AdventureWorks is a fictitious multinational manufacturing company. The data set used for this report is from the AdventureWorks2014 version. The goal of this project is to demonstrate how building powerful and beautiful (I hope :)) reports in Power BI by following the data visualization best practices and the data modelling patterns, showing the awesome features of Power BI like Report Page Tooltip, Calculated Groups, Forecasting, What-If parameters, Time Intelligence Functions, complex DAX code, custom charts and how it is easy to integrate Power BI with Python/R script in order to build machine learning models and so creating advanced and predictive analytics reports. Reports Pages are as follow: - P&L Overview: Profit & Loss Report, in which I analyzed the economic status of the company from the point of view of Revenues and Expenditures. In this report page I compared the trend of actual amount and budget amount, unfortunately for 2011 year only (missing data for other years). You can notice the powerful feature of “report page tooltip” in this page, by hovering over the bar charts. - Internet Sales Overview: in this report page I analyzed the sales of products to customers via Internet. You can notice the forecasting feature of Power BI and the use of what-if parameters in order to build advanced analytics reports. Also I made use of calculated groups (for further info about visit https://www.sqlbi.com/articles/introducing-calculation-groups/). - Reseller Sales Overview: similar to the previous one, but this time the focus is on the sales of products to resellers. Things to notice in this report page are the comparison of current sales vs last year sales, the comparison of actual sales amount vs budget/quota sales amount, and the running total and moving/running average charts. - Product Inventory Overview: inventory management analysis of the company. I analyzed the current stock on hold vs the recent sales revenue, showing the value of current stock, the current units in stock and the stock ratio. - Products Overview: in this report page I analyzed the products by category, color, size, “ABC” class too. Two very interesting and powerful things to notice: the basket analysis using DAX and the basket/association rules analysis using R script and apriori machine learning model. I made use of the custom chart called “Network Chart” to represent the association rules between products. Through the basket analysis the user could know which product is likely to be bought with another one. - Customers Overview: customer analysis by age group, location, status, and so on. The customer could be classified as “Bike Buyer” and “Non Bike Buyer”. So I decided to build a machine learning model (in this case I used XGBoost algorithm in Python) in order to predict if a (new) customer would be a bike buyer or not. The model has a prediction accuracy of about 84% which it is not bad. - Employees Overview: employee analysis by sales location, title, age. Thing to notice is the ribbon chart through which I can analyze the performance of employees over time. - Reseller Overview: analysis of the performance of the resellers. Important dimensions to analyze are the business type, the product line, the reseller location.

This project focuses on extracting, analyzing, and visualizing stock market data using Python. As part of the IBM Python for Data Science course, I scraped Tesla and GameStop's historical stock prices and revenue from online sources, cleaned the data, and created an interactive dashboard using Plotly to identify key trends.

Extracted and visualized Tesla and GameStop stock data with revenue. Used yfinance for stock prices, BeautifulSoup and pandas for scraping revenue data, and Plotly to create interactive graphs comparing historical share prices to quarterly revenue trends.

Python project demonstrating web scraping, data cleaning, time-series analysis, and visualization of stock and revenue data for Tesla and GameStop.

A Python data analysis project comparing Tesla and GameStop’s historical stock performance and revenue growth using yfinance, web scraping with BeautifulSoup, and interactive visualizations with Plotly.

This project extracts historical stock prices and quarterly revenue data for Tesla and GameStop using Python and yfinance. It visualizes the relationship between stock price and revenue through interactive Plotly dashboards. The analysis allows for easy comparison of financial performance trends between the two companies over time.

The project, titled "Stock Data Analysis and Visualization Dashboard," involves extracting financial data for Tesla, Amazon, AMD, and GameStop using Python and web scraping. This data includes historical share prices and quarterly revenue reports, which are visualized in a dashboard to identify trends and inform investment strategies effectively.

Stock & Revenue Data Analysis and Visualization

A comprehensive data analysis project that combines API integration and web scraping to extract, process, and visualize financial data for comparative stock analysis. The project demonstrates end-to-end data pipeline development for investment decision support.

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Comprehensive Analysis of Tesla and GameStop Stock Data with Revenue Insights and Visualization

No description available

In this project, we explore a wide range of data science tools, programming languages, libraries, and fundamental concepts. The repository contains Jupyter notebooks, code examples, markdown documents, and relevant data files that showcase our journey through the world of data science.

Stock and revenue analysis of Tesla and GameStop using Python, data scraping, and visualization

This project integrates AI/ML, web scraping, and financial data analysis to provide interactive insights into stock market performance. Using Yahoo Finance (yFinance API) and web scraping techniques, it collects real-time stock data and historical revenue figures for companies like Tesla (TSLA) and GameStop (GME).

Stock Data Analysis and Visualization: Exploring Historical Prices and Revenue Trends for Tesla and GameStop

This repository contains the analysis of Tesla stock data and visualization of quarterly revenue trends

Analysis and visualization of Tesla and GameStop stock data, including web scraping of revenue data and plotting stock price trends using Python.

End-to-end analysis of historical stock price and revenue data using Python, SQL, and data visualization techniques.

Data analysis project using Python, Pandas, web scraping and visualization to compare Tesla and GameStop stock and revenue data.

Analysis of Tesla and GameStop: Exploring the relationship between stock prices and quarterly revenues through data visualization and financial analysis.

Historical stock price and revenue analysis of Tesla and GameStop using Python, yfinance, web scraping, and data visualization.

A data analysis project using yfinance and web scraping to analyze historical stock and revenue data, including a dashboard visualization.

Historical stock price and revenue analysis of Tesla and GameStop using Python, yfinance, web scraping, and data visualization.

Analysis of Tesla and GameStop stock performance using Python, Pandas, BeautifulSoup, and Plotly. Includes data scraping, cleaning, visualization, and interactive stock-revenue graphs.

Analysis and visualization of Tesla and GameStop (GME) stock prices and revenue data using Python, yfinance, and web scraping.

A data analysis project using Python to explore Tesla and GameStop stock prices and revenue trends, including data cleaning and visualization.

Analysis of Tesla and GameStop stock prices and revenue using Python, yfinance, and web scraping techniques with data visualization.