ML Challenge 2026: Business Entity Resolution Solution Template
Team Name: [Your Team Name]
Team Members: [List all team members]
Submission Date: [Date]
1. Executive Summary
Provide a brief 2-3 sentence overview of your approach and key innovations.
2. Methodology
2.1 Problem Analysis
Key insights discovered during EDA — noise patterns, address variations, missing fields, etc.
2.2 Solution Strategy
Outline your high-level approach.
Approach Type: [Blocking + Classifier / End-to-End / Graph-Based / Hybrid, etc]
Core Innovation: [Brief description of your main technical contribution]
3. Candidate Generation (Blocking)
Describe how you reduced the comparison space to a manageable candidate set.
- Blocking keys used: [e.g., PIN code, phonetic name encoding, TF-IDF, etc.]
- Candidate pairs generated: [total]
- How you ensured true matches were not lost:
4. Matching Model
Features used:
- Name features: [e.g., Jaccard, Levenshtein, phonetic encoding]
- Address features: [e.g., token overlap, edit distance, PIN code matching]
- Other: []
Model type: [e.g., XGBoost, Siamese Network, Transformer, etc.]
Threshold selection method: [e.g., F_0.5 optimization on validation set]
5. Results & Error Analysis
- F_0.5 Score (macro): [your best validation score]
- Common false positives (wrong merges): [brief description]
- Common false negatives (missed matches): [brief description]
6. Conclusion
Summarize your approach, key achievements, and lessons learned in 2-3 sentences.
Appendix
A. Code Artefacts
Your complete, runnable code ships in the submission zip under
code/business_entity_resolution/ (all source in src/, with a README.md and
requirements.txt). Summarise its structure and the entry point(s) to reproduce
output/matching_results.tsv and output/candidate_pairs.tsv here.
B. Additional Results
Include any additional charts, graphs, or detailed results.
Note: Teams can modify sections according to their approach while maintaining clarity and technical depth.