Cyber-attack classification in the network traffic database using NSL-KDD dataset

Credit Risk Analysis with Machine Learning

To identify lithologies, geoscientists use subsurface data such as wireline logs and petrophysical data. However, this process is often tedious, repetitive, and time-consuming. This project aims to use machine learning techniques to predict lithology from petrophysical logs, which are direct indicators of lithology.

SageMaker Experiments and DVC

No description provided.

Interpreting wealth distribution via poverty map inference using multimodal data

Utilizing Machine Learning for portfolio selection with the aim of out-performing benchmark indices

Application of Machine Learning models to predict Company Bankruptcy

A python script for basic data cleaning/manipulation and modelling based on the open source House Sales Advanced Regression Techniques(Kaggle)

Predict the winning probability of white player in a chess game on the basis of first move of white player and first move of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move

In this repo, I am doing data analysis in water potability and check each and every classification model's accuracy.

Implemented various ML algorithms with and without library functions. Final Project-->Application of LGBM, XGBoost, Catboost and SVC

Application of Machine Learning Models to predict Credit Risk

Time Series Analysis and Forecasting for an online store using LSTM and CatBoost Algorithm.

Using SHAP values to explain model features

No description provided.

HackerEarth Machine Learning Challenge - Adopt a Pet Buddy, ----MultiClass and MultiLabel Classification using Catboost Classifier--

HealthCare Length of Stay predictions with Booster algorithms

this project leverages machine learning to predict customer churn, and we've built an interactive interface using Streamlit and containerized everything with Docker for easy deployment.

Implemeting Modern Day Boosting algos like LightGBM, XgBoost and Catboost to predict yield of Wild Blueberry, done as part of a Kaggle Competition.

Rank 9/113 AnalyticsVidhya

Predict the likelihood of a genome sequence undergoing mutation.

Predictive microbiology for challenge-test style shelf-life simulation.

A machine learning pipeline using CatBoost Regression to predict anime ratings by analyzing studio, genre, setting, and character demographics from a Hugging Face dataset.

Rank 1/190 MachineHack

Classifying german fake news based on news articles (text). The training data comes from Kaggle and a claim-by-claim approved fake news dataset by authoritative sources (see README.md)

Implementing Catboost

Modified fastapi microservice with scheduled A/B tests of base&enhanced catboost models (microservice based on final project of Start ML course at karpov.courses)

No description provided.

Project where I predict the probability of a customer leaving the telecom company