https://github.com/daksh90a/wine-quality-analysis
The Wine Quality Analysis project is an AI/ML-based data analysis initiative aimed at predicting and understanding the factors that influence the quality of wine.
https://github.com/daksh90a/wine-quality-analysis
matplotlib-python numpy pandas seaborn
Last synced: 7 months ago
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The Wine Quality Analysis project is an AI/ML-based data analysis initiative aimed at predicting and understanding the factors that influence the quality of wine.
- Host: GitHub
- URL: https://github.com/daksh90a/wine-quality-analysis
- Owner: Daksh90a
- Created: 2025-02-10T12:09:43.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-10T12:14:34.000Z (9 months ago)
- Last Synced: 2025-04-05T19:36:07.958Z (7 months ago)
- Topics: matplotlib-python, numpy, pandas, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 184 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
Wine Quality Analysis - AI/ML Project
Project Description:
The Wine Quality Analysis project is an AI/ML-based data analysis initiative aimed at predicting and understanding the factors that influence the quality of wine. Using Python and key data science libraries such as Pandas, NumPy, Seaborn, and Matplotlib, this project performs exploratory data analysis (EDA), visualization, and machine learning modeling to assess wine quality based on various physicochemical properties.
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Objectives:
Analyze wine quality datasets to identify key factors affecting wine ratings.
Visualize data trends and correlations using Seaborn and Matplotlib.
Preprocess and clean data using Pandas and NumPy.
Build ML models (e.g., Logistic Regression, Random Forest, or Decision Trees) to predict wine quality.
Evaluate model performance using metrics like accuracy, precision, and recall.
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Technologies & Libraries Used:
Python (Primary programming language)
Pandas & NumPy (Data manipulation and preprocessing)
Seaborn & Matplotlib (Data visualization and correlation analysis)
Scikit-learn (Machine learning model training and evaluation)
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Expected Outcomes:
A clear understanding of wine quality factors and their impact.
Accurate ML models capable of predicting wine quality based on input features.
Insightful data visualizations to interpret trends and relationships in the dataset.
This project is ideal for data enthusiasts and machine learning practitioners interested in data-driven decision-making in the food and beverage industry. 🍷📊