Olist Deep Dive

Abstract

1. Introduction

Olist is a Brazilian e-commerce platform that connects sellers and buyers, offering a wide range of products and convenient conditions for online sales. Olist also acts as an intermediary, allowing sellers to connect to multiple marketplaces simultaneously, thereby increasing their reach.

Comprehensive analysis of Brazilian e-commerce data, uncovering key insights and actionable business recommendations.

2. Objective

🌊 Deep Sales Analysis of Olist E-Commerce: EDA | Time Series| Viz | RFM | NLP | Geospatial | Segmentation & Actionable Business Recommendations.

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Negative Feedback Drivers: Low ratings correlate with longer delivery times, higher order value, and heavier items.
• Success Factors: Fast carrier handoff (≤3 days) and installment options boost ratings and repeat purchases.

4. Technology Stack

• Data Analysis: Python Pandas NumPy
• Visualization: Plotly Tableau
• Statistics & ML: StatsModels SciPy Sklearn Pingouin
• NLP & Text Processing: NLTK TextBlob
• Exploratory Data Analysis (EDA):
• Statistical summaries, missing value analysis, and outlier detection
• Data Preprocessing:
• Feature engineering, missing value handling, and creation of new metrics and dimensions

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/PavelGrigoryevDS/olist-deep-dive.git
cd olist-deep-dive

1. If Poetry is NOT installed on your system, use Option 1
2. If Poetry IS installed, use Option 2

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by PavelGrigoryevDS and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.