SVMRADIAL(DONE)_but_kernal=linear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from math import sqrt\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Common seed value to be used whenever required\n",
    "seed = 79\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[\"T1\", \"T2\", \"T3\", \"T4\", \"T5\", \"T6\", \"T7\", \"T8\", \"T9\",\"RH_1\", \"RH_2\", \"RH_3\", \"RH_4\", \"RH_5\", \"RH_6\", \"RH_7\", \"RH_8\", \"RH_9\",\"T_out\", \"Tdewpoint\", \"RH_out\", \"Press_mm_hg\", \"Windspeed\", \"Visibility\",\"Appliances\"]\n",
    "#load the datasets\n",
    "energy = pd.read_csv(r\"C:\\Users\\chandan sharma\\Desktop\\research\\-------------MOST\\final project\\Data-driven-prediction-models-of-energy-use-of-appliances-in-a-low-energy-house-master\\dataset\\training.csv\",usecols=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "X_train = energy.drop(\"Appliances\", axis=1)\n",
    "y_train = energy[\"Appliances\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(r\"C:\\Users\\chandan sharma\\Desktop\\research\\-------------MOST\\final project\\Data-driven-prediction-models-of-energy-use-of-appliances-in-a-low-energy-house-master\\dataset\\testing_validation.csv\",usecols=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the features and the target variable\n",
    "X_test = test.drop(\"Appliances\", axis=1)\n",
    "y_test = test[\"Appliances\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import svm model\n",
    "from sklearn import svm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = svm.SVC(kernel='linear') # Linear Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model using the training sets\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.2466233116558279\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "# Model Accuracy: how often is the classifier correct?\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage error: 34.329856125447236\n",
      "Mean squared error: 13486.34\n",
      "R2 score: -0.09\n",
      "Mean absolute error:51.11\n",
      "MSE: 13486.343171585793\n",
      "RMSE: 116.1307158833777\n"
     ]
    }
   ],
   "source": [
    "print(\"percentage error:\",(np.mean(np.abs(y_test - y_pred) / y_test)*100))\n",
    "\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_test, y_pred))\n",
    "\n",
    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "print(\"Mean absolute error:%.2f\" % mean_absolute_error(y_test, y_pred))\n",
    "\n",
    "meanSquaredError=mean_squared_error(y_test, y_pred)\n",
    "print(\"MSE:\", meanSquaredError)\n",
    "rootMeanSquaredError = sqrt(meanSquaredError)\n",
    "print(\"RMSE:\", rootMeanSquaredError)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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