MBA - Business Analytics | Proven Results in Data Cleaning, Analysis & Visualization | SQL | MS-Excel | Power-BI |Python

Ujjwal Kumar  • Peerlist

About Dataset Overview: This project focuses on using a dataset containing information about grocery sales at a Supermart. The dataset includes columns such as Order ID, Customer Name, Category, Sub Category, City, Order Date, Region, Sales, Discount, Profit, State, month_no, Month, and year. We’ll explore this data, perform feature engineering, and build a machine learning model to predict sales or profit. This is a fictional dataset created for helping the data analysts to practice exploratory data analysis and data visualization.The dataset has data on orders placed by customers on a grocery delivery application. The dataset is designed with an assumption that the orders are placed by customers living in the state of Tamil-Nadu, India.

Superstore Grocery Sales

About DatasetContext While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that the iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.ContentEach app (row) has values for catergory, rating, size, and more.AcknowledgementsThis information is scraped from the Google Play Store. This app information would not be availablewithout it.InspirationThe Playstore apps data has enormous potential to drive app-making businesses to success.Actionable insights can be drawn for developers to work on and capture the Android market!

Google Play Store Apps

This dataset contains detailed records of coffee sales from a vending machine. The vending machine is the work of a dataset author who is committed to providing an open dataset to the community. It is intended for analysis of purchasing patterns, sales trends, and customer preferences related to coffee productsData Collection Period The dataset spans from March 2024 to Present time, capturing daily transaction data. And new information continues to be addedTasks ● TimeSeries Exploratory Data Analysis ● Nextday/week/month sales ● Specific customer purchases

Coffee_Sales

Hello everyone! 👋🏻 On the occasion of the closing Paris 2024 Summer Olympics, I share with you a Power-BI Dashboard presenting the history of the Olympics games from 1896(Athens) to 2022(Beijing) 📊.💡 Some Insights:🌐 155 different countries have already participated in the Olympics games, or nearly 80% of the worlds states.🥇 The athlete who holds the record for participated in the Olympics games is Ian Millar, a Canadian rider. He participated in ten editions of the Olympic games, from Munich 1972 to London 2012, known by the nickname "Captain Canada".🥇 The USA has hosted the largest number of editions and won the largest number of medals.🏃🏻‍♀️ Female Participation, although non-existent at the beginning, is tending towards parity nowadays.📺 The Olympics games are profitable in terms of revenues linked to TV rights.📈 The Cheapest Olympic games were held in cities that already had the majority of the necessary infrastructure and organizational experience (Exp:Paris).<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=power&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7229149326457872384">hashtag#Power</a>-BI <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=data&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7229149326457872384">hashtag#Data</a> Visualization<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=olympics&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7229149326457872384">hashtag#Olympics</a> <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=paris&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7229149326457872384">hashtag#Paris</a> 2024

Olympics Games

I recently completed an in-depth analysis of Netflix's dataset using advanced SQL techniques, including window functions, subqueries, string functions, and CTEs. The project explores various aspects of Netflix content, structured like a TV series, with each section as a "season" and each insight as an "episode."Key Insights:🔹 Frequent Collaborations: Identified actors who often collaborate, offering valuable insights for casting decisions.🔹 Diverse Genre Preferences: Analyzed genre preferences across countries, aiding content localization and marketing.🔹 Director Versatility: Highlighted versatile directors who've worked across multiple genres.🔹 Popularity Trends: Explored how the popularity of movies vs. TV shows has evolved over time.🔹 Genre Combinations: Investigated popular genre combinations for informed content creation.

Netflix Data Analysis

D-Mart-sales-Analysis-project-The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales.Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

D-Mart Sales Project

🚀 Excited to Share My Latest Project!I recently completed an SQL project analyzing sales data for a local shop pizza shop🍕.Through this project, I explored patterns in sales, uncovering insights like most ordered pizza types, category- wise distribution of pizza and many more insights.I used various SQL techniques, including complex joins and aggregations, to derive these insights.Check out the visual below to see some of the key findings. If you're interested in data analysis or have any questions about SQL, feel free to reach out!<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=data&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121">#Data</a> Analysis <a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=sql&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121">#SQL</a><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=analytics&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121">#Analytics </a><a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=data&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121">#Data</a>-Science<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=business&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121"> #Business</a>-Analytics<a target="_blank" rel="noopener noreferrer nofollow" class="text-green-text rich-link break-all link-break" href="https://www.linkedin.com/feed/hashtag/?keywords=data&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7226591465282437121"> #Data</a>-Analytics