rajchandran006-ops/rfd-classification-machine-learning-project

This is a machine learning project that builds and compares several classification algorithms on a dataset labeled "RFD", though the README does not explain what RFD stands for or what the data represents. Classification in machine learning means training a program to sort items into categories based on their features, similar to how a spam filter learns to sort emails into "spam" or "not spam." The project walks through the full machine learning pipeline: cleaning the data, exploring patterns in it through charts and statistics, selecting the most useful data columns, and then training six different classification algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Each algorithm takes a different mathematical approach to the same problem, so comparing them reveals which one works best for this particular dataset. Everything is implemented in Python using Jupyter Notebook, which presents the code and results side by side. This is a learning or portfolio project, with plans mentioned for future improvements like model deployment and a web dashboard.