Update app.py

app.py

@@ -7,9 +7,10 @@ from sklearn.impute import SimpleImputer
 from sklearn.compose import ColumnTransformer
 from sklearn.model_selection import train_test_split
 from sklearn.tree import DecisionTreeClassifier
-from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.ensemble import RandomForestClassifier
+from xgboost import XGBClassifier
 
 # Load dataset
 def load_data():
@@ -44,9 +45,10 @@ def preprocess_data(df):
 def train_model(X_train, y_train, preprocess, model_name):
     models = {
         'Decision Tree': DecisionTreeClassifier(),
-        'SVM': SVC(),
         'Logistic Regression': LogisticRegression(),
-        'KNN': KNeighborsClassifier()
+        'KNN': KNeighborsClassifier(),
+        'Random Forest': RandomForestClassifier(),
+        'XGBoost': XGBClassifier()
     }
     pipeline = Pipeline([
         ('preprocessor', preprocess),
@@ -61,7 +63,7 @@ st.set_page_config(page_title='Cancer Prediction App', layout='wide')
 with st.sidebar:
     st.image('https://via.placeholder.com/300x150.png?text=Cancer+Prediction')
     st.markdown("### Select Machine Learning Model")
-    model_name = st.radio("Choose a Model", ['Decision Tree', 'SVM', 'Logistic Regression', 'KNN'])
+    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)