This repository contains three visualization projects that explore data using Matplotlib and Seaborn. Each project focuses on uncovering insights from real-world datasets through clear and visually engaging charts.
Overview:
Analyzes global coffee production trends and country-level data to understand changes over time.
Highlights:
- Visualized overall coffee production statistics.
- Compared top 5 coffee-producing countries across multiple years.
- Showed composition of coffee production across regions.
Overview:
Focuses specifically on Brazil’s position within the global coffee market.
Highlights:
- Processed and filtered data to prepare for visualization.
- Created subplots comparing Brazil’s coffee production to the rest of the world.
- Highlighted Brazil’s dominance and changes in its share of global output over time.
Overview:
Analyzes a car sales dataset to identify price patterns, brand trends, and market opportunities.
Highlights:
- Explored numeric variable relationships using scatter plots and correlation heatmaps.
- Analyzed categorical relationships such as price by car make and condition.
- Conducted a detailed Ford F-150 deep dive:
- Compared state-wise pricing and average differences.
- Identified potential deals and best states to buy trucks.
- Investigated value retention trends across brands and models.
- Python
- Matplotlib
- Seaborn
- Pandas
- NumPy
These projects demonstrate how data visualization can simplify complex datasets and reveal meaningful insights into global coffee production and automobile markets.
