This repository contains Jupyter notebooks summarizing the Kaggle Learn courses. Each notebook corresponds to a specific course and provides a concise summary of the key concepts, techniques, and code covered in that course.
- Intermediate ML: Cross Validation, XGBoost, and Data Leakage
- Data Visualisation Techniques: Line, Scatter, Histograms, and KDE plots
- Data Cleaning Techniques: Dropping, Dealing with dates, Character Encoding, Inconsistent Entries, and more
- Feature Engineering Techniques: MI, K-clustering, PCA, and Target Encoding
- Python 3.x
- Jupyter Notebook
The course summaries in this repository are based on the Kaggle Learn courses, which are available at www.kaggle.com/learn. Special thanks to Kaggle for providing the valuable learning resources.