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.
Data analysis about Brazilian e-commerce business Olist
This analysis is the project of the Platzi Master Cohort 10 BI group. We will use the data published by Olist , in which we will answer how the socio-economic context of Brazil influences the purchase of products online.
Final Data Science group project for Henry Bootcamp. Developed data solutions for Olist, an ecommerce brazilian startup, including a Dashboard in PowerBI, a forecasting model deployed on Streamlit and a web app for remote access.
Projeto final da disciplina Estatística I do curso Santander Coders em parceria com a AdaTech ministrada pelo professor Damodara Barbosa.
Project developed for the course Data Mining at UFSC
Analysis of around 100.000 orders from a Brazilian online store
Projeto de Data Lakehouse com o dataset Brazilian E-Commerce Public Dataset by Olist
Análise do dataset da Olist
A Comprehensive Time Series Analysis for Dynamic Sales Forecasting
Olist Brazil - Sales Insights and Machine Learning Predictions 📊🤖 Developed sales dashboards and machine learning models to analyze customer behavior and product performance for Olist, a Brazilian e-commerce platform. Utilized Power BI for visualization and multiple machine learning algorithms to predict customer satisfaction. 🔍📈
Segmentation de la clientèle de Olist.
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