Update app.py

app.py

@@ -6,9 +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
 
@@ -185,9 +188,23 @@ if 'Logistic Regression' in selected_models:
 if 'Decision Tree' in selected_models:
     models_to_run.append(DecisionTreeClassifier())
 
+
+
+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())
 
+if 'Multilayer Perceptron' in selected_models:
+    models_to_run.append(MLPClassifier())
+
+if 'Artificial Neural Network' in selected_models:
+    models_to_run.append(MLPClassifier(hidden_layer_sizes=(100,), max_iter=100))
+
 
 user_input = np.array([age, bp, sg, al, sugar, rbc, pc, pcc, bac, bgr, bu, sc,
                        sod, pot, hemo, pcv, wbc, rbcc, htn, dm, cad, appet, pe, ane]).reshape(1, -1)
@@ -195,6 +212,21 @@ user_input = np.array([age, bp, sg, al, sugar, rbc, pc, pcc, bac, bgr, bu, sc,
 # import dataset
 def get_dataset():
     data = pd.read_csv('kidney.csv')
+
+    # Calculate the correlation matrix
+    # corr_matrix = data.corr()
+
+    # Create a heatmap of the correlation matrix
+    # plt.figure(figsize=(10, 8))
+    # sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
+    # plt.title('Correlation Matrix')
+    # plt.xticks(rotation=45)
+    # plt.yticks(rotation=0)
+    # plt.tight_layout()
+
+    # Display the heatmap in Streamlit
+    # st.pyplot()
+
     return data
 
 def generate_model_labels(model_names):