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| title: Digit Recognizer RF | |
| emoji: 🔢 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: streamlit | |
| sdk_version: 1.31.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| python_version: '3.10' | |
| # Digit Recognizer - Random Forest | |
| This project is part of my journey to complete 20 Kaggle competitions. It uses a Random Forest Classifier trained on the MNIST dataset to recognize hand-drawn digits. | |
| ## Project Aim | |
| The goal is to map high-dimensional pixel data into ten distinct numerical categories. The target is to achieve high accuracy while maintaining a computationally efficient pipeline. | |
| ## Dataset | |
| - **Source:** Kaggle Digit Recognizer Competition | |
| - **Format:** 28x28 grayscale images (flattened to 784 pixels) | |
| - **Classes:** 0-9 | |
| ## Tech Stack | |
| - **Model:** Random Forest Classifier (Scikit-Learn) | |
| - **Deployment:** Streamlit & Hugging Face Spaces | |
| - **Environment:** Python 3.10 |