Dataset

This dataset was created by BunY12345

Contents

Dataset

This dataset was created by Aasim Parwez

Contents

1) Data Introduction • The Power BI Sample Data is a financial sample dataset provided for Power BI practice and data visualization exercises that includes a variety of financial metrics and transaction information, including sales, profits, and expenses.

2) Data Utilization (1) Power BI Sample Data has characteristics that: • This dataset consists of numerical and categorical variables such as transaction date, region, product category, sales, profit, and cost, optimized for aggregation, analysis, and visualization. (2) Power BI Sample Data can be used to: • Revenue and Revenue Analysis: Analyze sales and profit data by region, product, and period to understand business performance and trends. • Power BI Dashboard Practice: Utilize a variety of financial metrics and transaction data to design and practice dashboards, reports, visualization charts, and more directly at Power BI.

Credit Card Financial Dashboard

This project presents a comprehensive Credit Card Financial Report created using Power BI. It aims to provide a detailed analysis of credit card operations on a weekly basis, offering real-time insights into key performance metrics and trends. The dashboard empowers stakeholders to monitor and analyze credit card operations effectively, facilitating informed decision-making processes.

Project Overview:

The "Credit Card Financial Dashboard" leverages a Kaggle dataset containing anonymized credit card transaction data. The dataset includes information such as transaction volume, transaction types, transaction amounts, customer demographics, spending behavior, and credit limits.

Project Contents:

1. Credit Card Transaction Report: This page provides a detailed analysis of credit card transactions, including transaction volume, types of transactions (e.g., purchases, cash advances), transaction amounts, and any anomalies or trends observed. Visualizations such as bar charts, line graphs, and pie charts are utilized to present the data effectively. 2. Credit Card Customer Report: The second page focuses on analyzing customer-related metrics, such as customer demographics (age, gender), spending behavior (average transaction amount, frequency of transactions), credit limits, and any customer-specific insights that can aid in decision-making processes. Visualizations such as demographic distributions, spending patterns, and credit limit distributions are included to provide a comprehensive overview of customer behavior.

Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

Hello Everyone, I made this Finance Dashboard in Power BI with the Finance Excel Workbook provided by Microsoft on their Website. Problem Statement The goal of this Power BI Dashboard is to analyze the financial performance of a company using the provided Microsoft Sample Data. To create a visually appealing dashboard that provides an overview of the company's financial metrics enabling stakeholders to make informed business decisions. Sections in the Report Report has multiple section's from where you can manage the data, like : • Report data can be sliced by Segments, Country and Year to show particular data. - Report Contain Two Navigation Page one is overview and other is sales dashboard page for better visualisation of data. - Report Contain all the important data. - Report Contain different chart and bar garph for different section .

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Comprehensive business intelligence analysis for Microsoft Power BI including financial metrics, founder insights, competitive positioning, and investment research. This dataset contains AI-powered analysis of leadership interviews, public content, and market intelligence for due diligence and competitive research purposes.

Big 4 Financial Audit & Compliance Interactive Dashboard (Power BI)

This dataset contains the Power BI project files and supporting data used to build an interactive dashboard for analyzing financial audits, compliance, and fraud detection across Big 4 accounting firms.

📊 Project Overview The dashboard was designed to provide dynamic insights into: - Audit scores by firm, region, and time period - Compliance status and audit outcomes - Fraud detection risk levels - Key performance indicators (KPIs) for audit and compliance teams

💻 Key Features ✅ Built using Power BI Desktop
✅ Data cleaning performed with Power Query
✅ DAX measures created for audit scoring and fraud prediction
✅ Interactive visuals with slicers, drill-through filters, and custom tooltips
✅ Real-time filtering of firms, regions, and risk levels

📝 How to Explore 1. Download the Big4_Financial_Audit_Dashboard.pbix file 2. Open in Power BI Desktop 3. Use the slicers and filters to interact with audit and compliance insights

✨ Applications This dashboard can assist audit and compliance teams in monitoring financial risk, identifying compliance gaps, and improving decision-making across accounting operations.

📂 Files Included - Big4_Financial_Audit_Dashboard.pbix: Power BI dashboard file - Big4_Audit_Data.csv: Sample dataset used for analysis

👉 Built as an academic project to demonstrate data analysis, visualization, and reporting skills using Power BI.

