This project focuses on analyzing datasets provided by Blueberry Winery, a portuguese wine producer company. Through a series of data wrangling, data cleaning, exploratory data analysis (EDA), and machine learning (ML) techniques, valuable insights were extracted from the data.
- Conducted thorough data wrangling and data cleaning to ensure data quality and accuracy.
- Performed exploratory data analysis (EDA) to uncover patterns, trends, and relationships within the dataset.
- Utilized machine learning algorithms to develop predictive models and gain deeper insights.
- Created visually appealing and informative geographical visualizations to better understand the data and consumer behavior.
By applying these techniques, we gained valuable insights into wine quality producing patterns, chemichal correlations between different quality wines, and other important factors that can drive business decisions for Blueberry Winery.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
To get a comprehensive overview of the project, please check out the presentation I created on Google Slides.
This project serves as a demonstration of my data analysis skills and showcases my ability to extract meaningful insights from complex datasets.