NAMESPACE

Kaggle Wars

hackathon · offline · 19 April 2026 · Organised by Helix, Thapar University

Kaggle Wars

Starts
19 April 2026 at 10:00 am IST
Venue
Thapar University, Bhadson Road, Patiala, Punjab, 147004, India
Registration
29 March 2026
Team size
Up to 4

Register and view the event on NAMESPACE

About the event

Kaggle War is an immersive data science competition under MLSC X HELIX crafted to challenge participants in analytical thinking, machine learning, and real-world problem-solving. Moving beyond theory, it delivers a hands-on, competitive environment where participants build, refine, and strategically optimize models—just like professional data scientists.


Participants engage with a real-world–inspired dataset, tackling everything from data preprocessing and feature engineering to model tuning and evaluation. With a live leaderboard driving the competition, the focus extends beyond accuracy to include strategy, iteration, and decision-making—making it a true battlefield for data enthusiasts.


Date : Apr 19, 2026

Time : 10:00 AM

Team Size : 2-4 members

Prize Pool : Rs.20,000


Rules & Regulations


  1. Participants must bring their own laptops.
  2. Teams can consist of 2–4 members.
  3. All the participants must be of same college.
  4. Internet usage will be monitored throughout the event.
  5. Use of unfair means, plagiarism, or external/private datasets is strictly prohibited and leads to disqualification.
  6. Submissions must follow the defined evaluation metrics and guidelines.
  7. Organizers reserve the right to modify rules if necessary.
  8. In case of disputes, the coordinators' decisions are final.


Shortlisting Criteria


Participants will be evaluated through a combination of quantitative metrics and qualitative assessments:

  1. Model Performance: Primary ranking based on the defined evaluation metric (e.g., accuracy, F1-score, RMSE).
  2. Data Preprocessing: Effective handling of missing values, outliers, cleaning, and transformation.
  3. Feature Engineering: Creativity and effectiveness in deriving meaningful features to improve performance.
  4. Model Optimization: Appropriate choice of algorithms, hyperparameter tuning, and cross-validation.
  5. Advanced Techniques (Bonus): Implementation of ensemble methods, stacking, or innovative approaches.
  6. Generalization Ability: Performance consistency between public and private leaderboards (avoiding overfitting).
  7. Code Quality (Finalists): Clean, well-structured, and reproducible code with a clear methodology.


Organizing Team:


Khushi Dangi - +91 98781 02076

kdangi_be24@thapar.edu


Sudiksha - +91 73800 41807

ssharma15_be24@thapar.edu

Timeline

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