This project involves detecting and tracking children with Autism Spectrum Disorder (ASD) and therapists in a video. It uses real-time person detection through YOLOv8 and tracking via Deep SORT. The aim was to create a tool to aid in therapy sessions by identifying individuals and their movements. The project ensures privacy and ethical considerations while leveraging AI technologies.
This project aims to detect fraudulent transactions using machine learning. The dataset provided by a financial company contains thousands of transactions, and the model helps in predicting fraud based on past patterns. By utilizing classification algorithms like Logistic Regression and Random Forest, the model can identify anomalies and flag suspicious transactions.
This project focuses on predicting customer behavior to optimize marketing strategies. By analyzing customer purchase data, the model predicts which products a customer is likely to buy based on their historical behavior. Techniques like regression and classification are used to understand and predict customer trends and preferences.
This project involves predicting stock market prices using machine learning models. By analyzing historical stock data, we can predict future trends. Techniques like time series forecasting and LSTM (Long Short-Term Memory) networks are applied. The goal is to help investors make informed decisions based on predictions.