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Wine, a beverage celebrated for its complexity and depth of flavors, has been a symbol of sophistication and pleasure for centuries. With the myriad of wine options available, enthusiasts and connoisseurs often find themselves navigating through a vast sea of choices. In the pursuit of understanding and evaluating wine quality, this project aims to develop a Wine Quality Index that provides a comprehensive measure of the overall excellence of different wines.
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The appreciation of wine involves a nuanced understanding of various factors, including grape variety, winemaking techniques, and regional influences. Leveraging advancements in data science and machine learning, our project seeks to analyze a diverse dataset encompassing key attributes of wines, such as acidity, sweetness, alcohol content, and more. By employing predictive modeling and statistical analysis, we aim to create a robust index that encapsulates the diverse facets contributing to the perceived quality of a wine.
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This project not only serves the interests of seasoned wine enthusiasts but also caters to individuals exploring the world of wines for the first time. The Wine Quality Index will be a valuable tool for consumers, assisting them in making informed decisions based on their preferences and taste profiles. Moreover, winemakers and vineyard owners can benefit from the insights derived from the index, guiding them in refining their production processes to meet evolving consumer expectations.
For running the code, make sure that the following are installed on your local device.
| Module | Version |
|---|---|
| Python | 3.11.x |
| numpy | 1.26.x |
| Pandas | 2.1.x |
| matplotlib | 3.8.x |
| seaborn | 0.13.x |
| scikit | 1.3.x |
- Dataset
- The Wine Quality Dataset on Kaggle likely contains information about various attributes of wines along with associated quality ratings.
- Clone this repo.
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git clone https://github.com/Ekanth-Sai/Wine-Quality_Index
- Install the required libraries from Requirements
- Execute the python script
- Add star to this repo if you liked it 😄
This section provides instructions and details on how to submit a contribution via a pull request. It is important to follow these guidelines to make sure your pull request is accepted.
- Before choosing to propose changes to this project, it is advisable to go through the readme.md file of the project to get the philosphy and the motive that went behind this project. The pull request should align with the philosphy and the motive of the original poster of this project.
- To add your changes, make sure that the programming language in which you are proposing the changes should be same as the programming language that has been used in the project. The versions of the programming language and the libraries(if any) used should also match with the original code.
- Write a documentation on the changes that you are proposing. The documentation should include the problems you have noticed in the code(if any), the changes you would like to propose, the reason for these changes, and sample test cases. Remember that the topics in the documentation is strictly not limited to the topics aforementioned, but are just an inclusion.
- Submit a pull request via Git etiquettes