Learning the topic Machine learning, with sample code.

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.

Comparing Random Forest, Gradient Boosting, and XGBoost to select the best model to predict potential donors for a Charity.

A deep learning-based facial emotion recognition system using the CK+ dataset and a custom CNN built with PyTorch.

Implementing Breast cancer detection using machine learning

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.

Superstore Time Series Data Analysis and its prediction for next 4 years using ARIMA model.

🔹 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

All about Data intelligence

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.