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Analyze the Olist dataset with ease using Python DataAnalysis class. Fetch, clean, and visualize data on customer cities, payment types, order status, and popular seller cities. Dive into the world of data analysis with SQLite, Pandas, Matplotlib, and Seaborn. Discover insights and trends in the Olist dataset effortlessly.

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Suka Makmur E-commerce Data Analysis

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This repository contains the analysis of "Suka Makmur," an e-commerce platform. The analysis focuses on understanding customer behavior, payment processing, order management, and seller relationships. The goal is to uncover insights that can guide business decisions and improvements.

Overview

  • Introduction: Learn about the emergence of "Suka Makmur" and the role of data analysis in its journey.

  • Challenges: Explore the challenges faced by "Suka Makmur" in areas like customer engagement, payment processing, order management, and seller relations.

  • Data Analysis: Discover how data analysis, powered by the DataAnalysis class, played a crucial role in addressing these challenges.

  • The Journey: Dive into the specifics of the analysis, including customer city analysis, payment type distribution, payment value trends, order status insights, and popular seller cities.

Data and Code

  • Dataset Description: Understand the dataset used for analysis, which includes customer data, payment records, order details, and seller information.

  • Analysis Methods: Get an overview of the methods used for data loading, cleansing, and analysis, including code snippets.

Analysis Outputs

Explore the key findings and recommendations from the analysis:

  • Customer City Analysis: Identify popular customer cities and gather insights for targeted strategies.

  • Payment Type Analysis: Understand the distribution of payment types and improve the payment experience.

  • Payment Value Median: Determine the median payment value to better understand payment patterns.

  • Order Status Analysis: Gain insights into order statuses for different cities and improve order management.

  • Seller City Analysis: Identify popular seller cities and explore opportunities for partnerships and growth.

Disclaimer: “Suka Makmur” is a fictional entity used solely for the purpose of illustrating the approach and analysis in this report. It does not represent any real organization or entity.

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Analyze the Olist dataset with ease using Python DataAnalysis class. Fetch, clean, and visualize data on customer cities, payment types, order status, and popular seller cities. Dive into the world of data analysis with SQLite, Pandas, Matplotlib, and Seaborn. Discover insights and trends in the Olist dataset effortlessly.

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