Big Data Project - SSML - Spark Streaming for Machine Learning

Chapter 12: Data Preparation for Fraud Analytics

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

This project classifies multiple images into their respective categories with the help of an efficient Classifier

It calculates the accuracy score and confusion matrix for a logistic regression model. The dataset is about coupon used or not in an apparel store known as Simmons .

This project uses Cifar-10 dataset to classify the images into 10 different classes