regression code.txt

#MLR: 

#loading data

X= df.drop(['load_shortfall_3h','time','Seville_pressure','Valencia_wind_deg','Unnamed: 0','Valencia_pressure'],axis=1)
y= df['load_shortfall_3h']

#Training model
linear_regression = LinearRegression()
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20,random_state=1)
linear_regression.fit(X_train,y_train)

#The parameters of the best fit line:
beta_0 = float(linear_regression.intercept_)
beta_js = pd.DataFrame(linear_regression.coef_, X.columns, columns=['Coefficient'])
print("Intercept:", beta_0)
beta_js

#SLR:
slr = LinearRegression()

slr.fit(X_train[['Madrid_temp_min']], y_train)
from sklearn import metrics
import math


#Results:
results_dict = {'Training MSE':
                    {
                        "SLR": metrics.mean_squared_error(y_train, slr.predict(X_train[['Madrid_temp_min']])),
                        "MLR": metrics.mean_squared_error(y_train, linear_regression.predict(X_train))
                    },
                'Test MSE':
                    {
                        "SLR": metrics.mean_squared_error(y_test, slr.predict(X_test[['Madrid_temp_min']])),
                        "MLR": metrics.mean_squared_error(y_test, linear_regression.predict(X_test))
                    },
                'Test RMSE':
                    {
                        "SLR": math.sqrt(metrics.mean_squared_error(y_test, slr.predict(X_test[['Madrid_temp_min']]))),
                        "MLR": math.sqrt(metrics.mean_squared_error(y_test, linear_regression.predict(X_test)))
                    }
                }
results_df = pd.DataFrame(data=results_dict)
round(results_df,1)