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

Big Data Project - SSML - Spark Streaming for Machine Learning

Chapter 10: Data Preparation for Fraud Analytics

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

This is the Traffic Analysis and Prediction based project from RandomForest 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 .

Machine learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data, identify patterns, and make decisions without explicit programming.

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