Socio-economic dashboard depicting Employment and Job Creation. Unemployment Rate (Province, Year, Qtr, Rate [percentage])Unemployment Rate by population group (Province, Year, Population Group, Rate [percentage])Unemployment Rate by gender (Province, Year, gender, Rate [percentage])Youth Unemployment Rate (Province, Year, Age cohorts, Rate [percentage])% Employment in formal and informal sectors (Province, Year, Sectors incl Agriculture, Sectors excl Agriculture, Private Households (domestic work), Rate [percentage])Labour particpation Rate (Province, Year, labour particpation Rate [percentage])Publication Date13 March 2022LineageData is sourced from Stats SA Quarterly Labour Force Surveys. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Six reports exist for this dashboard:Unemployment RateUnemployment Rate by population groupUnemployment Rate by genderYouth Unemployment Rate % Employment in formal and informal sectorsLabour particpation RateData SourceData from Stats SA; Labour force surveys and Quarterly Labour Force Surveys 2017 – 2021Dynamic dashboard reflecting the Outcome Indicator Release - Outcome Indicator: Unemployment rateUnemployment rate by population in WCUnemployment rate by gender in WCYouth unemployment ratePercentage of employed people working in the informal sector, including domestic work in WCLabour participation rate

This dataset contains 100 days of historical stock market data for three major technology companies: Apple (AAPL), Microsoft (MSFT), and Google (GOOGL). It was created as part of a Power BI dashboard project focused on financial analytics and data visualization.

The dataset includes the following fields:

Date – Trading date Open – Opening stock price Close – Closing stock price Volume – Number of shares traded Daily Return (%) – Calculated as the percentage change in closing price from the previous day 7-day Moving Average – Short-term trend indicator 30-day Moving Average – Long-term trend indicator This dataset is ideal for:

Building interactive dashboards in Power BI or Tableau Practicing time series analysis and financial KPIs Comparing stock performance across multiple companies Learning how to clean, transform, and visualize financial data The data has been pre-processed and enriched with calculated metrics to support business insights and decision-making. It is suitable for students, analysts, and data science enthusiasts interested in stock market analytics.

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

Comprehensive business intelligence analysis for Microsoft Azure including financial metrics, founder insights, competitive positioning, and investment research. This dataset contains AI-powered analysis of leadership interviews, public content, and market intelligence for due diligence and competitive research purposes.

Data is sourced from various health resources. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. HIV Prevalence and TB Success RateHIV prevalence amongst women attending antenatal clinics in the Western Cape (2012-2015) by district and yearHIV prevalence amongst women attending antenatal clinics in the province (2012-2015) by province and yearTB Programme Success Rate (2013/14-2018/19) by TB Measure2. Births and Maternal MortalitiesNeonatal in facility (0-28 days) mortality rate (2015/16-2018/19); by years and neonatal death rate in facility and mortality rate by 1,000 live births Facility maternal mortality rate (2002, 2005, 2008, 2011, 2014); by triennia (3 years) deaths by 1,000 live births in WC (incl count of maternal deaths, count of live births, and infant maternal mortality ration)(Child (under 5) and Infant (under 1) mortality rate (2011, 2012, 2013); filter years, Infant/Child age band; Years, District, Births and Deaths by age bandDelivery rate in facility to women under 20 years (2013/14-2018/19); filter by financial year (FY); delivery rate by FY, delivery rate, numerator (births to women <20), denominator (total births)3. Deaths and Life ExpectancyLeading underlying causes of death in the Western Cape (2012-2016) by years and cause of deathYears of life lost (YLL) by cause of death in the WC (2012-2016) by years and YLL cause of deathAverage Life Expectency (LE) at birth (2006, 2011, 2016) by year, province, and gender4. Travel time to facilitiesTravel time taken to health facility by households with expenditure less than R1200-SA (2013-2018); by year, province, and travel time to health facilityTravel time taken to health facility by households with expenditure less than R1200-WC (2013-2018); by year, province, population group, and travel time to health facilityPublication Date2 September 2021LineageData from various sources transformed to a BI format and used to develop dynamic Power BI dashboards reflecting Outcome Indicators: HIV prevalence amongst women attending antenatal clinics in the provinceAll DS-TB (drug-susceptible tuberculosis) client treatment success rateNeonatal in facility (0-28 days) mortality rateFacility maternal mortality rateDelivery rate in facility to women under 20 yearsLife Expectancy (LE)Leading underlying causes of death in the Western CapeTravel time taken to health facility by households with expenditure less than R1200 (SA and WC)Data Source2019 National Antenatal Sentinel HIV Survey, National Department of Health 2021;Annual report 2014/15-2020/21, DOH;District Health Information Systems;Mid-year population estimates, Stats SA; Life Expectancy Stats SA calculations;Mortality and Causes of Death in South Africa 2018, June 2021, Stats SA

This Power BI dashboard was developed to calculate and visualize end-of-service benefits for employees, utilizing data such as employee tenure, position, and salary. This project provides detailed insights into cost forecasting and benefits trends, offering a scalable template suitable for any organization. Additionally, it represents a comprehensive analysis of the entire company's data, enabling management to gain a deeper understanding of the financial and operational impacts of end-of-service benefits on the organization. This tool is designed to enhance strategic decision-making and improve financial management.

