Step-by-step guide to win Kaggle competitions
Kaggle–an online community for data scientists to upskill, build street cred and make a quick buck– hosts competitions with prize rewards up to $50,000. However, getting on Kaggle leaderboards calls for patience, hard work, and constant practice. Keep in mind, the platform is home to world’s brightest data scientists. To become a grandmaster, you need a high level of dedication and subject matter knowledge. Here, we give a quick overview of how to win a Kaggle competition. Step 1 Learn the rules of the game until you have them down cold. To make inroads, you should understand the ins and outs of the competition, including summary, description, timeline, evaluation, eligibility criteria, and the prize. Small factors, such as a competition’s timeline, might prove deal-breakers. Do not begin working on a Kaggle competition until you have all the instructions by heart. Look before you leap. Step 2 The second step is taking stock of the performance metrics. Seasoned Kagglers use an optimised method tailored to a specific measure to improve the score. Because the terms Mean Square Error (MSE) and Mean Absolute Error (MAE) are similar, failing to understand the difference will lower your final score. Step 3 The third step is to fully comprehend the facts. To uncover missing and null values and hidden patterns in the dataset, start with exploratory data analysis. The more information you have about the data, the better models you can build. It is also important to know the data and have deep knowledge of the model to ace the competition. Kaggle Master, Mark Tenenholtz, shares some important tricks: Write convenience functions: These are functions that do common data transforms, visualization, error analysis, etcWrite error analysis code- So not skip the step due to code fatigue as it goes a long wayExploratory data analysis- Know your data to the coreSolution engineering- plan and brainstorm your approach Step 4 The most important step is to create your local validation environment. Instead of relying entirely on leaderboard scores, you will be able to create consistent results. You can run the submission as many times as you like in your environment, and you are not limited to five submissions per day in Kaggle tournaments. You can enter a live competition once you’re satisfied with the results. It provides you with a significant advantage over competitors who do not have their local ecosystems set up. Step 5 Discussion boards and forums are your best friends. Join the forum to receive notifications about the competition you’re in. In addition, the forum will keep you up to date on what your competitors are up to. The host also shares their thoughts and suggestions about the tournament more frequently on the forum. Step 6 Research is key! Codes, benchmarks, official business blogs, and extensive published papers or patents are frequently available to those who host such competitions. Even if you don’t win the first few times, you’ll learn from your mistakes, improve your skills, and become a better data scientist. Step 7 It’s time to put together some ensemble models. It just entails integrating all of the models you’ve created on your own. Different teams generally get together in high-profile events to merge their models to improve their scores. Because no competition on Kaggle has ever been won by a single model, it’s a good idea to combine multiple independent models even if you’re riding solo. Step 8 The final step is to choose the best approach. Neural/Deep Learning Networks and Feature Engineering have consistently emerged as the go-to tactics in Kaggle’s tournaments. Choose your approach wisely! We hope this helps you ace Kaggle competitions, or at the very least make you a better data scientist. Read More: Interview With Kaggle Triple Grandmaster Rob Mulla
Neural/Deep Learning Networks and Feature Engineering have consistently emerged as the go-to tactics in Kaggle’s tournaments.
Neural/Deep Learning Networks and Feature Engineering have consistently emerged as the go-to tactics in Kaggle's tournaments.
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["Go", "programming_languages:R", "AI", "feature engineering", "programming_languages:Go", "deep learning", "R"]
["AI", "deep learning", "R", "Go", "feature engineering", "programming_languages:R", "programming_languages:Go"]
Abhishree is a budding tech journalist with a UGD in Political Science. In her free time, Abhishree can be found watching French new wave classic films and playing with dogs.