This is the final capstone project for the Advanced Data Science with IBM Specialization certificate, on Coursera.

The main goal is to explore the UCI wine dataset an study several use cases using different machine learning and deep learning algorithms.

The structure of the project is as follows:

• Assests: This folder contains images and other files used in the presentation (a media folder).
• Data: This folder contains the UCI wine dataset and the preprocessed data.
• Docs: This folder contains the ADD report and the presentation.
• UnravalingWineQualityPatterns_DataExploration.ipynb: Data visualization and analysis of the raw dataset.
• UnravalingWineQualityPatterns_ETL.ipynb: Data preprocessing and feature engineering.
• UnravalingWineQualityPatterns_Model.ipynb: Model training and evaluation.
  • Use case 1: Predicting wine quality using four different regression ML algorithms.
  • Use case 2: Classifying wine by color using four different classification ML algorithms.
  • Use case 3: Predicting wine quality using a DL pytorch neural network.
  • Use case 4: Classifying wine by color using a DL pytorch neural network.