This project focuses on the application of machine learning techniques to predict the quality of red wine based on various parameters. It implements Logistic Regression from scratch, showcasing the underlying mechanics of this algorithm while also utilizing the Scikit-Learn library for streamlined model training and evaluation.
The predictive model allows users to input their own data regarding different wine parameters, enabling them to receive quality predictions based on the statistics provided. The user-friendly interface enhances accessibility, making it easier for both novice and experienced users to engage with the model. Through this project, users can gain insights into how specific characteristics influence wine quality and make informed decisions based on the predictions generated.
A thorough exploration of the Logistic Regression algorithm, implemented from scratch to enhance understanding of its functionality.
Utilization of Scikit-Learn’s robust library for model training, evaluation, and optimization, demonstrating best practices in machine learning.
An intuitive interface that allows users to input data easily and obtain quality predictions, making machine learning accessible to all.