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| import streamlit as st | |
| import pandas as pd | |
| from sklearn.svm import SVR | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import mean_squared_error, r2_score | |
| import matplotlib.pyplot as plt | |
| # --- Function to perform SVM Regression and display results --- | |
| def perform_svm_regression(df, problem_description, interpretation_text): | |
| st.write(problem_description) | |
| st.write("**Dataset:**") | |
| st.write(df) | |
| # Hyperparameters with unique keys | |
| kernel = st.selectbox("Kernel:", ("linear", "rbf", "poly"), key=f"kernel_{problem_description}") | |
| C = st.slider("C (Regularization parameter):", 0.1, 10.0, 1.0, 0.1, key=f"C_{problem_description}") | |
| epsilon = st.slider("Epsilon (Tolerance for errors):", 0.01, 1.0, 0.1, 0.01, key=f"epsilon_{problem_description}") | |
| # Train-test split | |
| X = df.iloc[:,:-1] | |
| y = df.iloc[:, -1] | |
| X_train, X_test, y_train, y_test = train_test_split( | |
| X, y, test_size=0.2, random_state=42 | |
| ) | |
| # Model training | |
| model = SVR(kernel=kernel, C=C, epsilon=epsilon) | |
| model.fit(X_train, y_train) | |
| # Predictions | |
| y_pred = model.predict(X_test) | |
| # Evaluation metrics | |
| mse = mean_squared_error(y_test, y_pred) | |
| r2 = r2_score(y_test, y_pred) | |
| st.write(f"Mean Squared Error: {mse:.2f}") | |
| st.write(f"R-squared: {r2:.2f}") | |
| # Visualization | |
| fig, ax = plt.subplots() | |
| ax.scatter(y_test, y_pred) | |
| ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=2) | |
| ax.set_xlabel("Actual") | |
| ax.set_ylabel("Predicted") | |
| st.pyplot(fig) | |
| # Interpretation | |
| st.write("**Interpretation:**") | |
| st.write(interpretation_text) | |
| def main(): | |
| # --- App --- | |
| st.title("SVM Regressor Demo") | |
| about = """This app demonstrates the use of Support Vector Machine (SVM) regression | |
| for different problems: Business, Engineering, and Education. Select a problem to | |
| view the dataset, train an SVM model, and interpret the results. Explore the | |
| impact of hyperparameter tuning and visualize the predictions. | |
| Created by Louie F. Cervantes, M.Eng. (Information Engineering)""" | |
| with st.expander("About this App"): | |
| st.markdown(about) | |
| # Tabs for each problem | |
| tab1, tab2, tab3 = st.tabs( | |
| ["Business Problem", "Engineering Problem", "Education Problem"] | |
| ) | |
| # --- Business Problem --- | |
| with tab1: | |
| df = pd.read_csv("business_data.csv") | |
| perform_svm_regression( | |
| df, | |
| "**Business Problem:** Predicting customer churn based on usage patterns and demographics.", | |
| "The model predicts customer churn rate based on usage patterns and demographics. " | |
| "This information can be used to identify customers at risk of churning and take proactive steps to retain them.", | |
| ) | |
| # --- Engineering Problem --- | |
| with tab2: | |
| df = pd.read_csv("engineering_data.csv") | |
| perform_svm_regression( | |
| df, | |
| "**Engineering Problem:** Predicting the remaining useful life of an industrial machine based on sensor data.", | |
| "The model predicts the remaining useful life of an industrial machine based on sensor data. " | |
| "This information can be used to schedule maintenance and prevent costly downtime.", | |
| ) | |
| # --- Education Problem --- | |
| with tab3: | |
| df = pd.read_csv("education_data.csv") | |
| perform_svm_regression( | |
| df, | |
| "**Education Problem:** Predicting student performance on a standardized test based on study habits and previous grades.", | |
| "The model predicts student performance on a standardized test based on study habits and previous grades. " | |
| "This information can be used to identify students who may need extra help and provide them with appropriate support.", | |
| ) | |
| if __name__ == "__main__": | |
| main() |