I‘m Inamullah. Welcome to my little corner of the web! This site is like a dungeon full of my adventures in coding, design, and all things geeky, whether I‘m weaving spells with data or crafting web wonders. Take a stroll and explore the bits and bytes of my world. Got a quest for me or just want to chat? Let‘s geek out together!
We implemented and evaluated Generalized Inverted Index (GIN) and Block Range Index (BRIN) in SQLite, aiming to enhance query performance for large datasets. Our tests showed that while BRIN indexing significantly outperforms other methods in querying time, the use of GIN should be carefully considered due to its substantial setup and querying overhead in SQLite environments.
In our project, we estimated obesity levels using Logistic Regression, SVC, GB, RF, and MLP models, achieving 99.17% accuracy with SVM. We employed feature engineering, hyperparameter tuning, cross-validation, and feature importance tests to optimize and validate our models.
We developed and evaluated three machine learning models (Logistic Regression, Random Forest, MARS Model) for predicting heart disease using a dataset of over 319,000 health records. Despite high accuracy, we found issues with low specificity, leading to more false positives.
