Automated Tool for Optimized Modelling

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

Counterfactual SHAP: a framework for counterfactual feature importance

A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.

Contains a collection of my experimentations, explorations, and data analysis of random datasets

This repo is all about feature importance. Whereby we look at the ways one can identify if a feature is worth having in the model or rather if it has a significant influence in the prediction. The methods are model-agnostic.

Explainable prediction of next year GDP Growth using the Kaggle World Development Indicators

Car dealership web application that is enhanced with online machine learning and interpretable machine learning.

This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost

Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.

Helpfulness of Amazon Digital Video Game Reviews: An Application of SHAP on NLP

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 repository presents a comprehensive project that leverages relevant features to accurately predict the Wine Quality.

This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.

Create a machine learning model to predict whether an individual earns above 50,000 in a specific currency or not.

The project consists of data obtained from the National Science Foundation’s (NSF) National Ecological Observatory Network (NEON) database. This project obtained data sets of quantified variables that are related to surface water quality, identify suitable predictors and a target response to construct ensemble based models.

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