Update CDC_ML_v1.py

Author: armixz

code/CDC_ML_v1.py

@@ -160,7 +160,6 @@ def univariate_numerical_statistic(dataframe, num_features):
  Args:
  dataframe (dataframe): cdc dataset
  """
- # call encode_age_category function to encode the values
  cat_col = list(num_features)
  # creat a list of univariate numreical feature as num_col
  print(dataframe.describe()[1:][cat_col])
@@ -172,7 +171,6 @@ def bivariate_categorical_graph(dataframe, cat_features):
  dataframe (dataframe): cdc dataset
  cat_features (list): list of all categorical features
  """
- # encode_age_category(dataframe)
  i = 1
  # creat an empty canvas size of 25*15
  plt.figure(figsize = (25,15))
@@ -422,20 +420,20 @@ def main():
  """
  df_cdc = load_dataset('G:\\My Drive\\CDC_ML\\2-Working\\dataset\\heart_2020_cleaned.csv')
  # unique_values(df_cdc)
- # print(numerical_features(df_cdc))
- # print(categorical_features(df_cdc)) 
- # univariate_categorical_graph(df_cdc, categorical_features(df_cdc))
- # univariate_numerical_graph(df_cdc, numerical_features(df_cdc))
- # univariate_numerical_statistic(df_cdc, numerical_features(df_cdc))
- # bivariate_categorical_graph(df_cdc, categorical_features(df_cdc))
- # bivariate_numerical_graph(df_cdc, numerical_features(df_cdc), 'DiffWalking')
- 
- df_cdc_encode = preprocessing_encode_columns(df_cdc)
- X_train, y_train, X_test, y_test = preprocessing_splitting(df_cdc_encode, 'HeartDisease')
- X_train_scale, X_test_scale = preprocessing_scaling(X_train, X_test) 
+
+ # print(numerical_features (df_cdc))
+ # print(categorical_features (df_cdc)) 
+ # univariate_categorical_graph (df_cdc, categorical_features(df_cdc))
+ # univariate_numerical_graph (df_cdc, numerical_features(df_cdc))
+ # univariate_numerical_statistic (df_cdc, numerical_features(df_cdc))
+ # bivariate_categorical_graph (df_cdc, categorical_features(df_cdc))
+ # bivariate_numerical_graph (df_cdc, numerical_features(df_cdc), 'DiffWalking')
 
- X_train_blnc, y_train_blnc = balance_data('smote', X_train_scale, y_train)
- cv = k_fold_cross_validation(10)
+ df_cdc_encode = preprocessing_encode_columns (df_cdc)
+ X_train, y_train, X_test, y_test = preprocessing_splitting (df_cdc_encode, 'HeartDisease')
+ X_train_scale, X_test_scale = preprocessing_scaling (X_train, X_test) 
+ X_train_blnc, y_train_blnc = balance_data ('smote', X_train_scale, y_train)
+ cv = k_fold_cross_validation (10)
 
  params= {
  "criterion" : ["gini", "entropy", "log_loss"],