"A comprehensive exploration of machine learning models using label encoding to preprocess categorical data, featuring implementations in Python with sklearn."
This Jupyter notebook contains the implementation of various machine learning models utilizing label encoding techniques to handle categorical data. The primary focus is to demonstrate the effectiveness of label encoding in preprocessing steps for machine learning tasks.
The notebook explores different ML models to predict outcomes based on categorical data that has been transformed using label encoding. It serves as an educational tool for understanding how label encoding works and its impact on model performance.
- pandas for data manipulation
- sklearn for implementing machine learning models and preprocessing
- matplotlib for visualizations
To run this notebook, ensure you have Jupyter installed and the above libraries available. It is designed for educational purposes and can be modified to fit specific datasets or model configurations.
Feel free to fork this repository or submit a pull request if you have suggestions for improvements or additional models to include.