A Machine Learning project for Machine Learning Internship offered by InternshipStudio.

Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.

I worked on this Live project while working as a Machine Learning Intern at Internship Studio that was offered by National Engineering Olympiad 5.0

Modelling and prediction of default + deployment via AWS Sagemaker

Create a machine learning model to help an insurance company understand which claims are worth rejecting and the claims which should be accepted for reimbursement.

This project explores machine learning techniques, focusing on data preprocessing, model building, and evaluation. It includes data analysis, visualization, various algorithms, and performance comparison. Key topics: data cleaning, feature engineering, model selection, hyperparameter tuning, and evaluation metrics.

Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.

Streamlit App for Node and Graph Classification and Explainability

study of hyperparameter tuning methods