Update app.py

app.py

@@ -6,13 +6,13 @@ import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from sklearn.impute import SimpleImputer
 from sklearn.preprocessing import LabelEncoder, StandardScaler
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
-from sklearn.linear_model import LogisticRegression, LinearRegression, Ridge
-from sklearn.svm import SVC, SVR
-from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
-from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
 from sklearn.naive_bayes import GaussianNB
-from sklearn.metrics import classification_report, accuracy_score, mean_squared_error
+from sklearn.metrics import accuracy_score, classification_report
 from scipy import stats
 
 # File uploader
@@ -90,7 +90,7 @@ if uploaded_file is not None:
             # Store results in a dictionary
             results = []
 
-            # Model Selection and Evaluation
+            # Model Selection and Evaluation (For Classification)
             if is_classification:
                 model_choices = [
                     ("Random Forest", RandomForestClassifier(n_estimators=50)),
@@ -105,32 +105,18 @@ if uploaded_file is not None:
                     model.fit(X_train, y_train)
                     y_pred = model.predict(X_test)
                     accuracy = accuracy_score(y_test, y_pred)
-                    results.append([name, accuracy, None])
+                    results.append([name, accuracy])
                 
-            else:
-                model_choices = [
-                    ("Random Forest", RandomForestRegressor(n_estimators=50)),
-                    ("Linear Regression", LinearRegression()),
-                    ("SVR", SVR()),
-                    ("K-Nearest Neighbors", KNeighborsRegressor(n_neighbors=5)),
-                    ("Decision Tree", DecisionTreeRegressor()),
-                    ("Ridge Regression", Ridge())
-                ]
-                
-                for name, model in model_choices:
-                    model.fit(X_train, y_train)
-                    y_pred = model.predict(X_test)
-                    mse = mean_squared_error(y_test, y_pred)
-                    results.append([name, None, mse])
-            
-            # Display results in a table
-            st.subheader("Model Performance Results")
-            results_df = pd.DataFrame(results, columns=["Model", "Accuracy" if is_classification else "Accuracy (N/A)", "Mean Squared Error" if not is_classification else "MSE (N/A)"])
-            
-            # Bold the headers
-            st.markdown(f"**Model Performance Results**")
-            st.dataframe(results_df)
+                # Display results in a table
+                st.subheader("Model Performance Results")
+                results_df = pd.DataFrame(results, columns=["Model", "Accuracy"])
+                st.markdown(f"**Model Performance Results**")
+                st.dataframe(results_df)
             
+            # If it's not classification (i.e., regression)
+            else:
+                st.warning("Regression models are not implemented in this version. Please select a classification target.")
+
             # Option to download the model performance metrics (Results Table)
             st.download_button(
                 label="Download Model Report",