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…
I Built ResearchLens AI to Make Data Exploration and ML Benchmarking Easier
Before training a machine learning model, it's crucial to understand the data. To facilitate this, I developed ResearchLens AI, an interactive dashboard designed for exploring CSV datasets, identifying data-quality issues, and comparing baseline classification models. The tool examines missing values, duplicates, and data types, creates interactive data visualizations, compares Logistic Regression and Random Forest models, and reports balanced accuracy, macro F1, and confusion matrices. It also fits preprocessing steps on the training data to reduce leakage.
ResearchLens AI is built using Python, Streamlit, Plotly, Pandas, and Scikit-learn. While it offers a valuable educational benchmarking tool for developers and data scientists, it's essential to note that it's not a fully automated scientific validation platform. Future improvements could include grouped validation, temporal validation, SHAP explainability, and automated testing. The biggest takeaway from this project is that reliable evaluation is as important as model selection itself.
You can find the GitHub repository for ResearchLens AI at https://github.com/tasnem-tech/researchlens-ai. The creator, Tasnem Tech, welcomes feedback from developers and data scientists working on similar tools.
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