Welcome to the Pokémon Data Visualization and Data Story Project! In this project, we explore and visualize data from the Pokémon dataset to gain insights into the strengths, weaknesses, and overall performance of different Pokémon, and then tell a story based on our insights.
The dataset used in this project is sourced from Kaggle. It contains detailed information about various Pokémon, including their attributes such as Type1, Type2, HP, Attack, Defense, Special Attack, Special Defense, Speed, and more.
For our data story, we began by extracting the top 20 Pokémon from the dataset based on their Composite Strength measure which we calculated. The Composite Strength measure was calculated using a weighted combination of Attack, Defense, Special Attack, Special Defense, and Speed attributes. These top 20 Pokémons were chosen as the elite group to compete in our simulated Pokémon battles.
The elite Pokémons were divided into three groups: A, B, and C, to compete in Round 1 of the battles. Each group showcased their strengths and weaknesses in intense one-on-one battles. The visualizations helped us analyze the performance of each Pokémon and understand how different attributes impacted the outcomes.
The winners from Round 1 advanced to Round 2, where they faced off in a semi-final battle. The stakes were higher, and the competition fiercer as the Pokémons strived to secure their position in the final round.
In the final round, the remaining Pokémons battled head-to-head to determine the ultimate champion. The visualizations allowed us to witness the intensity of the battles and gain valuable insights into the strategies used by each Pokémon to emerge as the top contender.
After an intense battle in the final round, Koraidon emerged as the champion, showcasing its exceptional abilities and strength. With a well-balanced combination of Attack, Defense, and Speed, Koraidon proved to be the most powerful and skilled Pokémon in the elite group.
Pokebook is your ultimate companion and guide to the mesmerizing world of Pokémon. This captivating book offers a treasure trove of information, insights, and captivating tales that will transport you into the enchanting realm of Pokémon. Pokebook is a doorway to an awe-inspiring journey of imagination and wonder.
Dashboard.mp4
Story.mp4
Our journey began by delving into the Pokémon dataset, which provided a treasure trove of information about these captivating creatures. We examined the different attributes available and brainstormed ways to extract valuable insights from the data. This involved understanding the significance of attributes such as HP, Attack, Defense, and more in battles and competitions.
To analyze and compare Pokémon effectively, we needed to establish suitable metrics and measures. We devised various metrics, including Composite Strength, which would capture the overall performance of each Pokémon based on a combination of attributes. By assigning appropriate weights to different attributes, we aimed to create a comprehensive measure that reflects the true potential of each Pokémon.
To make sense of the dataset and uncover hidden patterns, we employed the power of data visualization. We created a range of visualizations, such as stacked charts, scatter plots, radar charts, and more, to showcase the relationships between different attributes and types. These visualizations allowed us to observe trends, strengths, weaknesses, and notable characteristics of Pokémon.
While visualizations provided valuable insights, we realized the importance of storytelling to convey our findings effectively. We curated data stories that revolved around battles of elite Pokémon, highlighting their performance, strategies, and the ultimate champion. By presenting the data in a narrative format, we aimed to captivate audiences and engage them in the fascinating world of Pokémon.
The project is implemented using Python and various libraries such as,
- pandas
- matplotlib
- plotly
- Seaborn
The tools we have used are,
- PowerBI
- Canva
The data is preprocessed, analyzed, and visualized using these tools and technologies.
- Clone the repository or download the project files.
- Load the Pokémon dataset into your preferred data visualization tool (e.g., Power BI, Tableau, Flourish).
- Explore the visualizations to gain insights into Pokémon attributes and performance.
- Customize the visualizations as per your requirements to analyze specific aspects of the data.
We would like to express our gratitude to AngelHack team for inspiring us to take on this amazing challange. And also to Kaggle and the original dataset creator, Rohan Patil, for providing the Pokémon dataset.