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Course

Winning a Kaggle Competition in Python

AdvancedSkill Level

4.8+

Updated 05/2026

Learn how to approach and win competitions on Kaggle.

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PythonMachine Learning4 hr16 videos52 Exercises4,200 XP21,461Statement of Accomplishment

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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.

Prerequisites

Extreme Gradient Boosting with XGBoost

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.

Competitions overview

Explore train data

Explore test data

Prepare your first submission

Determine a problem type

Train a simple model

Prepare a submission

Public vs Private leaderboard

What model is overfitting?

Train XGBoost models

Explore overfitting XGBoost

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.

Understand the problem

Understand the problem type

Define a competition metric

Initial EDA

EDA statistics

EDA plots I

EDA plots II

Local validation

K-fold cross-validation

Stratified K-fold

Validation usage

Time K-fold

Overall validation score

3

Feature Engineering

You will now get exposure to different types of features. You will modify existing features and create new ones. Also, you will treat the missing data accordingly.

Feature engineering

Arithmetical features

Date features

Categorical features

Label encoding

One-Hot encoding

Target encoding

Mean target encoding

K-fold cross-validation

Beyond binary classification

Missing data

Find missing data

Impute missing data

4

Modeling

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.

Baseline model

Replicate validation score

Baseline based on the date

Baseline based on the gradient boosting

Hyperparameter tuning

Grid search

2D grid search

Model ensembling

Model blending

Model stacking I

Model stacking II

Testing Kaggle forum ideas

Select final submissions

Final thoughts

Winning a Kaggle Competition in Python

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FAQs

Is this course appropriate for someone who has never entered a Kaggle competition?

Yes, but it is an advanced course. You should already be comfortable with pandas, XGBoost, scikit-learn, and basic statistics before enrolling.

What specific competition techniques will I learn in this course?

You will learn local validation schemes, overfitting prevention, advanced feature engineering, and model ensembling approaches, all practiced on real Kaggle competition datasets.

Does the course explain the difference between Public and Private leaderboard splits?

Yes. The first chapter covers the Kaggle competition process including how Public and Private test splits work and why understanding them helps prevent overfitting.

Which Python libraries are used throughout the course?

The course uses pandas for data manipulation, XGBoost for gradient boosting, scikit-learn for supervised learning, and standard Python statistics libraries.

Will I actually prepare a submission file during the course?

Yes. You will train models and prepare CSV files ready for Kaggle submission as part of the hands-on exercises in the first chapter.

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