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Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

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4 horas16 vídeos52 exercícios
18.042 aprendizesTrophyDeclaração de Realização

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Descrição do Curso

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.
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  1. 1

    Kaggle competitions process

    Gratuito

    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.

    Reproduzir Capítulo Agora
    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.

    Reproduzir Capítulo Agora
    Understand the problem
    50 xp
    Understand the problem type
    50 xp
    Define a competition metric
    100 xp
    Initial EDA
    50 xp
    EDA statistics
    100 xp
    EDA plots I
    100 xp
    EDA plots II
    100 xp
    Local validation
    50 xp
    K-fold cross-validation
    100 xp
    Stratified K-fold
    100 xp
    Validation usage
    50 xp
    Time K-fold
    100 xp
    Overall validation score
    100 xp
  3. 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.

    Reproduzir Capítulo Agora
    Feature engineering
    50 xp
    Arithmetical features
    100 xp
    Date features
    100 xp
    Categorical features
    50 xp
    Label encoding
    100 xp
    One-Hot encoding
    100 xp
    Target encoding
    50 xp
    Mean target encoding
    100 xp
    K-fold cross-validation
    100 xp
    Beyond binary classification
    100 xp
    Missing data
    50 xp
    Find missing data
    100 xp
    Impute missing data
    100 xp
  4. 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.

    Reproduzir Capítulo Agora
    Baseline model
    50 xp
    Replicate validation score
    100 xp
    Baseline based on the date
    100 xp
    Baseline based on the gradient boosting
    100 xp
    Hyperparameter tuning
    50 xp
    Grid search
    100 xp
    2D grid search
    100 xp
    Model ensembling
    50 xp
    Model blending
    100 xp
    Model stacking I
    100 xp
    Model stacking II
    100 xp
    Final tips
    50 xp
    Testing Kaggle forum ideas
    100 xp
    Select final submissions
    50 xp
    Final thoughts
    50 xp
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conjuntos de dados

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colaboradores

Collaborator's avatar
Hillary Green-Lerman
Collaborator's avatar
Hadrien Lacroix

pré-requisitos

Extreme Gradient Boosting with XGBoost
Yauhen Babakhin HeadshotYauhen Babakhin

Kaggle Grandmaster

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