imblearn

Traffic Accident Analysis using python machine learning

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Our goal was to create a ML bot that analyzes real time trading data to determine the most opportune times buy and sell stock

Utilizing machine learning to examine deforestation rates in the undeveloped region of Paraguay's Chaco

Code repository for the book "Imbalanced Data: Myths, Mistakes and Modern Solutions".

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

Case study from UDACITY Data Scientist Nanodegree

Corporate Credit Rating Prediction System - Knowledge Engineering Project - A.A. 2023-2024

Imbalanced Intent classification model with deployment

Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite, Accuracy: 98% AUC-ROC

Course Project for CS273A: Machine Learning at UCI

Machine learning is used to analyze and predict attrition flags in credit card customers.

Facial skin disease detection using Neural Networks

End-to-end ML pipeline predicting loan default risk from real-world credit application data. Multi-table feature engineering, model comparison (Logistic Regression, Random Forest, XGBoost), and a FastAPI endpoint serving predictions in a Dockerized deployment.

Data Science Classification General Notebook

Prediction module for Tumor Teller - primary tumor prediction system

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

An analysis on credit risk

Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk