Red Wine Quality Prediction

A machine learning project to predict the quality of red wine using physicochemical properties and sensory data.

Overview

This project utilizes a dataset of Portuguese "Vinho Verde" red wine to explore the relationship between physicochemical attributes and wine quality. By applying machine learning algorithms, the goal is to predict wine quality on a scale of 0 to 10.

The dataset is sourced from Kaggle and contains physicochemical measurements (e.g., acidity, pH) as input variables and wine quality scores as the target variable. These tasks can be approached as regression or classification problems.

Dataset on Kaggle

Technologies Used

The project leverages the following Python libraries:

• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Matplotlib & Seaborn: For data visualization and exploratory analysis.
• Scikit-learn: (Optional, if used later) For implementing machine learning models.

Additional tools include Jupyter Notebooks for an interactive coding environment.

Dataset Description

The dataset includes physicochemical input variables and one output variable:

Input Variables

• Fixed acidity
• Volatile acidity
• Citric acid
• Residual sugar
• Chlorides
• Free sulfur dioxide
• Total sulfur dioxide
• Density
• pH
• Sulphates
• Alcohol

Output Variable

• Quality: A score between 0 (poor) and 10 (excellent) based on sensory evaluation.

How to Use

Step 1: Clone the Repository

git clone https://github.com/your-username/red-wine-quality-prediction.git
cd red-wine-quality-prediction

Step 2: Install Dependencies

Ensure you have Python installed, then install the required libraries:

pip install pandas numpy matplotlib seaborn

Step 3: Run the Notebook

The analysis and visualizations are in the Init.ipynb file. Open the file in Jupyter Notebook or Jupyter Lab:

jupyter notebook main.ipynb

Step 4: Explore the Code

Inside the notebook, you’ll find:

• Data loading and preprocessing steps.
• Exploratory data analysis (EDA) with visualizations.
• Basic insights derived from the dataset.

References

• [Cortez et al., 2009]
• Dataset available at Kaggle.