main.py

## Created by RayLi

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#to scale the data using z-score
from sklearn.preprocessing import StandardScaler

#to split the dataset
from sklearn.model_selection import train_test_split

#Metrics to evaluate the model
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score

#to ignore warnings
import warnings
warnings.filterwarnings("ignore")
from src.data.make_dataset import load_and_preprocess_data
from src.feature_engineering.build_features import create_dummy_vars
from src.models.train_models import deep_learning_layers
from src.models.train_models import deep_learning
from src.models.train_models import find_best_deep_learning_rate



#from src.models.predict_model import NN_evaluate_model
#from src.visulization.visulize import loss_curve

if __name__ == "__main__":
    # Load and preprocess the data
    
    df = pd.read_csv('src/data/raw/employee_attrition.csv')
    y= df.Attrition
    X= df.drop(columns = ['Attrition'])

    

    # Train the logistic regression model
    #deep_learning(X,y)
    find_best_deep_learning_rate(X,y)