Welcome to the Complex Visualization of Pokémon Data on my GitHub repository! As a passionate data scientist, I am thrilled to present this project that dives deep into the captivating world of Pokémon, focusing on data visualization for the first six generations.
Pokémon, a beloved franchise that has captured the hearts of millions, provides a treasure trove of fascinating data. In this project, I have harnessed my data science skills to analyze and visually represent various aspects of Pokémon data from the first six generations.
If you encounter any trouble viewing the Dash app in the Jupyter notebook, fear not! Simply click here, and the app should be readily available for your enjoyment.
The dataset used in this project comprises comprehensive information on Pokémon species, their characteristics, moves, abilities, types, and more. With this rich dataset at hand, we can uncover insights into the Pokémon universe and explore the relationships and patterns hidden within.
Utilizing powerful data visualization techniques and tools, I have created visually stunning representations of the Pokémon data, allowing us to gain a deeper understanding of the Pokémon world. Some highlights of the project include:
- Pokémon Distribution: I analyze and visualize the distribution of Pokémon across different generations, exploring the number of Pokémon introduced in each generation and their corresponding types.
- Type Interactions: I investigate the interactions between different Pokémon types, uncovering strengths, weaknesses, and resistances within the Pokémon battles and strategies.
- Stat Analysis: I explore the statistical attributes of Pokémon, including their base stats, comparing them across different generations and identifying trends or outliers.
- Interactive Plotly and Dash App: An interactive application that filters the pokemon based on both their primary and secondary type to display their hit-points, attack, defence, and the proportion of the chosen pokemon that are legendary. This app also shows hover data that shows data for specific pokemon, helping the Pokemon trainer to choose Pokemon to collect.
Throughout this project, I employ a wide array of visualization techniques and tools to bring the Pokémon data to life. I leverage libraries such as Matplotlib, Seaborn, Plotly, and Dash to create interactive visualizations, heatmaps, scatter plots, bar charts, and more, ensuring an immersive and informative experience.
In this GitHub repository, you will find the following:
- Jupyter Notebooks: Jupyter notebooks containing the code used for data exploration, analysis, and visualization, with detailed explanations and interpretations.
- Datasets: The Pokémon dataset used in the project, along with any additional data sources or preprocessed files.
- Documentation: Documentation detailing the project objectives, methodology, and key findings, enhancing the understanding and replicability of the analysis.
- Screenshots: Images of the dash app, in case the user cannot open the dash app they can see what the app can provide.
If you have any questions, feedback, or would like to collaborate on a project, please feel free to reach out to me via email or connect with me on LinkedIn. I am excited to engage with fellow Pokémon enthusiasts and data enthusiasts alike!
Thank you for exploring the my project on Pokémon Data Visualizations, and I hope you enjoy the captivating visual journey through the Pokémon universe.
Pokémon, Gotta Catch 'Em All!
Best regards,
David Shields