This project aims to analyze coffee quality data provided by the Coffee Quality Institute (CQI). The objective is to understand the factors influencing coffee quality through sensory evaluations, processing methods, origin regions, and defect occurrences. By leveraging tools like Power BI, the project seeks to uncover insights into what contributes to high-quality coffee and how different variables interact to influence overall coffee quality scores.
- Original Dataset:
df_arabica_clean.csv - Power BI Report:
CQI_Analysis_reports.pbix
- Tools: Power BI
- Methodologies: Data visualization, statistical analysis
- Insights into key determinants of coffee quality based on sensory attributes.
- Correlations between processing methods, origin regions, and coffee quality scores.
- Patterns in defect occurrences and their impact on overall coffee quality.
- Analysis of variables influencing Total Cup Points, a measure of overall coffee quality.
[Link to interactive dashboard, if applicable]
I played a pivotal role in conducting comprehensive data analysis using Power BI:
- Data Exploration and Preparation: Cleaned and prepared the
df_arabica_clean.csvdataset, focusing on sensory evaluations such as aroma, flavor, acidity, etc. - Power BI Analysis: Developed interactive dashboards and visualizations to explore correlations between various factors influencing coffee quality, including processing methods, origin regions, and defect categories.
- Statistical Analysis: Applied statistical techniques to uncover insights into the relationships between different variables and their impact on Total Cup Points.
- Visualization and Reporting: Created visual representations to effectively communicate complex data relationships and findings.
Data Visualization, Coffee Quality, Power BI, Sensory Evaluation, Data Analysis, Microsoft Excel