Week 18 – Practicing with Kaggle Challenges
Dates: September 28 – October 4
Internship: AI/ML Intern at SynerSense Pvt. Ltd.
Mentor: Praveen Kulkarni Sir
Focus
This week was all about hands-on competition experience — using Kaggle challenges to test my skills in real-world, data-driven scenarios.
After weeks of theory, model tuning, and internal experimentation, the goal was to measure how well those learnings translate to open-ended, public datasets where creativity, efficiency, and robustness all matter.
Goals for the Week
- Participate in Kaggle competitions across vision and NLP domains
- Build, train, and submit models using real-world datasets
- Improve leaderboard scores through feature engineering and tuning
- Learn collaborative workflows using Kaggle notebooks and discussions
- Analyze top-scoring kernels and learn from community approaches
Tasks Completed
| Task | Status | Notes |
|---|---|---|
| Participated in Kaggle “Digit Recognizer” (MNIST) competition | ✅ Completed | Achieved >99% accuracy using CNN and data augmentation |
| Tried “House Prices – Advanced Regression Techniques” | ✅ Completed | Practiced feature engineering, one-hot encoding, and cross-validation |
| Participated in NLP “Disaster Tweets” classification task | ✅ Completed | Used BERT embeddings and fine-tuned for better F1-score |
| Explored top Kaggle notebooks and implemented improvements | ✅ Completed | Compared ensemble methods and model stacking |
| Documented findings and model versions | ✅ Completed | Stored results and metrics in GitHub for reproducibility |
Key Learnings
- Competitions mirror real-world pressure. Kaggle forces clear thinking under data and time constraints.
- Feature engineering matters more than architecture sometimes. Clean data often beats complex models.
- Community learning accelerates growth. Reading public notebooks provided valuable shortcuts and insights.
- Evaluation metrics guide better models. Understanding RMSE, F1, and log-loss changed how I approached validation.
Challenges and Solutions
| Challenge | Solution |
|---|---|
| Handling large datasets with limited compute | Used Kaggle’s GPU runtimes and reduced image sizes for faster training |
| Overfitting on training data | Added dropout, regularization, and early stopping |
| Poor initial leaderboard ranking | Iteratively tuned features and hyperparameters |
| Comparing models efficiently | Created a local validation pipeline with consistent metrics |
References
- Kaggle Competitions
- Kaggle Notebooks – Digit Recognizer
- House Prices Dataset
- NLP with Disaster Tweets
- Kaggle Learn – Intro to Machine Learning
Goals for Next Week
- Review all weeks’ learnings and results
- Prepare final project summary and insights
- Create a portfolio section showcasing competition solutions
- Draft the final internship reflection
Screenshots (Optional)
Screenshots of Kaggle leaderboards, notebooks, and result plots from CNN and BERT models.
“Week 18 transformed learning into performance — every competition became a classroom, every submission a lesson.”