Red_Wine_Quality_Prediction_Project

This project demonstrates a regression analysis to predict the quality of red wine. Here 'quality' is the Output column. It utilizes Decision Tree Regression and Adaptive Boosting Regression techniques, implements K-Fold Cross Validation for robust evaluation, and showcases various performance metrics.

Features

• Predicts red wine quality using Decision Tree Regression and Adaptive Boosting Regression.
• Utilizes K-Fold Cross Validation with 10 splits for comprehensive model evaluation.
• Evaluates models using key performance metrics, including MAE, R2, Explained Variance, and Max Error.
• Compares model performance using visual plots.

Setup and Usage

1. Download the 'RedWineQuality.csv' dataset.
2. Open the project in a Jupyter Notebook or Google Colab environment.

Requirements

• Python3
• Scikit-Learn
• Matplotlib
• Pandas
• NumPy

Results

• Key metrics such as MAE, R2, Explained Variance, and Max Error are used to evaluate and compare between Decision Tree Regression and Adaptive Boosting Regression.
• Adaptive Boosting Regression is the more accurate model.