This project aims to predict the quality of wine based on various chemical properties using an ElasticNet model, which is a linear regression technique that combines both L1 (Lasso) and L2 (Ridge) regularization. The project is designed to showcase an end-to-end machine learning workflow, including data ingestion, validation, transformation, model training, evaluation, and deployment.
1. Data Ingestion
In this step, load the wine dataset, which includes attributes such as acidity, sugar levels, pH, alcohol content, and more. The data is sourced from Kaggle the Wine Quality dataset2. Data Validation
Once the data is ingested, it undergoes validation to check for consistency and accuracy. Ensure that the data types are correctly set and the dataset is clean and reliable for further analysis.3. Data Transformation
This includes: - **Normalization**: Scaling numeric features to bring them to a similar range. - **Feature Engineering**: Creating new features if necessary, or removing irrelevant ones. - **Train-Test Split**: Splitting the data into training and testing sets to evaluate the model’s performance on unseen data.4. Model: ElasticNet Regression
The **ElasticNet** model for predicting wine quality. ElasticNet is chosen due to its ability to handle feature selection and regularization. This model is a hybrid of Lasso and Ridge regression: - **L1 (Lasso)**: Helps in feature selection by adding a penalty. - **L2 (Ridge)**: Avoid overfitting.5. Model Evaluation
The performance of the ElasticNet model is evaluated on the test set. Various metrics such as **MSE**, **R²**, and **Cross-Validation Score** are reported to analyze the model’s predictive power. Hyperparameter tuning using **Grid Search** or **Random Search** is performed to optimize model parameters such as the alpha value for regularization.This project is a complete end-to-end machine learning pipeline implementation, demonstrating all the steps to successfully build, train, and evaluate a predictive model for wine quality classification. The ElasticNet model is particularly effective in this scenario, as it provides an optimal solution when both feature selection and regularization