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| # Module 2 Project Requirements Checklist | |
| ## Modeling Approaches | |
| - [x] Naive baseline (random retrieval) | |
| - [x] Classical ML model (TF-IDF + cosine similarity) | |
| - [x] Deep learning model (sentence-transformers dense embeddings + FAISS) | |
| - [x] All three documented with locations in repo | |
| ## Experimentation | |
| - [x] Focused experiment conducted (k-sensitivity analysis, k=1-10) | |
| - [x] Experiment is well-motivated and clearly described | |
| - [x] Results properly interpreted (in `notebooks/analysis.md`) | |
| ## Interactive Application | |
| - [ ] **Publicly accessible via the internet (DEPLOYED)** | |
| - [x] Runs model inference only (no training) | |
| - [ ] **Good UX — beyond basic Streamlit** (assignment says "a basic Streamlit app is not going to cut it") | |
| - [x] Streamlit app exists (`main.py`) with game selector, retriever selector, example questions, eval metrics | |
| ## Written Report | |
| - [ ] **Formal report (NeurIPS/ICML paper, white paper, or technical report)** | |
| - [ ] Problem Statement | |
| - [ ] Data Sources | |
| - [ ] Related Work | |
| - [ ] Evaluation Strategy & Metrics (with justification) | |
| - [ ] Modeling Approach | |
| - [ ] Data Processing Pipeline (with rationale) | |
| - [ ] Hyperparameter Tuning Strategy | |
| - [ ] Models Evaluated (all 3 with rationale) | |
| - [ ] Results (quantitative comparison, visualizations, confusion matrices) | |
| - [ ] Error Analysis (5 specific mispredictions with root causes and mitigations) | |
| - [ ] Experiment Write-Up (plan, results, interpretation, recommendations) | |
| - [ ] Conclusions | |
| - [ ] Future Work | |
| - [ ] Commercial Viability Statement | |
| - [ ] Ethics Statement | |
| ## In-Class Pitch (5 min) | |
| - [ ] **Presentation prepared** | |
| - [ ] Problem & Motivation | |
| - [ ] Approach Overview | |
| - [ ] Live Demo | |
| - [ ] Results, Insights, Key Findings | |
| ## Code & Deployment | |
| - [x] GitHub repository with full codebase | |
| - [ ] **Link to live deployed web application** | |
| - [x] App runs inference on trained model | |
| - [x] Git best practices (branches used, PRs made) | |
| ## Repository Structure | |
| - [x] README.md | |
| - [x] requirements.txt | |
| - [x] main.py (UI entry point) | |
| - [x] scripts/ directory with pipeline modules | |
| - [x] models/ directory | |
| - [x] data/ directory (raw, processed, outputs) | |
| - [x] notebooks/ directory | |
| - [x] .gitignore | |
| ## Code Quality | |
| - [x] Jupyter notebooks only in `notebooks/` (none used — all Python scripts) | |
| - [x] Code modularized into classes and functions | |
| - [x] No loose executable code outside functions/`__main__` | |
| - [x] Descriptive variable names, docstrings, comments | |
| - [ ] External code / AI attribution at top of files (if applicable) | |
| ## Novelty / Contribution | |
| - [x] Novel approach explained (multi-game RAG with 3-retriever comparison) | |
| - [x] Related work referenced (in project description) | |