Wine Quality Prediction Project

Overview

• This project focuses on exploring various data preparation techniques and their impact on the performance of a logistic regression model for predicting wine quality. By applying different transformations and feature selection strategies, the project aims to uncover insights into the complex relationship between wine characteristics and quality.

Data

• The dataset used in this project includes physicochemical properties of wines, such as acidity, sugar level, and alcohol content, along with a quality rating. The objective is to predict the quality score based on these properties.

Methodology

• The project follows a systematic approach, divided into two main phases:
  1. Data Transformation Phase: Different transformations like logarithmic, square root, and Box-Cox are applied to key features to observe their effect on model accuracy.
  2. Feature Selection Phase: Various combinations of features are selectively dropped from the dataset to identify their impact on the predictive power of the model.

Key Findings

• Minor improvements in model accuracy were observed with specific data transformations, indicating the potential of addressing feature skewness.
• The feature selection phase highlighted the importance of certain features over others and how their exclusion affects model performance.

Tools Used

• Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

How to Run the Project

• Clone the repository.
• Ensure Python and the required libraries are installed.
• Run the Jupyter Notebook to see the analysis and model building process.

Conclusion

• The project emphasizes the nuanced role of individual features and transformations in predictive modeling and provides a foundation for further research and exploration in wine quality prediction using machine learning techniques.