Update cancer.py

cancer.py

@@ -50,7 +50,7 @@ def train_model(x_train, y_train, preprocess, model_name):
         ('preprocessor', preprocess),
         ('classifier', models[model_name])
     ])
-    pipeline.fit(X_train, y_train)
+    pipeline.fit(x_train, y_train)
     return pipeline
 
 # Streamlit UI
@@ -62,11 +62,11 @@ with st.sidebar:
     model_name = st.radio("Choose a Model", ['Decision Tree', 'Logistic Regression', 'KNN', 'Random Forest', 'XGBoost'])
     if st.button("Train Model"):
         df = load_data()
-        (X_train, X_test, y_train, y_test), preprocess = preprocess_data(df)
-        model = train_model(X_train, y_train, preprocess, model_name)
-        accuracy = model.score(X_test, y_test)
+        (x_train, x_test, y_train, y_test), preprocess = preprocess_data(df)
+        model = train_model(x_train, y_train, preprocess, model_name)
+        accuracy = model.score(x_test, y_test)
         st.session_state['trained_model'] = model
-        st.session_state['X_train'] = X_train
+        st.session_state['x_train'] = x_train
         st.success(f"Model Trained Successfully! Accuracy: {accuracy:.2f}")
 
 st.title("🎗️ Cancer Prediction")
@@ -94,8 +94,8 @@ input_data = [[age, tumor_size, tumor_grade, symptoms_severity, smoking_history,
 if st.button("Predict Cancer Presence"):
     if 'trained_model' in st.session_state:
         model = st.session_state['trained_model']
-        X_train = st.session_state['X_train']
-        input_df = pd.DataFrame(input_data, columns=X_train.columns)
+        x_train = st.session_state['x_train']
+        input_df = pd.DataFrame(input_data, columns=x_train.columns)
         input_transformed = model.named_steps['preprocessor'].transform(input_df)
         prediction = model.named_steps['classifier'].predict(input_transformed)