This project tackles the growing concern of obesity by developing a model to predict an individual's risk. By analyzing various factors, we aim to identify people who might be more susceptible to weight gain and related health problems.

Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)

Contains our Approach for the competition organized at Udyam'21

A machine learning based forecasting system for taxi demand prediction

Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.

This approach has the potential to create accurate, generalizable and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction and early disease identification.

This is my Hamoye Stage C tag-along project. The notebook focuses on applying Machine Learning Classification models and Measuring Classification Performance.

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.

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.

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

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.

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.