wine_quality

In this project we will classify a wine in different quality classes, using its physicochemical properties.

Wine quality classification for unbalanced data

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Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. References
5. Contact

About The Project

This dataset is highly unbalanced, for which the main goal will be to compare a Random Forest Classifier performance, when using data augmentation techniques (SMOTE).

Built With

• Anaconda 4.10.1
• Python 3.9

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

A running installation of Anaconda. If you haven't installed Anaconda yet, you can follow the next tutorial:
Anaconda Installation

Installation

1. Clone the repo

   git clone https://github.com/loremendez/wine_quality.git

2. Install the environment
   You can do it either by loading the YML file

   conda env create -f conda_environment.yml

   or step by step
   1. Create and activate the environment

      conda create -n wine_env python=3.9
      conda activate wine_env

   2. Install the needed packages

      pip install --upgrade pip
      pip list  # show packages installed within the virtual environment
      
      pip install numpy pandas matplotlib seaborn scikit-learn imbalanced-learn
      pip install jupyterlab

Usage

Open Jupyter-lab and open the notebook Random_Forest.ipynb to see the classifier's performance on the original dataset, or open the notebook Random_Forest_SMOTE.ipynb to see the classifier's performance on the augmented dataset.

jupyter-lab

References

[1] Dataset by Cortez, Paulo (@LSIND) “Wine Quality Data Set”. Last updated: 2020-04-27. Link UCI Machine Learning Repository: [https://archive.ics.uci.edu/ml/datasets/wine+quality) Link Kaggle: https://www.kaggle.com/datasets/yasserh/wine-quality-dataset

Contact

Lorena Mendez - LinkedIn - lorena.mendez@tum.de

Take a look into my other projects!