This project explores e-commerce sales data using Python. It focuses on identifying top products, analyzing monthly sales patterns, and understanding customer purchasing behavior using basic data analysis and visualization.

Exploration, Insight generation and Visualization of Olist E-commerce sales data using Python and POWER BI

Customer Lifetime Value, Returns Predictions, Recommender system and sales analysis on UC Irvine online sales dataset.

INTRODUCTION Lung protocol is a platform created for buyers and sellers all over the world.With an easy-to-use online store builder Platform, any seller is able to create their own storefront to sell their things, while making use of the Lung protocol platform’s wide range of features for a minimal fee.Our service offers great opportunities for self-employed and entrepreneurs, small scale manufacturers, family businesses and makers of handmade crafts, including a selection of tools for direct customer feedback, bookkeeping, sales analysis, advertising and promotion, as well as a convenient, user-friendly interface. lung protocol utilizes innovative technological features to guarantee the maximum security in all your transactions. Securely buy & sell anywhere with decentralized reputation & escrow using the L2P token. ABOUT PLATFORM Lung protocol is a decentralized marketplace that allows anyone to buy and sell products or services for cryptocurrencies on a global scale. The marketplace will support thousands of merchants and millions of listings, with its primary focus on no fees. Sell and buy goods fast, secure and without commissions. Pay by crypto. Lung protocol is world’s largest decentralized, peer-to-peer marketplace. The computational efficiency of our protocol is powered by key code design features like forward and backward compatibility, component-based modular structure, and asynchronous core architecture. We are focused on bringing the benefits of this new disruptive technology to future facing enterprises that recognize its trans formative role in bringing real‑world commercial advantage. Lung protocol is a platform created for buyers and sellers all over the world. With an easy-to-use online store builder Platform, any seller is able to create their own storefront to sell their things, while making use of the Lung protocol platform’s wide range of features for a minimal fee. Our service offers great opportunities for self-employed and entrepreneurs, small scale manufacturers, family businesses and makers of handmade crafts, including a selection of tools for direct customer feedback, bookkeeping, sales analysis, advertising and promotion, as well as a convenient, user-friendly interface. Lung Protocol (L2P) powers our merchant storefronts and e-commerce ecosystem. Sell and buy goods fast, secure and without commissions. Pay by crypto. OUR VISION Our main intention is not to replace Fiat currency however, we aim to provide a unique approach to creating an ecosystem unattached to the current inflationary model. As we all know, Fiat currency is the foundational currency in which our society operates on a daily basis. Lung Protocol provides a reliable, stable and tax-exempt cryptocurrency to enable seamless transactions. A limited supply currency hosting safer transactions in an ecosystem that allows the token holders to have full control of their finances at all times. A limited supply currency in which there is no third-party requiring permission to interact with Lung Protocol (L2P). TOKEN INFORMATION Token Name :Lung Protocol Token symbol :L2P Token type :ERC20 Token contract :0xee0f286776639cd363da810daf3e0623f82576b0 Token Decimal :18 Token Supply :750000000 USEFUL LINKS 1. https://t.me/LungProtocol_Group 2. https://t.me/LungProtocol_Channel 3. https://twitter.com/lungprotocol 4. https://medium.com/@lungprotocol 5. https://web.facebook.com/lungProtocol 6. https://github.com/LungProtocol 7. https://www.linkedin.com/company/lungprotocolinfo/ EXCHANGE 1. CryptloCEX http://cryptlocex.com/ 2. Switchdex http://switchdex.com/ 3. Bolddax http://bolddax.com/ 4. exnce http://exnce.com/ Ongoing till 10th July Lung Protocol decide to arrange a self drop program to deliver an amazing eCommerce platform. As you all know for an e commerce platform community is the power to archive success for the future as well as you will be benefited by this path way there is a great chance to make money whatever you want in crypto exchange after lunching your favorite L2P in the open market. This concept will bring you a financial freedom world. Dear respectable community Lung Protocol is an ERC20 token now that is under ETHEUREUM block chain but very soon Lung Protocol will lunch main net for community betterment and to create the main vision in eCommerce sector. SELFDROP

This project focuses on analyzing sales data to uncover key business insights and performance trends. The goal is to empower stakeholders to make data-driven decisions that maximize revenue and optimize operational efficiency.

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

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This repository contains an SQL notebook created in Azure Data Studio which provides a sales analysis of an e-commerce business using PostgreSQL.

Data Analysis/Visualization using Python and Power BI

In this project we dive into the intriguing world of Ecommerce sales data from the year 2019. Through data wrangling, visualization, and insightful analysis, we aim to uncover trends, customer behaviors, and key factors that drove sales during that period.

Ecommerce Sales Dashboard using - Power Bi 📊

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This dashboard provides key insights into sales performance, including profits, product performance

This repository hosts a suite of tools for real-time sales data analysis and monitoring for an e-commerce platform. It includes AWS-powered data pipeline scripts for data ingestion, processing, and storage, along with Grafana dashboards for visual insights.

Year to Year Sales and Profit analysis for a medium sized e-commerce platform based in United States of America.

Explore our e-commerce sales analysis project showcasing robust growth, with a focus on key metrics like total sales and category dominance. Dive into graphical insights and future strategies. Contribute to refining our data-driven approach for sustained success. #DataAnalysis #ECommerce

"An interactive Power BI dashboard for eCommerce data analysis, providing insights into sales performance, customer behavior, product trends, and key metrics to optimize business strategies." Feel free to customize it based on the specific features or insights your dashboard provides!

Ecommerce Sales Analysis

ecommerce sales data analysis

E-Commerce Sales Analysis using Python and Power BI

Successful ecommerce businesses need to optimize every stage of the customer journey, from first touch to purchase. This ecommerce dashboard is a curated set of metrics, KPIs and other data that’s designed to highlight, in the clearest possible way, trends, anomalies and information related to an online store that regularly warrants attention.

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This repository contains a data-driven analysis of an e-commerce company's sales and return patterns using Python. The project focuses on demand forecasting, product performance evaluation (Pareto Analysis), return analysis and Segmentation techniques to help optimize inventory management, marketing strategies, and operational efficiency.

End-to-end Excel-based analysis of e-commerce sales data.

Ecommerce Analysis using Excel

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This project uses MS Excel for data cleaning, My SQL to analyze the dataset and Power BI for data visualization of an online E-Commerce platform. The goal of the project is to answer a set of questions about the business performance and help in its growth by making better decisions.

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"Power BI dashboard analyzing e-commerce sales trends, category performance, and customer behavior."