15–18 yrs Project 1: School FAQ Chatbot

Problem Statement:

How can students instantly get accurate answers to common school-life questions without waiting for staff?

Objective:

Build a conversational agent that handles 15–20 intents (e.g. “What’s today’s lunch menu?” “How do I join the debate club?”) via a web or messaging interface.

Research Topics:

• Intent detection & slot filling
  
  
• Dialogflow vs. Rasa comparison
  
  
• Handling out-of-scope queries gracefully
  
  
• Basic NLU evaluation metrics (accuracy, intent-error rate)
  
  

Methodology:

1. Design: List 15–20 intents and sample utterances.
   
   
2. Implementation: Use Dialogflow (no-code) or Rasa (light code) to define intents, entities, and fulfillment.
   
   
3. Integration: Embed the bot in a simple HTML/JavaScript page or connect via Telegram.
   
   
4. Testing: Run through 50 test queries, log mis-classifications, and refine.
   
   

Phase-wise Plan (20 hrs):

• Day 1–2 (4 hrs): Identify intents & collect 5–10 utterances each
  
  
• Day 3–4 (4 hrs): Configure NLU pipeline; train initial model
  
  
• Day 5–6 (4 hrs): Build fulfillment logic (static answers or small JSON DB)
  
  
• Day 7–8 (4 hrs): Front-end integration & end-to-end testing
  
  
• Day 9 (2 hrs): Create user guide & maintenance notes
  
  
• Day 10 (2 hrs): Final demo & feedback session
  
  

Expected Outcome:

A live chatbot users can interact with—accurately answering ≥85 % of scripted queries—and a short project report.

15–18 yrs Project 2: Neural Style Art Generator

Problem Statement:

Can we let anyone transform their photos into artworks in the style of famous painters, entirely in the browser?

Objective:

Implement neural style transfer so users can upload one “content” and one “style” image and receive a fused, stylized result.

Research Topics:

• Content vs. style loss in Gatys et al.’s algorithm
  
  
• Pre-trained VGG-19 feature extraction
  
  
• Balancing iterative optimization (content/style weight)
  
  
• Web deployment via TensorFlow.js or a Flask back end + React front end
  
  

Methodology:

1. Core Algorithm: Port the PyTorch/TensorFlow tutorial to your environment.
   
   
2. Optimization: Experiment with different content/style weights to get pleasing outputs.
   
   
3. Deployment: Expose it as a simple web app—either all in Python/Flask or using TF.js to run in the browser.
   
   
4. UI Polish: Allow users to adjust style intensity via a slider.
   
   

Phase-wise Plan (20 hrs):

• Day 1–2 (4 hrs): Reproduce tutorial code on sample images
  
  
• Day 3–4 (4 hrs): Tweak weights & hyperparameters for 3 distinct style images
  
  
• Day 5–6 (4 hrs): Build web UI + back-end inference endpoint
  
  
• Day 7–8 (4 hrs): Integrate slider controls & test on user images
  
  
• Day 9 (2 hrs): Write usage instructions & showcase gallery
  
  
• Day 10 (2 hrs): Final presentation of user-generated artworks
  
  

Expected Outcome:

A demo site where peers can upload two images and download a stylized result, plus a “gallery” of at least three before/after examples.

UG Project 1: Movie Recommendation System

Problem Statement:

How can we suggest films to users based on past ratings, minimizing “cold start” issues?

Objective:

Build a collaborative-filtering recommender (e.g. matrix factorization via Surprise or Implicit) using MovieLens 100K, then wrap it in a Flask API.

Research Topics:

• Collaborative vs. content-based filtering
  
  
• SVD matrix factorization fundamentals
  
  
• Evaluation (RMSE, MAE, precision@k)
  
  
• Simple UX: collecting initial user ratings
  
  

Methodology:

1. Data Prep: Load MovieLens 100K; split into train/validation.
   
   
2. Modeling: Train at least two algorithms (e.g. SVD, KNN) and compare.
   
   
3. API: Create an endpoint where a user POSTs 5 ratings and GETs 5 recommendations.
   
   
4. Report: Analyze error metrics and discuss cold-start mitigation (e.g. hybrid).
   
   

Phase-wise Plan (20 hrs):

• Day 1–2 (4 hrs): Exploratory data analysis & train/test split
  
  
• Day 3–4 (4 hrs): Train & evaluate two algorithms; log metrics
  
  
• Day 5–6 (4 hrs): Build Flask API for inference
  
  
• Day 7–8 (4 hrs): Write simple HTML/JS front end or CURL examples
  
  
• Day 9 (2 hrs): Document endpoints & usage examples
  
  
• Day 10 (2 hrs): Present recommendations & metric comparisons
  
  

Expected Outcome:

A runnable service returning personalized movie lists with ≥0.9 RMSE improvement over baseline, plus a short comparative analysis.

UG Project 2: Transformer-Based Text Summarizer

Problem Statement:

How can busy readers quickly digest long articles by reading automatically generated concise summaries?

Objective:

Fine-tune a pre-trained T5 or BART model on a news-summarization dataset (CNN/DailyMail) and serve it via a REST API.

Research Topics:

• Encoder–decoder transformer architecture
  
  
• Fine-tuning with Hugging Face’s Trainer API
  
  
• ROUGE evaluation metrics
  
  
• Resource considerations for inference (batch size, beam search)
  
  

Methodology:

1. Dataset: Download the CNN/DailyMail subset.
   
   
2. Preprocessing: Tokenize with Hugging Face tokenizer, truncate/segment long inputs.
   
   
3. Fine-Tuning: Set up Trainer with early stopping; track ROUGE-1/2/L.
   
   
4. API: Build a Flask endpoint for sending raw text and receiving the summary.
   
   

Phase-wise Plan (20 hrs):

• Day 1–2 (4 hrs): Data cleanup & tokenization pipeline
  
  
• Day 3–4 (4 hrs): Configure & run fine-tuning; monitor validation ROUGE
  
  
• Day 5–6 (4 hrs): Test inference on 10 unseen articles; record ROUGE
  
  
• Day 7–8 (4 hrs): Develop REST API endpoint + usage script
  
  
• Day 9 (2 hrs): Prepare examples & usage documentation
  
  
• Day 10 (2 hrs): Final demo: live summarization of news links
  
  

Expected Outcome:

A service achieving ≥35 ROUGE-1 on validation, plus an interactive demo where users submit URLs or text and get concise summaries.