🖱️ 𝙏𝙚𝙘𝙝 𝙨𝙖𝙡𝙚𝙨 𝙧𝙚𝙥𝙤𝙧𝙩

For this project, I combined three datasets with information about sellers, articles, and orders of one-month trade log, to answer some questions. My analytical process involved using Pandas for exploratory data analysis, NumPy for analysis of specific columns, and Matplotlib/Seaborn for visualization of results.

First, I scoped and collected the necessary data, followed by data exploration and preparation. Next, I defined the model and pipelines necessary to achieve the desired results. Using these tools, I was able to answer the questions and draw appropriate conclusions.

The sources of data used in this project were:

✔️ articles.db: DB with articles data

✔️ sellers.xlsx: Excel file with sellers data

✔️ orders.csv: CSV file with sales records withing the month

The client requested the following information to be answered:

✔️ What is the best-selling item? (in units)

✔️ Which item provided us with the highest revenue?

✔️ Which seller should be awarded the “Best Seller of the Month” bonus?

✔️ Are there significant variations in sales throughout the month?

✔️ What were the top 5 countries in terms of purchases, and what was the total amount of their purchases?

✔️ Notebooks or CPUs? Which did the top 5 purchasing countries buy more of?

Development:

✔️ To begin with, I was tasked with collecting and organizing data from various sources, including CSV and Excel files, as well as a database. To accomplish this, I utilized a range of Python libraries, such as Pandas, SQLite3, and openpyxl. During the exploratory analysis, I assessed the data for various characteristics, such as the number of columns and entries, null values, data types, and unique indexes, and subsequently prepared the data for analysis. To proceed, I merged all the data frames into a single one for ease of use.

✔️ In the analytical section, I was tasked with answering four questions using both analytical and graphical approaches. Additionally, I had to formulate three new questions and provide responses to them.

✔️ Finally, I presented the conclusions and recommendations of the project in a clear and concise manner.

Resources used:

I used Python and its libraries Pandas, NumPy, and Matplotlib/Seaborn for data analysis and visualization

I'd love to hear from you!

If you have any question, comments, or suggestions, do not hesitate to contact me (melisa.s.rossi@gmail.com).