I Built ResearchLens AI to Make Data Exploration and ML Benchmarking Easier

Before training a machine learning model, I believe it's important to understand the data.

That's why I developed ResearchLens AI, an interactive dashboard for exploring CSV datasets, identifying data-quality issues, and comparing baseline classification models.

What it does

• Examines missing values, duplicates, and data types.
• Creates interactive data visualisations.
• Compares Logistic Regression and Random Forest.
• Reports balanced accuracy, macro F1, and confusion matrices.
• Fits preprocessing steps on training data to reduce leakage.

Technology

Python, Streamlit, Plotly, Pandas, and Scikit-learn.

Limitations and next steps

This is an educational benchmarking tool, not a fully automated scientific validation platform. Grouped validation, temporal validation, SHAP explainability, and automated testing remain future improvements.

The biggest lesson from this project is that reliable evaluation matters just as much as model selection.

GitHub repository: https://github.com/tasnem-tech/researchlens-ai

I'd love feedback from developers and data scientists working on similar tools.