anderson-andre-p / wine-data-analysis

This repository contains a data analysis project that focuses on a series of wine data. The project was completed using Python libraries such as NumPy, Pandas, Seaborn, and Matplotlib. The goal of this project was to gain insights into the characteristics of the wines and to practice data analysis skills.

Wine Data Analysis

This repository contains a data analysis project that focuses on a series of wine data. The project was completed using Python libraries such as NumPy, Pandas, Seaborn, and Matplotlib. The goal of this project was to gain insights into the characteristics of the wines and to practice data analysis skills.

The data analysis process involved several steps, including data cleaning, exploratory data analysis, and visualization. The data cleaning process involved handling missing values and removing duplicates. Exploratory data analysis was used to summarize the characteristics of the wines and to identify patterns and relationships in the data.

Data visualization was used to communicate the results of the analysis effectively. Matplotlib and Seaborn were used to create visualizations such as histograms, scatter plots, and box plots.

The project provides insights into the characteristics of the wines, such as the alcohol content, acidity, and sugar levels. The project also identified correlations between these characteristics and the quality of the wine.

Overall, this project serves as a valuable resource for anyone interested in learning about data analysis and practicing their skills. The wine dataset provides an excellent opportunity to practice data analysis techniques and to gain insights into the characteristics of wines. The Python libraries used in this project are widely used in data science and provide a solid foundation for anyone interested in pursuing a career in data analysis.