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Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

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4 Hours16 Videos52 Exercises11,955 Learners4200 XPMachine Learning Scientist Track

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Course Description

Kaggle is the most famous platform for Data Science competitions. Taking part in such competitions allows you to work with real-world datasets, explore various machine learning problems, compete with other participants and, finally, get invaluable hands-on experience. In this course, you will learn how to approach and structure any Data Science competition. You will be able to select the correct local validation scheme and to avoid overfitting. Moreover, you will master advanced feature engineering together with model ensembling approaches. All these techniques will be practiced on Kaggle competitions datasets.

  1. 1

    Kaggle competitions process


    In this first chapter, you will get exposure to the Kaggle competition process. You will train a model and prepare a csv file ready for submission. You will learn the difference between Public and Private test splits, and how to prevent overfitting.

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    Competitions overview
    50 xp
    Explore train data
    100 xp
    Explore test data
    100 xp
    Prepare your first submission
    50 xp
    Determine a problem type
    50 xp
    Train a simple model
    100 xp
    Prepare a submission
    100 xp
    Public vs Private leaderboard
    50 xp
    What model is overfitting?
    50 xp
    Train XGBoost models
    100 xp
    Explore overfitting XGBoost
    100 xp
  2. 2

    Dive into the Competition

    Now that you know the basics of Kaggle competitions, you will learn how to study the specific problem at hand. You will practice EDA and get to establish correct local validation strategies. You will also learn about data leakage.

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  3. 4


    Time to bring everything together and build some models! In this last chapter, you will build a base model before tuning some hyperparameters and improving your results with ensembles. You will then get some final tips and tricks to help you compete more efficiently.

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In the following tracks

Machine Learning Scientist


hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixhillary-green-lermanHillary Green-Lerman
Yauhen Babakhin Headshot

Yauhen Babakhin

Kaggle Grandmaster

Yauhen holds a Master’s Degree in Applied Data Analysis and has over 5 years of working experience in Data Science. He worked in Banking, Gaming and eCommerce domains. Yauhen is also the first Kaggle competitions Grandmaster in Belarus having gold medals in both classic Machine Learning and Deep Learning competitions.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA