I'm working with data(with 4 columns which are p(product), M(name of the store)), I want predict the demand of store for that I sued SVR on the data by theses formulation:
dfn = pd.get_dummies(df) x = dfn.drop(["demand"],axis=1) y = dfn.demand from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0,1)) dfn = scaler.fit_transform(dfn) . . . from sklearn.metrics import r2_score pred = regressor.predict(testX) SVM_R2 = print('r2= ' +str(r2_score(testY,pred))) print(pred) # array example is between 0 and 1 array = np.array(pred) #scaled from 200 to 800 minimo = 200 maximo = 800 output=array * minimo + (maximo - minimo) print(output) df2=pd.DataFrame(output) df2.to_excel(r'/content/Book1.xlsx', index = False)
and now I get the output of this prediction. My question is, how can I match these outputs to inputs, or how can I found which demands are related to each market?