https://github.com/anuj-rai-23/model-ananlysis-and-red-wine-quality-prediction

An attempt to study various ML models for predicting the quality of Red Wine using various performance measures.
https://github.com/anuj-rai-23/model-ananlysis-and-red-wine-quality-prediction

accuracy-metrics decision-tree decision-tree-classifier f1-score gradient-boosting-classifier ipynb ipynb-jupyter-notebook machine-learning performance-measures python random-forest recall-score support-vector-machines svm-classifier wine-quality

Last synced: about 1 month ago
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An attempt to study various ML models for predicting the quality of Red Wine using various performance measures.

• Host: GitHub
• URL: https://github.com/anuj-rai-23/model-ananlysis-and-red-wine-quality-prediction
• Owner: anuj-rai-23
• Created: 2020-08-24T01:22:15.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-24T01:37:34.000Z (over 4 years ago)
• Last Synced: 2025-04-10T20:58:46.204Z (about 1 month ago)
• Topics: accuracy-metrics, decision-tree, decision-tree-classifier, f1-score, gradient-boosting-classifier, ipynb, ipynb-jupyter-notebook, machine-learning, performance-measures, python, random-forest, recall-score, support-vector-machines, svm-classifier, wine-quality
• Language: Jupyter Notebook
• Homepage:
• Size: 387 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Model-Ananlysis-and-Red-Wine-Quality-Prediction

An attempt to study various ML models for predicting the quality of Red Wine using various performance measures.


A project for [Machine Learning course (CS-503)](http://cse.iitrpr.ac.in/ckn/courses/s2020/cs503/cs503.html) done under supervision of [Dr. CK Narayanan](http://cse.iitrpr.ac.in/ckn/)

## Dataset 

[Kaggle Red Wine Quality](https://www.kaggle.com/sgus1318/winedata?select=winequality_red.csv)

Dataset is heavily biased towards 3 classes. Converted it into a two class dataset and performed binary classification.

## ML Models used



  
Decision Tree

  
Random Forests

  
Random Gradient Boost

  
Support Vector Machine

  


## Performance Measures



  
Train Accuracy

  
Test Accuracy

  
F1-Score

  
Precision Score

  
Recall 

  

  

 For all details of feature selection, model analysis and results see [notebook](/WineQualityPrediction.ipynb)