Kaggle Training

3 hours ago

Estimated Reading Time: 6 mins

Published: Dec 17, 2018

1. Choose a data science programming environment. There are many machine learning programming environments to choose from, and you may end up using a number of them, but to get started with Kaggle you just need to choose one.
2. Practice on commonly used test data sets. Once you get up to speed with a language, you need to start practicing on actual data sets. It’s a good idea to set up some realistic exercises to gain experience with simple, well-known data sets.
3. Explore many facets of data transformation. Data transformation (aka data wrangling, or data munging) involves various forms of data prep including merging data, aggregating data, cleansing data, handle missing data, making data consistent, and so much more.
4. Feature engineering is king. Feature engineering is when you choose the best predictors for your problem in terms of predictive power. It’s been reported many times that Kaggle challenges are won with clever feature engineering, and not with the most sophisticated algorithms.
5. Learn how to use ensembles. Ensembles refer to statistical learning algorithms that achieve enhanced predictive performance by constructing a set of classifiers and then classifying new data points by taking a weighted vote of their predictions.
6. Learn how to beat overfitting. Overfitting refers to models that perform well on the training set, but not as well on the test set. In the Kaggle system, this extends to the scores you see on the leaderboard.
7. Make use of the forum. The Kaggle user forums represent an excellent learning resource. Just browsing through the conversations can lead to insights. Feel free to ask questions, and you’ll be surprised at all the well-crafted answers you’ll receive.
8. Develop your own Kaggle toolbox. Build a special Kaggle toolbox with a variety of tools consisting of commonly used code sequences. With practice, you’ll become efficient when using these tools.
9. Practice on past Kaggle challenges. Now that you possess a good level of familiarity with your tools and how to use them, it’s time to practice on past Kaggle challenges.
10. Start competing! If you’ve found success with all the above tips, you’re now ready to compete on Kaggle. Consider working on one challenge at a time until you achieve a good score or hit a roadblock.

Website: opendatascience.com/10-tips-to-get-started-with-kaggle/