Suport vector regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CPU Usage (%)</th>\n",
       "      <th>Memory Usage (%)</th>\n",
       "      <th>CPU Temperature (°C)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>74.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>73.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.1</td>\n",
       "      <td>33.6</td>\n",
       "      <td>73.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.3</td>\n",
       "      <td>33.7</td>\n",
       "      <td>73.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.8</td>\n",
       "      <td>33.7</td>\n",
       "      <td>73.598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   CPU Usage (%)  Memory Usage (%)  CPU Temperature (°C)\n",
       "0            1.5              33.3                74.136\n",
       "1            1.3              33.3                73.598\n",
       "2            5.1              33.6                73.598\n",
       "3           11.3              33.7                73.060\n",
       "4            5.8              33.7                73.598"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"temp_data1.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CPU Usage (%)           float64\n",
       "Memory Usage (%)        float64\n",
       "CPU Temperature (°C)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()\n",
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5, 33.3],\n",
       "       [ 1.3, 33.3],\n",
       "       [ 5.1, 33.6],\n",
       "       ...,\n",
       "       [ 2.5, 37.2],\n",
       "       [ 3. , 37.2],\n",
       "       [ 2.7, 37.2]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.iloc[:,:2].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([74.136, 73.598, 73.598, ..., 70.908, 71.446, 70.908])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dataset.iloc[:,-1].values\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  \n",
    "from sklearn.svm import SVR\n",
    "regressor = SVR(kernel = 'rbf')\n",
    "regressor.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[70.75438191 70.72679962 70.93826402 ... 70.74183004 70.93217728\n",
      " 70.7952219 ]\n",
      "Mean Absolute Error: 0.722619042653895\n",
      "Mean Squared Error: 0.9327575426024436\n",
      "R-squared: 0.020916138806374773\n"
     ]
    }
   ],
   "source": [
    "# Predicting a new result\n",
    "y_pred = regressor.predict(x_test)\n",
    "print(y_pred)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Assuming you have y_test and y_pred\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9327575426024436\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('svm_model3.pkl', 'wb') as f:\n",
    "    pickle.dump((regressor), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "source": []
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   "cell_type": "code",
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