Ecommurz, a fictional e-commerce company, aims to evaluate its sales performance by analyzing top-selling products, preferred payment methods, and product ratings. To achieve this, data from the Olist database is utilized, consisting of various datasets on customers, orders, products, and payments. Relevant datasets are selected for analysis, facilitating a comprehensive examination of sales data, including product categories, payment methods, and customer reviews.
The data undergoes cleaning to address missing values, duplicates, and inconsistent formats. After removal of duplicates and standardization of product category names, outliers in payment values are treated to ensure data integrity. Subsequent exploration reveals insights into top and least selling products, preferred payment methods, and product ratings. Analysis of payment types indicates credit cards as the preferred method, suggesting potential partnerships with banks to enhance transaction values. Examining product ratings uncovers discrepancies between sales performance and customer satisfaction, guiding strategies for optimizing sales and improving customer experience.
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This repository only documents the code. For the article, kindly visit my Medium article.
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https://github.com/LingAdeu/Data-Wrangling-with-Python.git
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