Update cancer.py

cancer.py

@@ -10,13 +10,10 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestClassifier
 from xgboost import XGBClassifier
-from sklearn.metrics import classification_report
 
 # Load dataset
 def load_data():
-    df = pd.read_csv('cancer_prediction_data (2).csv')
-    st.write("Class distribution:", df['Cancer_Present'].value_counts())
-    return df
+    return pd.read_csv('cancer_prediction_data (2).csv')
 
 # Data Preprocessing
 def preprocess_data(df):
@@ -46,11 +43,11 @@ def preprocess_data(df):
 # Train Model
 def train_model(x_train, y_train, preprocess, model_name):
     models = {
-        'Decision Tree': DecisionTreeClassifier(class_weight='balanced'),
-        'Logistic Regression': LogisticRegression(class_weight='balanced'),
+        'Decision Tree': DecisionTreeClassifier(),
+        'Logistic Regression': LogisticRegression(),
         'KNN': KNeighborsClassifier(),
-        'Random Forest': RandomForestClassifier(class_weight='balanced'),
-        'XGBoost': XGBClassifier(scale_pos_weight=y_train.value_counts()[0] / y_train.value_counts()[1])
+        'Random Forest': RandomForestClassifier(),
+        'XGBoost': XGBClassifier()
     }
     pipeline = Pipeline([
         ('preprocessor', preprocess),
@@ -69,15 +66,10 @@ with st.sidebar:
         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)
-        
-        # Evaluate the model
-        y_pred = model.predict(x_test)
-        report = classification_report(y_test, y_pred, output_dict=True)
-        accuracy = report['accuracy']
+        accuracy = model.score(x_test, y_test)
         st.session_state['trained_model'] = model
         st.session_state['x_train'] = x_train
         st.success(f"Model Trained Successfully! Accuracy: {accuracy:.2f}")
-        st.write("Classification Report:", report)
 
 st.title("🎗️ Cancer Prediction")