Create data_cleaner.py

projects/DAPIO/data-processing/preprocessing/data_cleaner.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+class DataCleaner:
+    def __init__(self, data: pd.DataFrame):
+        self.data = data
+
+    def handle_missing_values(self) -> pd.DataFrame:
+        self.data.fillna(self.data.mean(), inplace=True)
+        return self.data
+
+    def remove_outliers(self) -> pd.DataFrame:
+        Q1 = self.data.quantile(0.25)
+        Q3 = self.data.quantile(0.75)
+        IQR = Q3 - Q1
+        self.data = self.data[~((self.data < (Q1 - 1.5 * IQR)) | (self.data > (Q3 + 1.5 * IQR)))]
+        return self.data
+
+    def scale_data(self) -> pd.DataFrame:
+        scaler = StandardScaler()
+        self.data[['feature1', 'feature2', 'feature3']] = scaler.fit_transform(self.data[['feature1', 'feature2', 'feature3']])
+        return self.data
+
+    def preprocess_data(self) -> pd.DataFrame:
+        self.data = self.handle_missing_values()
+        self.data = self.remove_outliers()
+        self.data = self.scale_data()
+        return self.data