Update app.py

app.py

@@ -6,11 +6,12 @@ import matplotlib.pyplot as plt
 from sklearn.ensemble import RandomForestClassifier, VotingClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
-
+from sklearn.svm import SVC
 from sklearn.naive_bayes import GaussianNB
-
+from sklearn.neural_network import MLPClassifier
+from sklearn.ensemble import GradientBoostingClassifier
 from xgboost import XGBClassifier
-
+from lightgbm import LGBMClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
@@ -167,7 +168,7 @@ else:
 
 
 
-selected_models = st.multiselect("Choose Classifier Models", ('Random Forest', 'Naïve Bayes', 'Logistic Regression', 'Decision Tree',  'XGBoost',))
+selected_models = st.multiselect("Choose Classifier Models", ('Random Forest', 'Naïve Bayes', 'Logistic Regression', 'Decision Tree', 'Gradient Boosting', 'LightGBM', 'XGBoost', 'Multilayer Perceptron', 'Artificial Neural Network', 'Support Vector Machine'))
 st.write(''' ''')
 
 # Initialize an empty list to store the selected models
@@ -187,9 +188,14 @@ if 'Logistic Regression' in selected_models:
 if 'Decision Tree' in selected_models:
     models_to_run.append(DecisionTreeClassifier())
 
+if 'Gradient Boosting' in selected_models:
+    models_to_run.append(GradientBoostingClassifier())
 
+if 'Support Vector Machine' in selected_models:
+    models_to_run.append(SVC())
 
-
+if 'LightGBM' in selected_models:
+    models_to_run.append(LGBMClassifier())
 
 if 'XGBoost' in selected_models:
     models_to_run.append(XGBClassifier())