Update utils/data_cleaning.py

utils/data_cleaning.py

@@ -7,14 +7,11 @@ def preprocess_data(df):
     Preprocess the dataset by handling missing values and performing any necessary cleanup.
     """
     # Example: Handling missing values by filling with column mean
-    if method == "Drop rows":
-        df = df.dropna()
-    elif method == "Fill with mean/median":
-        for col in df.columns:
-            if df[col].dtype in ['float64', 'int64']:
-                df[col].fillna(df[col].mean(), inplace=True)
-            else:
-                df[col].fillna(df[col].mode()[0], inplace=True)
+    for col in df.columns:
+        if df[col].dtype in ['float64', 'int64']:
+            df[col].fillna(df[col].mean(), inplace=True)
+        else:
+            df[col].fillna(df[col].mode()[0], inplace=True)
     return df
 
 def remove_outliers_iqr(df):