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.

Contains our Approach for the competition organized at Udyam'21

A machine learning based forecasting system for taxi demand prediction

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.

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

Example notebooks to produce the models used in the SexEst web application.

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.

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

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.

End to end Heart Diseases Prediction Model with webapp using Flask

Machine Learning model for heart failure prediction using LGBM Classifier.

Learning to Rank - Cross Sell

Early prediction of Mortality Risk among Covid -19 Patients in early stages when patients gets admitted into the hospital.

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"

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.

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

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

Predicting Next Booking Destinations for Airbnb Users. Feel free to access the Streamlit App in the link below.