olist
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Data analysis about Brazilian e-commerce business Olist
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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.
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Projeto final da disciplina Estatística I do curso Santander Coders em parceria com a AdaTech ministrada pelo professor Damodara Barbosa.
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A Comprehensive Time Series Analysis for Dynamic Sales Forecasting
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🔷 Business Insights & Executive Report for E-Commerce Dataset 🔷 Analyzed sales, profit, delivery performance, and customer behavior across multiple Brazilian regions. Used DAX, data modeling, and visualization techniques for dynamic, interactive dashboards. Identified key growth areas, top-performing categories, and optimized payment insights.
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SDK não oficial em TypeScript para a API v2 do TinyERP (Olist)
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Customer segmentation & delivery performance analysis using SQL + Power BI
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End-to-end analysis of Olist e-commerce data, including customer segmentation and testing the impact of free-shipping vouchers.
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Projeto de portfólio: modelagem de banco de dados relacional MySQL a partir do dataset público Olist (Kaggle). Inclui ETL em Python, criação de MER/MMD, análises em SQL, e visualizações em Power BI/Flask.
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Este projeto utiliza o Power BI para importar, tratar e analisar dados da Olist, gerando insights sobre vendas e comportamento dos consumidores a partir de quatro bases distintas.
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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. 🔍📈
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Esse projeto utiliza dados da Olist para importar, tratar e analisar quatro bases de dados com o Power Query no Power BI. O objetivo é fornecer insights valiosos sobre vendas e comportamento do cliente nos principais marketplaces do Brasil.
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📊 Build an ETL pipeline to transform raw marketplace data into structured analytics-ready advertiser KPIs using Python and Pandas.
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🔍 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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