August 11, 2025
I completed my first Kaggle competition using Ryan’s 3-hour tutorial from Ryan and Matt Data Science. While long, it’s invaluable for beginners like me.
The process involved three main steps:
1. Data cleaning
• Visualized every feature (input variables) as scatterplots and boxplots
• Identified patterns and removed outliers (data points that don’t fit the trend)
• Ensures models learn from clean, representative data
2. Feature engineering
• Created new features from existing ones (like house_age = YearSold - YearBuilt)
• Normalized target values to improve model performance
• Converted text categories into numbers that algorithms can process
3. Model building
• Split data into training and testing portions
• Tested different regression models (random forest, gradient boosting, etc.)
• Measured success using root mean squared error (how far off predictions were from actual values)
My main takeaway
Success in ML heavily depends on knowing what methods exist for your problem type. Implementation becomes straightforward once you understand your options. Following this tutorial gave me the blueprint, but for future competitions, I need to develop intuition for choosing approaches independently.