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

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

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.

Predict the Position of a Player Based on his skills and group the players based on their similarities.

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

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

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 repository about Robust Deep Neural Networks with Uncertainty, Local Competition and Error-Correcting-Output-Codes in TensorFlow.

An ensemble of BERTs for classifying injury narratives

With the help of a brand new KATS package, we can detect outliers, change points, and build very strong Time Series Analysis models. By inspecting this repository you can get a solid vision of KATS on real Covid-19 data of Azerbaijan.

Advanced Data Assimilation Algorithms and Methods

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! 💡✨

Repository contains Customer Lifetime Value Prediction for Automobile Insurance Company in USA

A Java and Python package to Predict Retention Times

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).

MediaEval challenge 2019 - to predict the memorability of the Videos

Classify malware into families based on file content and characteristics

Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio

Introductory project to Linear Models, Decision Trees and Ensemble Methods