In this analysis, our group looked at data from the years 2016-2018 of 100,000 orders placed on the Brazilian eCommerce platform, Olist. We used the Random Forest machine learning algorithm to create a web app that predicts customer review scores using the most highly correlated features from the data as user inputs.
This is my final project in the Data Analytics immersive program at General Assembly. This project is on Olist e-commerce, a marketplace similar to ebay and amazon. Here, I have demonstrated SQL, Python, Pandas and Tableau skills which are critical for data analytics, data intelligence and visualizations. Today the world is data-driven and it ha…
Olist Customers Segementation using 3 method Domain Knowledge, KMeans & DBScan. After Customers segment I Analyst our customers segment behavior
This repository contains jupyter notebooks of how to solve some problems of Brazilian E-Commerce Public Dataset by Olist
Analysis of around 100.000 orders from a Brazilian online store
"From Data to Insights: Analyzing Olist's E-commerce Performance"
An insightful data analysis project on the Olist Store e-commerce dataset, focused on uncovering trends in customer behavior, payment methods, delivery performance, and satisfaction levels. The project integrates Excel, Power BI, Tableau, and MySQL for data processing, visualization, and reporting.
🔍 Analyze the Brazilian Olist E-Commerce Dataset to uncover sales trends, product performance, and customer insights for data-driven business decisions.
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