AI-Powered Crush Predictor - Machine Learning Relationship Analyzer Final Year Project

AI-Powered Crush Predictor - Complete Machine Learning Final Year Project

The AI-Powered Crush Predictor is an innovative machine learning final year project that demonstrates the practical application of multiple classification algorithms in predicting relationship compatibility. This comprehensive project combines advanced data science techniques with modern web development, making it an ideal choice for computer science and engineering students.

Project Overview

This machine learning project uses behavioral analysis and pattern recognition to predict relationship outcomes with up to 75% accuracy. Built using Python's scikit-learn library, the system trains and compares four different machine learning algorithms to provide the most accurate predictions possible.

Key Features of This Final Year Project

• Multiple ML Algorithms: Implements Random Forest Classifier, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) for comprehensive model comparison
• RESTful API Architecture: Clean Flask-based backend with CORS support for seamless frontend-backend communication
• Interactive Data Visualization: Real-time radar and bar charts using Chart.js to display prediction confidence and feature importance
• Beautiful 3D Interface: Animated Three.js background with floating hearts for enhanced user experience
• Responsive Web Design: Fully responsive interface that works flawlessly on mobile, tablet, and desktop devices
• Model Training Pipeline: Automated training script that generates 2000 synthetic samples and selects the best-performing model
• Feature Engineering: 10 carefully selected behavioral features including eye contact frequency, conversation initiation, message response time, and physical proximity
• Prediction Confidence Scoring: Provides percentage-based confidence levels with detailed explanations

Technical Implementation

The project follows industry-standard software architecture with clear separation of concerns. The Flask backend handles all machine learning operations, model training, and predictions, while the frontend provides an intuitive interface for user interaction. The system uses joblib for model persistence, ensuring quick loading times and efficient memory usage.

Real-World Applications

• Social Psychology Research: Understanding behavioral patterns in interpersonal relationships
• Dating Applications: Compatibility matching algorithms for dating platforms
• Behavioral Analysis: Pattern recognition in human interactions for psychological studies
• Machine Learning Education: Demonstrating classification algorithms and model comparison techniques
• Web Application Development: Showcasing full-stack development skills with modern frameworks

Machine Learning Techniques Used

This project demonstrates proficiency in supervised learning, specifically classification problems. The Random Forest algorithm typically achieves the highest accuracy due to its ensemble approach, while Logistic Regression provides interpretable results. SVM handles non-linear relationships effectively, and KNN offers a simple yet powerful baseline comparison.

Project Structure and Organization

The project follows best practices with modular code organization, separate configuration files, and clear directory structure. The models directory stores trained algorithms, the templates folder contains HTML files, and static assets are properly organized for easy maintenance and scalability.

What You Will Learn

• Implementing multiple machine learning algorithms and comparing their performance
• Building RESTful APIs with Flask for ML model deployment
• Creating synthetic datasets for model training and validation
• Frontend-backend integration using AJAX and JSON
• Data visualization techniques for presenting ML results
• Model persistence and deployment strategies
• Cross-Origin Resource Sharing (CORS) implementation
• Responsive web design with modern CSS and JavaScript

Perfect For

This final year project is ideal for B.Tech, B.E., MCA, and M.Tech students specializing in Computer Science, Information Technology, Artificial Intelligence, or Data Science. It demonstrates strong technical skills in both machine learning and web development, making it impressive for academic evaluation and job interviews.

Delivery Includes

• Complete source code with detailed comments
• Pre-trained machine learning models
• Model training script with dataset generation
• Comprehensive project documentation
• Installation and setup guide
• API documentation with request/response examples
• Project report template ready for submission
• PPT presentation slides

Technical Requirements

The project requires Python 3.7 or higher with Flask, scikit-learn, NumPy, and Pandas libraries. Frontend dependencies include Three.js for 3D graphics and Chart.js for data visualization. All dependencies are clearly listed in requirements.txt for easy installation.

Why Choose This Project

This project stands out because it combines trendy machine learning concepts with practical web development skills. The topic is engaging and easy to present, while the technical implementation is sophisticated enough to impress evaluators. The multi-algorithm approach demonstrates understanding of model selection and comparison, a crucial skill in data science.

Support and Customization

At CodeAj Marketplace, we provide comprehensive support for project setup and customization. Our team can help you understand the code, modify features, and adapt the project to your specific requirements. We also offer custom project development services if you need a unique implementation.