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Categorical variables — Machine Learning Zoomcamp Module 2

Categorical variables are typically represented as strings, and pandas identifies them as object types. However, some variables that appear to be numerical may actually be categorical (e.g., the number of doors a car has). All these...

Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Categorical variables

Notes

Categorical variables are typically represented as strings, and pandas identifies them as object types. However, some variables that appear to be numerical may actually be categorical (e.g., the number of doors a car has). All these categorical variables need to be converted to a numerical form because ML models can interpret only numerical features. It is possible to incorporate certain categories from a feature, not necessarily all of them. This transformation from categorical to numerical variables is known as One-Hot encoding.

The entire code of this project is available in this jupyter notebook.

• Notes from Peter Ernicke

Comments

This way of encoding categorical features is called “one-hot encoding”. We’ll learn more about it in Session 3.

Key concepts

• Pandas
• Jupyter Notebooks

Related notes

• mlz-m01-introduction-to-numpy
• mlz-m01-introduction-to-pandas
• mlz-m01-linear-algebra-refresher
• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-car-price-prediction-project
• mlz-m02-car-price-prediction-project-summary
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-data-preparation
• mlz-m02-exploratory-data-analysis
• mlz-m02-explore-more
• mlz-m02-feature-engineering
• mlz-m02-linear-regression
• mlz-m02-linear-regression-vector-form
• mlz-m02-regularization
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-setting-up-the-validation-framework
• mlz-m02-training-linear-regression-normal-equation
• mlz-m02-tuning-the-model
• mlz-m02-using-the-model
• mlz-m03-data-preparation
• mlz-m03-eda
• mlz-m03-feature-importance-churn-rate-and-risk-ratio
• mlz-m03-feature-importance-correlation
• mlz-m03-feature-importance-mutual-information
• mlz-m03-logistic-regression
• mlz-m03-model-interpretation
• mlz-m03-one-hot-encoding
• mlz-m03-setting-up-the-validation-framework
• mlz-m03-training-logistic-regression-with-scikit-learn
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-data-cleaning-and-preparation
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-gradient-boosting-and-xgboost
• mlz-m06-xgboost-parameter-tuning
• mlz-m08-installation-of-tensorflow
• mlz-m10-creating-a-pre-processing-service

Sources

• Video
• Lesson file