Solving One Hot Encoding Issues with Duplicate Columns in Python

Learn how to handle duplicate columns in One Hot Encoding using Python and LightGBM with a practical example.
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Solving One Hot Encoding Issues with Duplicate Columns in Python

When working with datasets, it's common to encounter challenges that can derail your data analysis or machine learning projects. One such issue is dealing with duplicate columns during the One Hot Encoding (OHE) process, especially when those duplicates stem from similar names across different categorical variables. Let's explore this problem in detail using a real-world scenario involving cities and provinces in Belgium.

The Problem at Hand

Suppose you have a dataset containing columns for City and Province Name. In certain regions, like Belgium, both a city and a province can bear the same name. For instance, we have Limburg as both a city and a province, leading to a conflict during the model training phase with LightGBM. Here's the error you might encounter:

[[See Video to Reveal this Text or Code Snippet]]

Understanding One Hot Encoding

How to Resolve the Duplicate Columns Issue

To circumvent this problem, we need to adjust the names of the conflicting columns so that each One Hot Encoded feature remains unique. Here's how to accomplish that step by step:

Step 1: Import Necessary Libraries

Firstly, ensure that you have the required library imported:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Modify the Conflicting Column Names

When performing One Hot Encoding, we can rename the conflicting column directly during the encoding process. For our example of Limburg, we want to differentiate the city from the province by appending a label to the city name.

Step 3: Implement One Hot Encoding

Here's an updated version of the code that demonstrates this solution:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

Rename the Conflicting Value: The rename function is used to change 'Limburg' to 'Limburg (city)'. This small change ensures that the city name does not conflict with the province name during the encoding process.

Conclusion

By carefully renaming the conflicting categories during the One Hot Encoding process, we can effectively avoid duplicate column errors when using machine learning libraries like LightGBM. This approach not only helps in maintaining the integrity of the data but also enhances the model's ability to learn from unique features.

With this solution, you can continue working on your dataset confidently, knowing that you've resolved potential encoding issues. Happy coding!