olist-ecommerce-sales-data-analysis-using-python

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olist-ecommerce-sales-data-analysis-using-python

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Exploration, Insight generation and Visualization of Olist E-commerce sales data using Python and POWER BI

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# Olist E-commerce Data Analysis

## Exploration, Insight generation and Visualization of E-commerce sales data

1. Olist Data Analysis.ipynb - the file that contains the exploratory data analysis using Python
2. Sales Analysis Dashboad.pdf - the file that contains the power bi vizualization

Olist is a Brazilian e-commerce platform that connects small and medium-sized businesses to customers across Brazil. The platform operates as a marketplace, where merchants can list their products and services and customers can browse and purchase them online. The Olist sales dataset is a collection of anonymized data about orders placed on the Olist from September 2016 to September 2018.

This repository contains a data analysis project that explores the sales data of the Olist platform and provides insights into revenue trends, order patterns, product categories, seller performance, customer behavior, and payment methods. The project is implemented in Python using Jupyter notebooks and various data science libraries.

In this project, we aim to help Olist gain better insights into their e-commerce platform and optimize available opportunities for growth, by providing answers to the following business questions:

1. What is the total revenue generated by Olist, and how has it changed over time?
2. How many orders were placed on Olist, and how does this vary by month?
3. What are the most popular product categories on Olist, and how do their sales volumes compare to each other?
4. What is the average order value (AOV) on Olist, and how does this vary by product category and payment method?
5. Who are the top active sellers on Olist?
6. What is the distribution of seller ratings on Olist, and how does this impact sales performance?
7. How many customers have made repeat purchases on Olist, and what percentage of total sales do they account for?
8. What is the average customer rating for products sold on Olist, and how does this impact sales performance?
9. What is the total order cancellation on Olist, and how does this impact the company's revenue?
10. Which payment methods are most commonly used by Olist customers?
11. Which product categories have the highest revenue on Olist, and how can the company increase revenue across different categories?
12. What Geolocation has high customer density?

By answering these questions, we aim to help Olist managers and stakeholders understand the e-commerce landscape in Brazil and recommend opportunities to improve the company's performance.

By: Judith Okon

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