The focus is on predicting the quality of wine based on its chemical characteristics, offering a real-world application of machine learning in the context of viticulture. The dataset encompasses diverse chemical attributes, including density and acidity, which serve as the features for three distinct classifier models.
Kaggle here
- Classifier Models: Utilizing Random Forest, Stochastic Gradient Descent, and Support Vector Classifier (SVC) for wine quality prediction.
- Chemical Qualities: Analyzing features like density and acidity as predictors for wine quality.
- Data Analysis Libraries: Employing Pandas for data manipulation and Numpy for array operations.
- Data Visualization: Using Seaborn and Matplotlib for visualizing patterns and insights in the dataset.