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| title: SVM Regressor | |
| emoji: 🤖 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: streamlit | |
| sdk_version: 1.41.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: App demonstrating the SVM Regressor | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # SVM Regressor Demo 🤖 | |
| This app demonstrates the use of Support Vector Machines (SVM) for regression problems in various domains. It provides a user-friendly interface to select a problem, visualize the dataset, train an SVM Regressor, and interpret the results. | |
| ## Features | |
| * **Problem Selection:** Choose from three example problems: Business 💼, Engineering ⚙️, or Education 🎓. | |
| * **Dataset Display:** View the corresponding dataset for the selected problem, along with a description of its features. | |
| * **Model Training and Performance:** Train an SVM Regressor on the selected dataset. Display the model's performance using metrics like R-squared and Mean Squared Error. Adjust hyperparameters to fine-tune the model. Visualize the regression results using a scatter plot with a regression line. | |
| * **Output Interpretation:** Get a clear explanation of the model's output in the context of the selected problem. Understand the significance of the results and how they can be used for predictions or decisions. | |
| ## Learning Objectives | |
| * Understand the concept of SVM Regression and its applications. | |
| * Learn how to train and evaluate SVM Regressors using Python. | |
| * Explore the impact of different hyperparameters on the model's performance. | |
| * Interpret and explain the results of SVM Regression in various domains. | |
| ## Datasets | |
| The app uses three synthetic datasets for demonstration purposes: | |
| * **Business 💼:** | |
| * **Features:** `Usage` (in hours), `Demographics` (binary) | |
| * **Target:** `Churn` (continuous) | |
| * **Description:** Predicts customer churn based on usage patterns and demographics. | |
| * **Engineering ⚙️:** | |
| * **Features:** `Sensor_Data` (continuous) | |
| * **Target:** `RUL` (Remaining Useful Life) (continuous) | |
| * **Description:** Predicts the remaining useful life of an industrial machine based on sensor data. | |
| * **Education 🎓:** | |
| * **Features:** `Study_Hours` (continuous), `Prev_Grades` (continuous) | |
| * **Target:** `Test_Scores` (continuous) | |
| * **Description:** Predicts student performance on a standardized test based on study habits and previous grades. | |
| ## How to Use | |
| 1. **Select a Problem:** Choose one of the three problems from the dropdown menu. | |
| 2. **View Dataset:** The corresponding dataset will be displayed, along with a description of its features. | |
| 3. **Train and Evaluate Model:** Adjust the hyperparameters (kernel, C, epsilon) using the sliders. The model will be trained on the dataset and its performance metrics will be displayed. | |
| 4. **Visualize Results:** The regression results will be visualized using a scatter plot with a regression line. | |
| 5. **Interpret Output:** Read the interpretation of the model's output in the context of the selected problem. | |
| ## Additional Features | |
| * **Interactive Visualizations:** Explore the impact of different hyperparameters on the model's performance by adjusting the sliders and observing the changes in the regression results. | |
| * **Upload Your Own Data:** Train an SVM Regressor on your own dataset by uploading a CSV file. (This feature is not yet implemented.) | |
| ## Requirements | |
| * Python 3.7+ | |
| * Streamlit | |
| * Pandas | |
| * Scikit-learn | |
| * Matplotlib | |
| ## Installation | |
| 1. Install Python 3.7+ | |
| 2. Create a virtual environment (optional but recommended) | |
| 3. Activate the virtual environment | |
| 4. Install the required packages using pip: `pip install streamlit pandas scikit-learn matplotlib` | |
| ## Usage | |
| 1. Download the repository and unzip it. | |
| 2. Navigate to the repository directory. | |
| 3. Run the app using `streamlit run app.py`. | |
| ## Contributing | |
| Contributions are welcome! If you find any bugs, have suggestions for improvements, or want to add new features, please feel free to submit a pull request. | |
| ## License | |
| This project is licensed under the MIT License. See the LICENSE file for more details. | |
| ## Acknowledgements | |
| This app is inspired by [https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff](https://medium.com/coinmonks/support-vector-regression-or-svr-8eb3acf6d0ff). |