Data Visualizer

This project is a reusable notebook focused on exploring and demonstrating simple methods to visualize data in both 2D and 3D. As a sandbox for experimentation, it includes a range of plotting techniques using Python libraries like Matplotlib and Seaborn, ideal for anyone looking to understand or improve their data visualization skills.

🔑 Key Feature

Basic Plotting:

Demonstrates the basics of plotting simple line graphs and adjusting visual properties like line style, width, and color.

Advanced Plotting Techniques:

Explores multiple plots on the same figure, handling subplots, and creating complex layouts.

Interactive Visualizations:

Utilizes Plotly for dynamic, interactive graphs that enhance user engagement and provide deeper insights.

Styling Plots:

Implements various styles to visualize data, adapting the appearance of plots to match preferences or themes like ggplot from R and fivethirtyeight style.

3D Visualizations:

Showcases 3D plotting capabilities to represent multi-dimensional data, enhancing the perception of depth and trends.

🧠 Skills

• Python
• Matplotlib, Seaborn, Plotly
• Data Visualization Techniques
• Statistical Data Analysis

🦾Future Work

• Incorporate More Interactive Tools: Enhance notebooks with more interactive elements, possibly integrating with web-based visualization tools.
• Expand Dataset Variety: Apply visualization techniques to a broader range of datasets and scenarios to cover more use cases.
• Machine Learning Integration: Utilize visualizations for machine learning model diagnostics to better understand model behaviors and results.