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Wine Quality Prediction Model – Project Wiki Overview

This project predicts whether red wine is Good or Bad based on 11 physicochemical properties using multiple machine learning models. It is designed as a complete, end-to-end learning resource covering data exploration, preprocessing, dimensionality reduction, model training, and evaluation.

1. Roadmap Phase 1: Data Understanding

Import and inspect the Red Wine Quality dataset

Explore class distribution

Identify patterns, correlations, and feature behavior

Phase 2: Data Preparation

Convert quality scores into binary labels

Handle scaling using StandardScaler

Split data into training and testing sets

Apply PCA for dimensionality reduction

Phase 3: Model Development

Train and compare models:

Logistic Regression

SVM (Linear)

SVM (RBF)

Random Forest

Phase 4: Evaluation

Compare metrics: Accuracy, Precision, Recall, F1 Score

Analyze strengths and weaknesses of each model

Identify the best-performing algorithm

Phase 5: Documentation & Improvements

Add visualizations and explanations in the notebook

Plan enhancements such as hyperparameter tuning, cross-validation, and advanced models (XGBoost, ensembles)

Prepare for deployment in Flask/FastAPI

2. Current Status Core Progress

Dataset fully explored and validated

Binary classification pipeline implemented

PCA applied to reduce dimensionality

Four ML models trained and tested

Model comparison table generated

Jupyter notebook thoroughly documented

Stability

No missing data

Deterministic results (fixed random_state)

Clean and reproducible workflow

What’s Working Well

RBF SVM and Random Forest show strong performance

PCA significantly speeds up training

Visualizations clearly highlight data characteristics

Pending Enhancements

Hyperparameter tuning

K-fold cross validation

Improved feature selection

Possible deployment as an API

3. Project Documentation Objectives

Build an interpretable ML pipeline

Compare traditional classification algorithms

Demonstrate good ML practices (no data leakage, proper scaling, reproducibility)

Offer a beginner-friendly guide to understanding real-world datasets

Features

End-to-end ML workflow in a single Jupyter notebook

Detailed EDA with visualizations

Feature engineering and PCA

Model training and evaluation using multiple algorithms

Clear metric-based comparison

Dataset Information

1,599 red wine samples

11 chemical features (pH, acidity, alcohol, density, etc.)

Binary classification threshold:

Good > 6.5

Bad ≤ 6.5

Clean dataset with no missing values

Tools & Technologies

Python

NumPy, Pandas

Matplotlib, Seaborn

Scikit-learn

Jupyter Notebook

4. Best Practices Followed

Train-test split before preprocessing

Scaler fit only on training data

PCA applied after scaling

Evaluation based solely on unseen data

All experiments made reproducible with fixed seeds

5. Future Directions Short-Term Enhancements

GridSearchCV or RandomizedSearchCV tuning

K-fold cross-validation

Additional visualizations (ROC, Precision-Recall curves)

Long-Term Enhancements

Deployment-ready API using Flask/FastAPI

UI integration with Streamlit

Experiment tracking using MLflow

Model monitoring and automated retraining

6. Summary

This project serves as a practical guide to understanding how classical ML models behave on a real-world dataset. It walks through each stage of the pipeline with clarity, offering a structured foundation for anyone learning machine learning or preparing for more advanced projects.