Contains a collection of my experimentations, explorations, and data analysis of random datasets
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Updated
Oct 22, 2019 - HTML
Contains a collection of my experimentations, explorations, and data analysis of random datasets
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
Helpfulness of Amazon Digital Video Game Reviews: An Application of SHAP on NLP
Car dealership web application that is enhanced with online machine learning and interpretable machine learning.
Explainable prediction of next year GDP Growth using the Kaggle World Development Indicators
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.
A website that provides analytics on how different features contribute to your chances of getting into a university of your choice.
Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.
Counterfactual SHAP: a framework for counterfactual feature importance
This repository presents a comprehensive project that leverages relevant features to accurately predict the Wine Quality.
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.
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.
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.
Automated Tool for Optimized Modelling
This repository includes a machine learning modeling study about estimating customers hotel cancellation and what are the reasons for these cancellations.
Build a Web App called AI-Powered Heart Disease Risk Assessment App
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