Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.

This project focuses on predicting heart disease using a comprehensive dataset containing patient information. The goal is to build machine learning models that can predict the presence of heart disease based on various health parameters.

This repository contain my final projekt on the Data science Skillbox school on the topic: "Development of a machine learning algorithm to predict the behavior of customers of the "SberAvtopodpiska"

Bank Churn Classification

Identifying Giants and Dwarfs Stars through Machine Learning

Participated in Analytics Vidya Hackathon ( JOB-A-THON | May 2021 ). This Repository contains all code, reports and approach.

MlFlow Project creating pipelines and using Grid-Search Cross Validation to find optimal parameters for Old School Runescape Machine Learning datasets.

Spectral type classification using LGBM and deployed using FastAPI, Pydantic, and Docker

데이콘 신용카드 사용자 연체 예측 AI 경진대회 코드

Using LGBMClassifier to solve To-Be Challenge, which is a machine learning challenge on CodaLab Platform that aims to adress the problems of medical imbalanced data classification.

Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.

Credit card fraud detection using various sampling methods and machine learning algorithms.

End to end Heart Diseases Prediction Model with webapp using Flask

Practicum Workshop

With imbalanced observed data, a search for the best model is conducted. The bank is seeing its customers leave. Wondering if there are patterns to their decision to exit, the bank wishes to anticipate for this trend. When the positive class is the minority in an imbalanced dataset, a model need to be trained for robustness.

Interconnect seeks to forecast customer churn by analyzing package choices and contracts. If a customer plans to leave, they're offered unique codes and special packages to foster loyalty.

This project is about to detecting the text generated by different LLM given prompt. The instance is labeled by Human and Machine, and this project utilised both traditional machine learning method and deep learning method to classify the instance.

The goal of this Project is to predict whether a mushroom is edible or poisonous based on its physical characteristics.

Rank 4/125 MachineHack

Learning to Rank - Cross Sell