fin separation front/back

backend/classification_strategy.py

@@ -0,0 +1,45 @@
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+
+def perform_classification(data, data_name, target_name, test_size):
+    X = data[data_name]
+    y = data[target_name]
+
+    label_encoders = {}
+    for column in X.select_dtypes(include=['object']).columns:
+        le = LabelEncoder()
+        X[column] = le.fit_transform(X[column])
+        label_encoders[column] = le
+
+    if y.dtype == 'object':
+        le = LabelEncoder()
+        y = le.fit_transform(y)
+        label_encoders[target_name] = le
+    else:
+        if y.nunique() > 10:
+            raise ValueError("The target variable seems to be continuous. Please select a categorical target for classification.")
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
+
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+
+    return model, label_encoders, accuracy
+
+def make_prediction(model, label_encoders, data_name, target_name, input_values):
+    X_new = []
+    for feature, value in zip(data_name, input_values):
+        if feature in label_encoders:
+            value = label_encoders[feature].transform([value])[0]
+        X_new.append(value)
+    
+    prediction = model.predict([X_new])
+
+    if target_name in label_encoders:
+        prediction = label_encoders[target_name].inverse_transform(prediction)
+    
+    return prediction[0]