Wine Quality

Dataset Overview

Input Variables: Physicochemical properties (e.g., pH, alcohol content, acidity).
Output Variable: Sensory ratings (quality), which are ordered categories.

Tasks

Classification or Regression:

Treat the output as a categorical variable (classification) or as a continuous score (regression).
Outlier Detection:

Identify outliers (e.g., excellent or poor wines) using techniques like Isolation Forest or Local Outlier Factor (LOF).
Feature Selection:

Apply methods such as Recursive Feature Elimination (RFE), LASSO, or tree-based feature importance to identify relevant features.

Suggested Analysis Steps

Data Preprocessing:

• Handle missing values if any.
• Normalize or standardize input features for better model performance.

Exploratory Data Analysis (EDA):

• Visualize the distribution of quality ratings.
• Use pair plots or correlation heatmaps to understand relationships between features.

Modeling:

For Classification:

Try models like Logistic Regression, Decision Trees, Random Forest, or Gradient Boosting.

For Regression:

Use Linear Regression, SVR, or Tree-based models like Random Forest Regressor.

Evaluation:

• Use metrics like accuracy, F1-score, or ROC-AUC for classification.
• For regression, consider MAE, MSE, or R².

Feature Importance:

Analyze which features contribute the most to the predictions to aid in understanding the data.