Kai Zhang Da E Commerce Data Analysis In Excel Github

Contributions to enhance the analysis or address any insights are welcome! Please feel free to submit a pull request or open an issue. There was an error while loading. Please reload this page. This repository contains a comprehensive analysis of an e-commerce dataset using Excel . The project explores customer purchasing patterns, product performance, and business insights to optimize strategies for revenue growth and customer satisfaction.

This project focuses on analyzing an e-commerce dataset using Microsoft Excel, uncovering key business insights, and creating an interactive dashboard. It showcases a range of data analysis techniques, visualization methods, and actionable recommendations to improve business performance. This project is a testament to how Excel can go beyond simple spreadsheet tasks to perform advanced analytics and visualization. The dashboard provides actionable insights and encourages informed decision-making, particularly for optimizing product offerings and pricing strategies. Dataset and live dashboard --- https://github.com/Archanadevi-29/E-Commerce-Data-Analysis/commit/1d888ab1d92d8268b2304eafcd8c47b3ca524533 Visualization --- https://github.com/Archanadevi-29/E-Commerce-Data-Analysis/blob/main/Screenshot%202025-01-01%20224340.png

This project analyzes an e-commerce dataset to uncover key business insights using Excel and presents them visually in PowerPoint. To extract actionable insights from e-commerce data by analyzing: Chaitali Roy Student | Data Enthusiast | Aspiring Analyst This project involves analyzing e-commerce data using SQL, Excel, and Power BI to derive insights and visualize key metrics. The main objectives are to identify trends, track sales performance, and provide a comprehensive overview of business operations. This dashboard provides a comprehensive analysis of sales and net profit.

This dashboard focuses on a more detailed analysis of net profit. This project showcases the use of SQL, Excel, and Power BI to analyze and visualize e-commerce data effectively. The insights derived can help in making informed business decisions. Click this link, download the dataset and answer the question on each sheet This project deals with many real-world challenges faced by e-commerce websites that includes predicting customer lifetime value using RFM score and k-means clustering, customer segmentation to find out best valued customers. Also, predicting review score that customers will give to their order experience depending on their location, order cost and other factors.

I have also done a detailed analysis of how geolocation can affect user’s experience and their purchase and much more. Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. Also included is a geolocation dataset that relates Brazilian zip codes to lat/lng coordinates. This dataset have nine tables which are connected by common attributes.

Few instances of the analysis performed are as follows: The model helps to find a way to estimate that i.e. based on data about the product and order what will be the customer review score. Project : https://github.com/nairkivm/e-commerce-public-data-project As part of my final data project in the data science course from Dicoding, I conducted an end-to-end data analysis for an e-commerce platform and created a comprehensive dashboard. Here’s a step-by-step explanation of how I approached the analysis.

Effective business questions are crucial for achieving business objectives and solving problems. To formulate effective business questions, we can use the SMART criteria: questions should be Specific, Measurable, Action-oriented, Relevant, and Time-bound. For this e-commerce dataset, the business questions are: In this stage, data is collected to answer the analytical questions formulated earlier. The quality and structure of the data are then assessed to identify any issues and develop an appropriate data cleansing strategy. For this project, I created a function to automatically generate data assessments after each process.

This is the example of the assessment report.

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