diff --git a/src/back/managing_missing_values.py b/src/back/managing_missing_values.py
new file mode 100644
index 0000000..8360cf1
--- /dev/null
+++ b/src/back/managing_missing_values.py
@@ -0,0 +1,69 @@
+import pandas as pd
+from sklearn.impute import KNNImputer
+from sklearn.linear_model import LinearRegression
+import numpy as np
+import load_csv as l 
+
+def convert_categorical_to_numeric(data):
+    for column in data.columns:
+        if data[column].nunique() <= 15:
+            data[column] = data[column].astype('category')
+            data[column] = data[column].cat.codes.replace(-1, np.nan) + 1
+        else:
+            data = data.drop(column, axis=1)
+    return data
+
+def drop_high_null_percentage(data, threshold=0.5):
+    missing_percentage = data.isnull().mean()
+    data = data.loc[:, missing_percentage <= threshold]
+    return data
+
+
+def replace_with_mean(data):
+    return data.apply(lambda col: col.fillna(col.mean()) if col.dtype.kind in 'biufc' else col)
+
+def replace_with_median(data):
+    return data.apply(lambda col: col.fillna(col.median()) if col.dtype.kind in 'biufc' else col)
+
+def replace_with_mode(data):
+    return data.apply(lambda col: col.fillna(col.mode()[0]) if col.mode().size > 0 else col)
+
+def impute_with_knn(data, n_neighbors=5):
+    imputer = KNNImputer(n_neighbors=n_neighbors)
+    return pd.DataFrame(imputer.fit_transform(data), columns=data.columns)
+
+def impute_with_regression(data):
+    for column in data.columns:
+        if data[column].isnull().sum() > 0:
+            train_data = data[data[column].notna()]
+            test_data = data[data[column].isna()]
+            if not train_data.empty and not test_data.empty:
+                regressor = LinearRegression()
+                regressor.fit(train_data.drop(column, axis=1), train_data[column])
+                data.loc[data[column].isna(), column] = regressor.predict(test_data.drop(column, axis=1))
+    return data
+
+
+"""    
+    Parameters:
+    - data: Pandas DataFrame with the data
+    - method: Method to handle missing values ('drop', 'mean', 'median', 'mode', 'knn', 'regression')
+    - n_neighbors: Number of neighbors to use for KNN imputation (only used if method='knn')
+"""
+def handle_missing_values(data, method, n_neighbors=5):
+
+    data = drop_high_null_percentage(data)
+    data = convert_categorical_to_numeric(data)    
+    if method == 'mean':
+        return replace_with_mean(data)
+    elif method == 'median':
+        return replace_with_median(data)
+    elif method == 'mode':
+        return replace_with_mode(data)
+    elif method == 'knn':
+        return impute_with_knn(data, n_neighbors=n_neighbors)
+    elif method == 'regression':
+        return impute_with_regression(data)
+    else:
+        raise ValueError("Unknown method")
+