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In engaging 'Lego Analysis Using Python' project, i have embark on a data exploration journey inspired by real-world challenges provided by Data Camp. Leveraging the power of Python and its data manipulation library, Pandas, we dive into a fascinating dataset centered around Lego sales. This project serves as a testament to our data analysis and problem-solving skills, bringing valuable insights to the world of Lego enthusiasts and collectors.
Key Project Highlights:
Data Collection and Merging: This project initiated with the meticulous collection and merging of Lego sales data, consolidating multiple sources into a cohesive dataset. This step laid the foundation for our comprehensive analysis.
Data Cleaning: I have applied robust data cleaning techniques, including the removal of duplicates, handling of missing values (NaN), sorting, and format standardization. This ensured the dataset's integrity and reliability.
Star Wars Theme Analysis: I have conducted a focused analysis on the Star Wars-themed Lego sets, dissecting their sales performance. Then i have determined the total number of Star Wars-themed Lego sets sold and differentiated them from non-licensed sets.
Identifying Top-Selling Themes: Our exploration didn't stop there. I have identified the most popular Lego themes, shedding light on the community's preferences and interests.
Yearly Analysis: I delved into the data to discover the year in which Star Wars-themed Lego sets were not the top-selling Lego theme. This historical perspective provided valuable insights into evolving consumer preferences.
Comparative Analysis: Going beyond the Star Wars theme, i have compared the sales of other themed Lego sets to determine which themes outperformed even the iconic Star Wars Lego.
This project showcases proficiency in Python and data analysis techniques, demonstrating our ability to extract actionable insights from complex datasets. Our results provide a deeper understanding of Lego sales dynamics and offer valuable guidance to Lego enthusiasts and collectors worldwide.