It is a Machine Learning Model used to predict wine quality using linear regression only.
The following code has been performed on Jupyter Notebook.
Here's a brief overview of the libraries I used in this project, which played a crucial role in its success:
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NumPy: This fundamental library for numerical computing allowed me to efficiently work with large datasets and perform various mathematical operations.
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Pandas: Pandas was my go-to for data manipulation and analysis. It made handling and cleaning the dataset a breeze, enabling me to extract meaningful insights.
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Matplotlib: Matplotlib helped me visualize the data, making it easier to understand trends, patterns, and correlations. Clear and informative visualizations were key to this project's success.
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Scikit-Learn: This machine learning library provided a wide range of tools for modeling and evaluating predictive algorithms. I used various regression models to predict house prices accurately.
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Jupyter Notebook: Jupyter Notebook served as my interactive coding environment, allowing me to document my work step by step and share it with my team.
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Sklearn: Simple and efficient tools for data mining and data analysis. It provides various supervised and unsupervised learning algorithms, including classification, regression, clustering, dimensionality reduction, model selection, and pre-processing.
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Seaborn: High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations. Specialized support for using categorical variables to show observations or aggregate statistics. Functions for visualizing univariate and bivariate distributions and for comparing them between subsets of data.