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| title: ML Model Comparison Tool | |
| emoji: 🔥 | |
| colorFrom: red | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.14.0 | |
| python_version: '3.13' | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # AI Model Comparison App | |
| A Machine Learning web application built using Python and Gradio. | |
| This project allows users to compare multiple Machine Learning models on different datasets for both Classification and Regression tasks. | |
| --- | |
| # Features | |
| ## Classification | |
| Users can choose one of the following datasets: | |
| - Iris Dataset | |
| - Breast Cancer Dataset | |
| - Titanic Dataset | |
| ## Regression | |
| Users can choose one of the following datasets: | |
| - California Housing Dataset | |
| - Diabetes Dataset | |
| - Boston Housing Dataset | |
| --- | |
| # Machine Learning Models | |
| ## Classification Models | |
| - Logistic Regression | |
| - Decision Tree Classifier | |
| - Random Forest Classifier | |
| - K-Nearest Neighbors (KNN) | |
| ## Regression Models | |
| - Linear Regression | |
| - Decision Tree Regressor | |
| - Random Forest Regressor | |
| - Support Vector Regressor (SVR) | |
| --- | |
| # Technologies Used | |
| - Python | |
| - Scikit-learn | |
| - Pandas | |
| - Gradio | |
| - Hugging Face Spaces | |
| --- | |
| # Project Structure | |
| ```bash | |
| ├── app.py | |
| ├── requirements.txt | |
| ├── README.md | |
| ``` | |
| --- | |
| # Installation | |
| ## Clone the Repository | |
| ```bash | |
| git clone https://github.com/your-username/your-repo-name.git | |
| cd your-repo-name | |
| ``` | |
| ## Install Required Libraries | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Run the Application | |
| ```bash | |
| python app.py | |
| ``` | |
| --- | |
| # Hugging Face Deployment | |
| ## Steps | |
| 1. Create a new Space on Hugging Face | |
| 2. Choose: | |
| - SDK: Gradio | |
| 3. Upload these files: | |
| - app.py | |
| - requirements.txt | |
| - README.md | |
| 4. Wait for automatic deployment | |
| --- | |
| # How the Application Works | |
| ## Step 1 | |
| Select the task type: | |
| - Classification | |
| - Regression | |
| ## Step 2 | |
| Choose a dataset | |
| ## Step 3 | |
| Click the button to run the models | |
| ## Step 4 | |
| The application will: | |
| - Train multiple machine learning models | |
| - Compare their performance | |
| - Display the evaluation results | |
| - Show the best-performing model | |
| --- | |
| # Datasets Used | |
| ## Classification Datasets | |
| ### Iris Dataset | |
| A famous dataset used for flower classification. | |
| ### Breast Cancer Dataset | |
| Used to classify tumors as malignant or benign. | |
| ### Titanic Dataset | |
| Predicts passenger survival on the Titanic. | |
| --- | |
| ## Regression Datasets | |
| ### California Housing Dataset | |
| Predicts housing prices in California districts. | |
| ### Diabetes Dataset | |
| Predicts disease progression measurements. | |
| ### Boston Housing Dataset | |
| Predicts house prices using multiple features. | |
| --- | |
| # Evaluation Metrics | |
| ## Classification | |
| - Accuracy Score | |
| ## Regression | |
| - R² Score | |
| - Mean Squared Error (MSE) | |
| --- | |
| # Future Improvements | |
| - Add more datasets | |
| - Add XGBoost and LightGBM | |
| - Allow users to upload custom datasets | |
| - Add graphs and visualizations | |
| - Generate downloadable reports | |
| - Deploy with a custom UI design | |
| --- | |
| # Example Use Case | |
| A user selects: | |
| - Task Type: Classification | |
| - Dataset: Iris Dataset | |
| The app trains: | |
| - Logistic Regression | |
| - Decision Tree | |
| - Random Forest | |
| - KNN | |
| Then compares their accuracy scores and displays the best model. | |
| --- | |
| # Author | |
| ## Saja | |
| - Master’s Student in Artificial Intelligence | |