CompletedUniversity Capstone

TripMate

Emotion-aware AI travel recommendation system developed as a university capstone project.

Role

AI & Backend Developer

Team

Group Project

Duration

Final Year

Project Type

University Capstone

Overview

TripMate combines collaborative filtering, emotion recognition, and natural language processing to recommend personalized travel destinations based on user emotions and preferences.

Problem Statement

Traditional travel recommendation systems rely mainly on popularity or location without considering the user's emotional state and travel preferences.

Solution

TripMate integrates CNN-based facial emotion recognition, collaborative filtering, and NLP to deliver personalized travel recommendations.

Technologies Used

PythonDjangoTensorFlowCNNMySQLNLP

Key Features

✔Emotion Recognition

✔Collaborative Filtering

✔CNN-based prediction

✔Personalized travel recommendation

✔Aspect-based recommendation

Challenges

•Improving emotion detection accuracy

•Preparing training datasets

•Combining multiple recommendation techniques

Lessons Learned

✔Recommendation systems

✔Computer Vision

✔Deep Learning

✔System integration

System Architecture

Architecture diagram coming soon.

This section will showcase the system design, backend flow, AI pipeline, and database architecture.

Source Code

Source Code Not Public

This project was developed as part of academic research and is not available in a public GitHub repository.

Project Summary

This project demonstrates practical experience in designing, developing, and implementing AI-powered software solutions using modern technologies while following software engineering best practices.

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