A Model Context Protocol (MCP) server that lets Claude access Keepa's Amazon marketplace data through a conversational interface. This makes product research, sales trends, inventory insights, and competitive analysis easier without navigating Keepa's complex UI.

This is a part of the ineuron intership. This project will deal with the analysis of Amazon Sales Data

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In this project work, I managed the end-to-end process and analyzed sales data.

Analyze Amazon sales data to understand to understands sales trends

In this project, I used Python to analyze Amazon sales data and created visualizations to better understand trends and patterns. I also used machine learning algorithms to make predictions about future sales.

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A comprehensive ETL pipeline and sales analysis project leveraging Microsoft Azure and PySpark, designed to optimize e-commerce sales by providing actionable insights through detailed data analysis.

Amazon sales analysis focused on analyzing an e-commerce dataset.

Developing an automated system for trend analysis in e-commerce. Utilizing Xpath and Python to obtain data on top-selling products from influential marketplaces like Amazon and eBay based on user choice. Using AI to transform data into visualized insights, aiding business owners in product selection decisions and enhancing sales performance.

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.

Analyzed Amazon Food Sales Data and visualized the Findings using Tableau and MS Excel.

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DIGITAL MARKETING BY SARA ATIQ INTRODUCTION “It is a marketing technique that involves usage of digital mediums such as internet & wireless for creating awareness, consideration, purchase & loyalty for a brand product or a service". It is the term used to describe any marketing efforts that place on the internet or a digital device. It has different channels that enable the business to entice their customer into buying their product & services. Philip Kotler is considered the father of digital marketing who is the author of 60 marketing books and provides us important lessons that can be applied to our digital strategy. Before digital marketing, we have Traditional marketing, which is a conventional mode of marketing that helps to reach out to the semi-targeted audience with various offline advertising & promotion modes. CONSTITUENT OF DIGITAL MARKETING TRAFFIC ACQUISITION CHANNELS SEARCH ENGINE MARKETING(SEM): It is a form of internet marketing that involves the promotion of websites by increasing their visibility in search engine result pages (SERP) primarily through paid advertising. SEM may incorporate search engine optimization (SEO), which adjusts or rewrites website content and site architecture to achieve a higher ranking in search engine result pages to enhance pay per click. SOCIAL MEDIA MARKETING: Social media marketing involves the use of social media platforms to connect with the audience to build your brand, increase sales & drive website traffic. It also allows to publish great content on social media platforms & run social media advertisements. Major social media platforms are Facebook, Instagram, Twitter, LinkedIn, Pinterest, YouTube, and Snapchat. EMAIL MARKETING: It is an act of sending a commercial message particularly to a group of people, using email. It involves using emails to send advertisements, request business, or sales, or donations. It usually refers to sending email to enhance a merchant's relationship with a current or previous customer, encouraging customer loyalty, acquiring new customers, or convincing new customers to purchase something immediately. DISPLAY ADVERTISING: It is an online form of advertising in which a company's Ads appear on third-party sites or appear on the search engine result page such as publishers or social networks. This advertisement can increase the website page view of a company from most types of customers except the non-unauthenticated visitor who visits the site before. The main purpose of display advertising is to support brand awareness and it also helps to increase the purchase, intention of the consumers. AFFILIATE MARKETING: It is a type of performance-based marketing in which a business reward one or more affiliates for each visitor or customer brought by the affiliate's marketing efforts. The internet has increased the prominence of affiliate marketing. Amazon popularized the practice by creating the affiliate marketing program whereby the website and bloggers put the link to the Amazon page for a reviewed product to receive an advertising fee when a purchase is made. So, it is essentially a pay-for-performance marketing program where the act of selling is outsourced across a vast network. SUPPORTING CHANNELS MOBILE MARKETING: Mobile marketing is a multi-channel, digital marketing strategy aimed at reaching a target audience on their smartphones, tablets, or other mobile devices via websites, email, social media, and Apps. Mobile marketing is an important piece of the puzzle when it comes to building out any short-term or long-term marketing plan. From email to pay per click (PPC), search engine optimization (SEO)content marketing, and social media marketing, there is a mobile marketing channel to reach every part of your audience where they are most comfortable. mobile marketing can do wonders to drive brand value. WEBSITE: Website is the must-have tool for your business as it provides you with a dedicated platform where you can educate your audience about your brands, products, and services. This requires a solid understanding of your target audience and an effective content marketing strategy. Your website is an ideal channel for your content marketing campaigns. Through blogs, posts, and announcements you can provide existing and potential customers with valuable and relevant content to help them solve their pain points. Because websites have multimedia capabilities you can easily distribute different types of content in the form of articles, infographics, and even videos. If your website will have high-quality relevant and insightful content then your website will have increased organic traffic. WEB ANALYTICS: Web analytics is the measurement, collection, analysis, and reporting of internet data for understanding and optimizing web usage. The focus of web analytics is to understand the users of a site, their behavior, and their activities. The study of online user behavior and activities generate valuable marketing intelligence and provide - Performance measures of the website against the target. Insight on user behaviors and needs, and how the sight meets those needs. Optimization ability to make modifications to improve the website based on the result. Web analytics tools offer hundreds of metrics. all of them are interesting but only a few would be useful for measuring website performance. PROCESS FRAMEWORK OF DIGITAL MARKETING The framework of digital marketing is based on the 4 main objectives of digital marketing. 1.awareness 3. purchase 2.consideration 4. loyalty Loyalty Buyer -> loyal customer Purchase Interested -> buyer Awareness Unaware -> aware Consideration Aware -> interested

