This project involved analyzing a wine dataset to create graphs and draw conclusions about wine characteristics. It significantly enhanced our data processing abilities, particularly in data cleaning and visualization. Through this project, we developed a stronger understanding of handling real-world datasets and effectively communicating insights through visualizations.
This project was a term project for STAT112, Introduction to Data Processing and Visualization, course from METU and the data was given to us from our instructors. The excel file used in this project has been uploaded.
The dataset shows the properties of the 100 wines from three different kinds and colors in the store.
quality_score: the quality score of the wine out of 10 acidity: acidity level of the wine sugar: sugar percentage in the wine cholorides: the amount of salt in the wine total_sulfur_dioxide: total sulfur dioxide (SO2) (ppm) density: density of the wine ph: ph level of the wine (between 2.5-4.5) alcohol: the alcohol percentage of the wine wine_type: the type of the wine color: the color of the wine grape: the type ofgrape where the wine has been made.
This project helped to understand python and data visualization better. Also, what makes a wine better quality was understood. The range of values that the sugar percentage, total sulfur dioxide, density, pH, alcohol percentages wine can take were understood.