This repository showcases various data preparation techniques in Python. The focus is on preparing data for analysis, with a particular emphasis on techniques such as transforming categorical variables into numerical ones, detecting outliers using statistical methods, handling and modifying null values, instance selection, and variable selection.
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Categorical Variable Transformation:
- Explore methods for converting categorical variables to numerical representations.
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Outlier Detection:
- Utilize statistical techniques for identifying and handling outliers in the dataset.
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Handling Null Values:
- Learn strategies for detecting and modifying null values within the dataset.
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Instance Selection:
- Understand techniques for selecting specific instances or rows based on criteria.
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Variable Selection:
- Explore methods to choose relevant variables for analysis.
This work was conducted in Spanish, as it aligns with the language of the course in which the project was undertaken.
Feel free to explore the provided Jupyter notebook.
- Unai Gurbindo
- Ander Aquerreta
This project is licensed under the MIT License, which means you are free to use, modify, and distribute the code for both commercial and non-commercial purposes.
Enjoy exploring the world of data preparation in Python!