data.py:1016\u001b[0m, in \u001b[0;36mStandardScaler.inverse_transform\u001b[1;34m(self, X, copy)\u001b[0m\n\u001b[0;32m   1013\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m   1015\u001b[0m copy \u001b[38;5;241m=\u001b[39m copy \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy\n\u001b[1;32m-> 1016\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1017\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1018\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1019\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1020\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1021\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1022\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1023\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1025\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sparse\u001b[38;5;241m.\u001b[39missparse(X):\n\u001b[0;32m   1026\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwith_mean:\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:769\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    767\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[0;32m    768\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 769\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    770\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    771\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    772\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    773\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    774\u001b[0m         )\n\u001b[0;32m    776\u001b[0m \u001b[38;5;66;03m# make sure we actually converted to numeric:\u001b[39;00m\n\u001b[0;32m    777\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[-0.6197853  -0.62924694 -0.56788994 -0.49732233 -0.44690338 -0.36617206\n -0.14115569  0.28557658  0.78226531  1.08559794].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    },
    {
     "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": [
    "#Visualizing the Results\n",
    "\n",
    "plt.scatter(sc_x.inverse_transform(x),sc_y.inverse_transform(y),color='red')\n",
    "plt.plot(sc_x.inverse_transform(x),sc_y.inverse_transform(reg.predict(x)), color = 'blue')\n",
    "plt.title('Support Vector Regression')\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec3222c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}