Open to opportunities

MOULI BANERJEE

@moulibanerjee

Message

Data science and machine learning intern building predictive models and real-time apps for actionable insights.

India

Message

What I'm looking for

I’m looking for a role where I can build and explain predictive ML solutions, ship interactive dashboards/apps, and collaborate on secure, production-minded implementations that deliver measurable improvements.

I’m a Data Science and Machine Learning Intern who develops and optimizes classification models for measurable outcomes. In my CipherByte internship, I built and improved KNN, SVM, and Logistic Regression pipelines, reaching a 92% F1-score, and used Exploratory Data Analysis to raise prediction precision by 12%.

I also turn models into usable systems through practical engineering. I constructed a Tkinter desktop app for immediate spam detection, reducing processing time by 40%, and at Academy Innova World I built and deployed 5+ end-to-end ML models achieving up to 93% accuracy—while automating preprocessing to boost accuracy by 18% and reduce manual effort by 70%.

Beyond modeling, I build explainable, interactive experiences. Projects like Insight Predict use dashboards with PCA and SHAP across 1,000+ records to strengthen interpretability, and I’ve shipped web apps (React/Node/Express/MongoDB, OAuth) such as a real-time collaborative “Think Canvas.” I’m excited by roles where I can deliver reliable, secure analytics and ML-driven products end-to-end.

Experience

Work history, roles, and key accomplishments

Data Science Intern

CipherByte Technologies

Jul 2025 - Aug 2025 (1 month)

Developed and optimized classification models (KNN, SVM, Logistic Regression) achieving a 92% F1-score on validation data. Built a Tkinter desktop application for spam detection using the trained models, reducing processing time by 40%.

K NN SVM Logistic Regression Classification F1 Score scikit learn Tkinter Spam Detection

Machine Learning Intern

Academy Innova World

Jun 2025 - Jul 2025 (1 month)

Built and deployed 5+ end-to-end machine learning models (Linear/Logistic Regression, Random Forest, XGBoost, CatBoost) achieving up to 93% accuracy. Created Tkinter dashboards integrating Matplotlib, PCA, and SHAP to analyze 20K+ data points and improve explainability.