feature_preprocess.py

#Subset the dataframe to only include the features and labels that we're going to use 
#in the model
peak_demand_hour_model=peak_demand_hour_df[['Peak_Demand_Hour',
                                                      'Day_Of_Week', 
                                                      'Week',
                                                      'Month']]
#Convert the Week, Year, and Peak_Demand_Your variables into categorical string variables (from numeric)
peak_demand_hour_model.loc[:,'Week']=peak_demand_hour_model['Week'].apply(str)
peak_demand_hour_model.loc[:,'Peak_Demand_Hour']='Hour '+peak_demand_hour_model['Peak_Demand_Hour'].apply(str)
 
#Remove the labels from the features
features= peak_demand_hour_model.drop('Peak_Demand_Hour', axis = 1)
#One hot encode the categorical features
features = pd.get_dummies(features)
#Create labels 
labels = np.array(peak_demand_hour_model['Peak_Demand_Hour'])