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This project is an intelligent analytics dashboard designed to help e-commerce business owners make sense of their customer data. Instead of forcing users to analyze endless spreadsheets of transactions, the system uses Machine Learning to automatically sort shoppers into distinct, actionable groups—such as "Premium Loyalists" or "At-Risk Whales"
🔹 Hands-on experience in building and training ML & DL models using TensorFlow 🤖 🔹 Skilled with Keras API, Tensors, & Computational Graphs ⚙️ 🔹 Developed projects like Image Classification & Neural Networks 🧠 🔹 Strong understanding of data preprocessing model evaluation 🔹 Exploring model
A machine learning project using Random Forest to detect abnormal electricity usage. Features include yearly average consumption, seasonal adjustment, and vacation handling. Comes with a risk dashboard (Normal / Medium / High Risk) and model saving for reuse.
Machine learning models trained on U.S. Census data to classify income levels, including preprocessing, PCA, SVD, and evaluation with MLP, logistic regression, and Naïve Bayes.
This repository contains a basic fraud detection system utilising supervised learning techniques to identify potentially fraudulent credit card transactions. The project establishes a baseline model that addresses the challenges of credit card fraud in financial institutions.