https://github.com/nirmalyabag20/wine-quality-prediction-machine-learning

This project analyzes the chemical properties of wines to identify key factors influencing quality. By leveraging machine learning techniques, i aim to develop predictive models that accurately classify wine quality, providing valuable insights for producers and enthusiasts alike.
https://github.com/nirmalyabag20/wine-quality-prediction-machine-learning

k-neighbors-classifier logistic-regression machine-learning matplotlib numpy pandas python random-forest seaborn svc

Last synced: 10 days ago
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This project analyzes the chemical properties of wines to identify key factors influencing quality. By leveraging machine learning techniques, i aim to develop predictive models that accurately classify wine quality, providing valuable insights for producers and enthusiasts alike.

• Host: GitHub
• URL: https://github.com/nirmalyabag20/wine-quality-prediction-machine-learning
• Owner: nirmalyabag20
• Created: 2024-09-05T05:26:03.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-05T16:13:56.000Z (2 months ago)
• Last Synced: 2024-09-06T11:02:22.553Z (2 months ago)
• Topics: k-neighbors-classifier, logistic-regression, machine-learning, matplotlib, numpy, pandas, python, random-forest, seaborn, svc
• Language: Jupyter Notebook
• Homepage:
• Size: 3.94 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Wine Quality Prediction

Overview~

This project leverages machine learning to predict the quality of wine based on its physicochemical properties. The objective is to develop a robust classification model that aids in determining wine quality, offering value to vintners, distributors, and consumers alike.

Tools~

   Python, NumPy, pandas, scikit-learn, matplotlib, seaborn

Approach~

   •	Data Preprocessing: Cleaned and standardized data for model training.

   •	EDA: Visualized relationships between chemical features and wine quality.

   •	Modeling: Tested RandomForestClassifier model.

   •	Evaluation: Assessed models using accuracy_score.

Results~

   •	Model: RandomForestClassifier model with 90% accuracy

   •	Insights: Key factors like alcohol and acidity affect wine quality.