Wine Quality Prediction

Summary

This project explores the relationship between physicochemical properties of wines and their quality ratings, aiming to predict wine quality and identify key factors influencing it using machine learning models such as Decision Trees. Through exploratory data analysis (EDA), we examine patterns, distributions, and correlations, addressing challenges such as class imbalances in wine quality ratings. The Decision Tree model is evaluated using metrics like accuracy, precision, recall, and feature importance to uncover significant predictors, such as density, alcohol, and volatile_acidity. The primary goal is to build an interpretable machine learning pipeline that provides actionable insights for winemakers to optimize production processes and for consumers to make informed choices. Additionally, the project sets the foundation for future work, including incorporating sensory attributes, addressing dataset imbalances, and leveraging more advanced ensemble methods for better predictions.

Contributors:

• Chukwunonso Ebele-Muolokwu
• Samuel Adetsi
• Shashank Hosahalli Shivamurthy
• Ci Xu

Reproducible Computational Environment

This project ensures a reproducible computational environment using Conda. Follow the steps below to set up the environment for this project.

Prerequisites

1. Install Miniconda or Anaconda.

2. Clone this repository:

   git clone https://github.com/UBC-MDS/522-wine-quality-32.git
   cd 522-wine-quality-32

Setting Up the Environment

Option 1: Using environment.yaml

This is the recommended method to set up the environment.

1. Create the Conda environment:

   conda env create -f environment.yaml

2. Activate the environment:

   conda activate 522_milestone_env

3. Verify the environment setup:

   python -c "import pandas as pd; print('Environment set up successfully!')"

Option 2: Using Platform-Specific Lock Files

If you want to ensure reproducibility across different operating systems, use platform-specific lock files.

1. Install conda-lock:

   pip install conda-lock

2. Create the environment using the lock file for your platform:

   • For Linux Or macOS or Windows:

     conda-lock install --name 522_milestone_env conda-lock.yml

3. Activate the environment:

   conda activate 522_milestone_env

Updating the Environment

If the environment.yaml file is updated (e.g., new dependencies are added), you can update your existing environment with:

conda env update -f environment.yaml --prune

Cleaning Up

To remove the Conda environment:

conda env remove -n 522_milestone_env

Files Included for Reproducibility

• environment.yaml: Contains the dependencies required for the project.
• conda-linux-64.lock, conda-osx-64.lock, conda-win-64.lock: Platform-specific lock files for precise reproducibility.

Example Workflow

Here’s a typical workflow for setting up and testing the environment:

Clone the repository

git clone https://github.com/UBC-MDS/522-wine-quality-32.git
cd 522-wine-quality-32

Create and activate the environment

conda env create -f environment.yaml
conda activate 522_milestone_env

Test the environment

python -c "import pandas as pd; print('Environment set up successfully!')"

Remove the environment (if needed)

conda env remove -n 522_milestone_env