"Explore simplified data insights and trends from Amazon sales, perfect for beginners getting started with data analysis."

Analyzed Amazon sales data using Excel and Tableau to identify key trends, top 10 products, and profit fluctuations over time. Created an interactive, visually appealing dashboard to help stakeholders make data-driven decisions and optimize sales strategies.

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The advent of electronic commerce with growth in internet and network technologies has led customers to move to online retail platforms such as Amazon, Walmart, Nykaa etc. People often rely on customer reviews of products before they buy online.These reviews are often rich in information describing the product. Customers often choose to compare between various products and brands based on whether an item has a positive or negative review. More often, these reviews act as a feedback mechanism for the seller. Through this medium, sellers strategize their future sales and product improvement.Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing) that has gained much attention in recent years. The sentiment is a feeling, expression, thought, or judgment, and using sentiment analysis one can study the target audience’s sentiments towards an entity. It’s a form of text analysis that senses polarity (e.g. a positive or negative opinion) within the whole text, sentence, paragraph or phrase.

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A project focused on analyzing and processing E-commerce sales data using pandas. This repository includes code for data cleaning, transformation, and analysis of sales data, covering tasks such as standardizing column names, handling missing values, and deriving insights on sales performance.

amazon sales analysis project on Power BI

This data project can be used as a take-home assignment to learn Pyspark and Data Engineering.

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Discover a portfolio of comprehensive data analytics projects featuring SQL, Python, and Power BI. Uncover critical insights and data-driven solutions to real-world challenges through advanced data extraction, in-depth analysis, and dynamic visualization. Perfect for both novices and seasoned professionals aiming to elevate their data analytics.

Product Sales Tableau Dashboard

Amazon Sales Analytics – E-commerce Data Analysis Using mySQL

This repository contains an analysis of Amazon sales data. The analysis is performed using a Jupyter Notebook that processes and visualizes the data from a CSV file.

📊 Dive into Amazon Grocery Data: Explore the dataset's features and structure. 🛠️ Data Preprocessing: Learn how to prepare data for analysis. 📈 Predictive Modeling: Build and fine-tune ML models for sales and revenue prediction.

Amazon Sales Dashboard built with Power BI. Data cleaned and prepared in Excel, then visualized with interactive charts, KPIs, and filters. Provides insights into revenue trends, regional performance, and customer behavior. Showcases skills in data analysis, visualization, and business intelligence.