This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

Modelling and prediction of default + deployment via AWS Sagemaker

In this analysis, the credit risk dataset consisting of 32851 loan records to determine how best to predict whether or not a loan applicant will repay.

Credit Score Prediction is a machine learning project that classifies credit scores ('Good', 'Standard', 'Poor') using a streamlined pipeline. It involves data extraction, cleaning, and preprocessing, with key techniques like Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for efficient and accurate model tr

Model to Predict if a customer will purchase a Travel Package

Project for insurance churn prediction with xgboost classification algorithm

Develop a ML model that can accurately predict credit risk for loan applicants based on historical data and financial metrics.

Maybank - Senior Data Scientist

👨🏼‍⚕️🧠 Web-App predicting brain drain in AI research at public institutions

Predicting the probability of a loan applicant paying back the loan.This repository aims to analyze data from different types of personal loans and apply machine learning algorithms to develop a credit risk predictor.

This repository contains a machine learning project that classifies patients at risk of cervical cancer using the XGBoost algorithm. The project includes data preprocessing, feature selection, model training, and evaluation to achieve high accuracy in identifying at-risk patients.

HOTEL RESERVATION CANCELLATION

Creating SQL databases and writing queries in R.

The goal of this project is to create a classifier and see how accurately it can predict song genres. Taking a dataset from Spotify [Pandya, 2022], which is al- ready using machine learning algorithms for these purposes, can help assess if the resulting model can be considered apt for a large-scale business.

Using XGBoost to build a collection of boosted trees. Utilizing continuous and categorical customer data from the Telco Churn Dataset (IBM Base Samples) to predict whether or not a customer will stop using the company's service.

A Flask application to predict the productivity of garment manufacturing employees using a pre-trained Random Forest Regressor model and XGBoost classification model. This tool helps optimize processes and improve overall efficiency in the garment industry.