DataForge

Automated Classification System:

1. Developed an automated classification system using machine learning algorithms (Random Forest, AdaBoost, Logistic Regression, KNN, Decision Tree) to streamline and enhance the data classification process. Flexible and User-friendly Application:

2. Designed and implemented a user-friendly application using Streamlit, allowing users to dynamically select classifiers, adjust test/train split percentages, and drop columns for tailored analysis. Text Data Handling and Customization:

3. Integrated advanced features, including the handling of textual data through TF-IDF vectorization and the option for users to drop specific columns of their choice, providing a high level of customization. Real-time Metric Calculation:

4. Implemented live calculation of key metrics (accuracy, precision, recall, and F1 score) for each selected classifier, providing users with instant feedback on model performance. Deployment and Scalability:

5. Deployed the application on a platform like Streamlit, showcasing the ability to make data-driven predictions accessible to non-technical users. Demonstrated scalability and performance optimization for efficient use in various scenarios.

Link: https://dataforge.streamlit.app/