Mental Health Prediction Web App: AI & Machine Learning for Early Detection
Our Mental Health Prediction Web App leverages multiple machine learning algorithms—including Logistic Regression, Random Forest, SVM, KNN, Naive Bayes, Decision Tree, and XGBoost—for early and accurate detection of mental health conditions. Built with Django, HTML/CSS/Bootstrap, and powered by Python’s data analysis libraries, this AI-driven solution transforms behavioural data into actionable mental health insights.
Anomaly Detection in Data using Machine Learning
Detect unusual patterns in data using advanced machine learning techniques. Ensure data integrity, prevent fraud, and improve system reliability.
UPI Fraud Detection Using Machine Learning
Explore how machine learning can detect and prevent fraudulent UPI transactions by analyzing patterns and anomalies in user behavior. This project delivers insights and models to secure digital payments in real time.
Malicious URL Detection using Machine Learning
Detect unsafe websites before they strike! This project uses machine learning to classify and block malicious URLs, protecting users from phishing and cyber-attacks.
IPL Analysis
Uncover winning patterns, top players, and season trends with data-driven IPL insights. Dive into match stats and performance analytics.