MLimputer: Missing Data Imputation Framework for Machine Learning

In this project, we have a set of data related to cyclists, which we intend to analyze, and it should be known that cyclists are very sensitive to air temperature.

This is a machine learning project that predicts credit card payment defaults using Support Vector Machine classification with comprehensive data preprocessing and model optimization.

This project analyzes Netflix's content library using SQL. It explores content type distribution, rating trends, country-wise content availability, and genre classification to extract meaningful insights from Netflix data for better analysis.

A Noise-Resilient Hybrid Imputation-Ensemble (NR-HIE) framework designed to bridge the generalizability gap in medical AI. Utilizing a triple-stream imputation strategy and stacked generalization, this model achieved 81.62% accuracy on external validation data, ensuring robust and medically safe diabetes prediction

This script analyses the relationship between the Human Development Index (HDI), population, and non-religious groups in various countries. Plots visualise relationships between HDI, population, and non-religious groups and using scatterplots and a linear regression model to predict.

handle missing data in practice by understanding developer trends in Stack Overflow survey data