DataBricks Notebook Showcase

Explore three of my Databricks notebooks that exemplify the application of machine learning and artificial intelligence. Each notebook provides a detailed walkthrough of different techniques and solutions, demonstrating the power of Databricks in advancing ML and AI projects.

Machine learning feature engineering

Binarizer, PCA Principal Component Analysis, Normalizer, Standardization, Binning

Machine Learning analytical workflows

Machine Learning Pipelines for the automation for workflow of machine learning models from data preparation to training and inference.

ML - AI data transformation techniques

Together, String Indexer and OneHotEncoder are specifically used for encoding categorical data, transforming it into a format that machine learning algorithms can understand, while the Chi Square test is used for hypothesis testing related to the independence of variables in a dataset.

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