Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

The main aim of the project is to develop a sentiment analyzer that can be used on twitter data to classify it as positive or negative. Our project takes care of the challenge of bilingual comments, where people tweet in two languages, in this case Hindi and English, in the Latin Alphabet.

This project classifies diseases in grape plant using various Machine Learning classification algorithms.

Ensemble Model Biomarker Analysis R package

Programming assignments completed in the PG Program for AI ML

[In Progress] Code for the paper-- MTDeep: boosting the security of deep neural nets against adversarial attacks with moving target defense

An ensemble of BERTs for classifying injury narratives

Explore the fascinating world of real estate data with this Jupyter Notebook! 🏠📈 Dive into feature engineering, data analysis, and advanced ML models for predicting house prices. 🚀🔍 Contribute to the data science community and sharpen your skills! 💡✨

In this repository, a regression analysis is conducted using different machine learning and deep learning models. The study is led in order to choose the most suitable model by looking at different characteristics (models tuning, features scaling, etc).

UBayFS implements the UBayFS feature selection framework, together with an interactive Shiny dashbord.

Advanced Data Assimilation Algorithms and Methods

Package confeito provides fast ensemble tree inference

Latent space XGBoost for regression of latent variables representing high-dimensional multivariate timeseries in unconventional reservoirs.

Flexible and transparent Python Boruta implementation

Predicting Photosynthesis with Ensemble Models and Neural Networks

Classify malware into families based on file content and characteristics

Boston House Price

This project is focused on end to end application of Machine Learning methodologies to achieve best predicting accuracy. Our goal is to predict churn rate of customers for a telecom service provider based on service charges and usage data of customer.

A Machine Learning project to predict the success or failure of startups based on data by using ensemble modeling techniques, MLflow for tracking experiments, Docker for containerization.