Create data_cleaning.py

data_cleaning.py

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+import pandas as pd
+import numpy as np
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler
+
+class DataCleaner:
+    def __init__(self):
+        self.imputer = SimpleImputer(strategy='mean')
+        self.scaler = StandardScaler()
+
+    def clean(self, data):
+        # Handle missing values
+        data = self.handle_missing_values(data)
+
+        # Remove outliers
+        data = self.remove_outliers(data)
+
+        # Normalize data
+        data = self.normalize_data(data)
+
+        return data
+
+    def handle_missing_values(self, data):
+        numeric_columns = data.select_dtypes(include=[np.number]).columns
+        data[numeric_columns] = self.imputer.fit_transform(data[numeric_columns])
+        return data
+
+    def remove_outliers(self, data):
+        numeric_columns = data.select_dtypes(include=[np.number]).columns
+        for column in numeric_columns:
+            Q1 = data[column].quantile(0.25)
+            Q3 = data[column].quantile(0.75)
+            IQR = Q3 - Q1
+            lower_bound = Q1 - 1.5 * IQR
+            upper_bound = Q3 + 1.5 * IQR
+            data = data[(data[column] >= lower_bound) & (data[column] <= upper_bound)]
+        return data
+
+    def normalize_data(self, data):
+        numeric_columns = data.select_dtypes(include=[np.number]).columns
+        data[numeric_columns] = self.scaler.fit_transform(data[numeric_columns])
+        return data