main.py

import munich_handler
import berlin_handler
import statistics.outliers as outliers
import statistics.corelation as corelation
from algorithms import regression_impl
from algorithms.neural_network import neural_networks_impl
from algorithms import xgboost_impl
from algorithms import adaboost_impl
from algorithms import random_forest_impl
from statistics import outliers, corelation
import utility.enums as enum


df_munich = munich_handler.get_munich_data()
#corelation.print_df_corelation(df_munich)
#outliers.get_outliers_statistics(df_munich)

#df_berlin = berlin_handler.get_berlin_dataset()
#outliers.get_outliers_statistics(df_berlin)

# model = regression_impl.train(df_munich, enum.RegressionType.LINEAR)
# xgboost_impl.train(df_munich, print_stats=True)
# adaboost_impl.train(df_munich, True, True)
# random_forest_impl.train(df_munich)
print('-------------------------------')
neural_networks_impl.train(df_munich, print_stats=True)
#xgboost_impl.train(df_berlin, True, True)
#adaboost_impl.train(df_berlin, True, True)
#random_forest_impl.train(df_berlin)