Welcome to the Coffee Quality Data Analysis project! In this notebook, we dive deep into the world of coffee 🍃, exploring a rich dataset to understand what makes a cup truly exceptional.
The data comes from the Coffee Quality Institute (CQI), scraped as of May 2023. It includes detailed sensory evaluations of Arabica coffee beans from around the world.
• ✅ Data Cleaning: Removed irrelevant columns and standardized data for clean exploration.
• 📊 Data Visualization: Leveraged seaborn, matplotlib, and plotly to create beautiful, insightful charts.
• 🕵️ Exploratory Analysis:
• Investigated relationships between coffee quality attributes.
• Detected missing values and addressed inconsistencies.
• Highlighted trends in flavor, aroma, body, and acidity.
• Pandas for data manipulation
• NumPy for numerical operations
• Seaborn for statistical visualizations
• Matplotlib for plotting
• Plotly for interactive graphs
analysis_coffee.ipynb # Jupyter Notebook with full analysis README.md # This file
• High-quality coffee often correlates with high scores in fragrance, flavor, and aftertaste.
• Country of origin and processing method play crucial roles in final cup quality.
• Proper data cleaning is crucial to meaningful analysis!
1. Clone this repo 📥
2. Open analysis_coffee.ipynb in Jupyter Notebook or VSCode
3. Install required libraries:
pip install -r requirements.txt
4. Run the notebook and enjoy exploring the data!
Made with love for coffee enthusiasts and data nerds alike ☕💻