IMDB + OTT Platforms Movies Dashboard Dataset 🎬📊

About This Dataset

This comprehensive dataset combines movie and TV show data from major streaming platforms and cinema industries, designed for creating interactive analytics dashboards. Perfect for data analysis, visualization projects, and streaming industry insights.

🎯 What's Inside

Streaming Platforms Covered:

• Netflix (5,329+ titles)
• Amazon Prime Video (9,655+ titles)
• Disney+ Hotstar (1,321+ titles)
• Apple TV+ (167+ titles)

Cinema Industries:

• Hollywood Movies (10,000+ entries with box office data)
• Bollywood Movies (1,300+ entries with revenue insights)

🔍 Key Features & Analysis Potential

Content Analytics

• Genre Distribution: Analyze popular genres across platforms
• Release Trends: Track content release patterns over years
• Platform Comparison: Compare content libraries and strategies
• Geographic Analysis: Content production by country/region

Financial Insights

• Revenue Analysis: Box office performance tracking
• Budget vs Revenue: ROI calculations for movie productions
• Market Performance: Regional and global revenue comparisons

Quality Metrics

• Rating Analysis: User and critic scores across platforms
• Popular Content: Top-performing titles by platform
• Duration Patterns: Runtime distribution analysis

📂 Dataset Files

1. amazon_prime.csv

Amazon Prime Video content library with detailed metadata

Column	Description
show_id	Unique identifier for each show
type	Content type (Movie/TV Show)
title	Name of the content
director	Director(s) name
cast	Main actors/actresses
country	Production country
date_added	Date when added to platform
release_year	Original release year
rating	Content rating (PG, R, etc.)
duration	Runtime (minutes for movies, seasons for shows)
listed_in	Genre categories
description	Content summary

2. netflix_titles.csv

Netflix content catalog with comprehensive details

Column	Description
type	Content type (Movie/TV Show)
title	Name of the content
director	Director(s) name
cast	Main actors/actresses
country	Production country
date_added	Date when added to platform
release_year	Original release year
rating	Content rating (PG, R, etc.)
duration	Runtime information
listed_in	Genre categories
description	Content summary
day_added	Specific day when content was added
year_added	Year when content was added
month_added	Month when content was added
Genre Categories	Individual genre columns (Teen_TV_Shows, Horror_Movies, etc.)
User_Rating	User-generated ratings

3. disney_hotstar.csv

Disney+ Hotstar content database with family-friendly focus

Column	Description
title	Name of the content
description	Content summary
genre	Content category
year	Release year
age_rating	Age-appropriate rating
running_time	Duration in minutes
type	Content type (Movie/TV Show)

4. apple_tv.csv

Apple TV+ premium content collection with quality metrics

Column	Description
id	Unique content identifier
title	Name of the content
type	Content type (Movie/TV Show)
description	Content summary
release_year	Original release year
age_certification	Age rating classification
runtime	Duration in minutes
genres	Content categories
production_countries	Countries where produced
seasons	Number of seasons (for TV shows)
imdb_id	IMDB database identifier
imdb_score	IMDB user rating
imdb_votes	Number of IMDB votes
tmdb_popularity	The Movie Database popularity score
tmdb_score	The Movie Database user rating

5. hollywood_movies.csv

Hollywood box office and movie database with financial data

Column	Description
Title	Movie name
Date	Release date
Genre	Movie category
orig_lang	Original language
Revenue($)	Box office earnings in USD
Budget($)	Production cost in USD
country	Production country
score	Critical/User rating

6. bollywood_movies.csv

Bollywood film industry dataset with regional insights

Column	Description
title	Movie name
date	Release date
genre	Movie category
language	Primary language
revenue	Box office earnings
budget	Production cost
country	Production country
score	Critical/User rating

📊 Sample Insights You Can Discover

• Drama is the most popular genre across all platforms
• Significant content production increase post-2015
• ...

“AI-Powered Banking Analytics: Automated Power BI Documentation, Churn Prediction, and Transaction Forecasting”

Project Workflow 1. Data Acquisition (Kaggle) • Dataset sourced from Kaggle (credit card / banking dataset). • Contains customer demographics, credit card transactions, and account details. • Cleaned and transformed data in Power BI for dashboard building.

1. Interactive Power BI Dashboard • Built two key analytics pages:
2. Customer Churn Insights → shows churn risk, drivers, segmentation.
3. Transaction Forecasting → predicts future monthly transactions with confidence bands. • Added KPI cards, slicers, and professional formatting. • Ensured design follows: customer risk, forecasting, and governance. _
4. Automated Documentation (Python + VPAX) • Exported the Power BI data model (VPAX) using DAX Studio. • Created a Python script to automatically generate: o Word doc with model documentation. o Excel file with tables, relationships, and fields. o ER diagram image. • This automation saves analysts hours of manual work and enforces governance. _
5. Churn Prediction Model (Python + Power BI) • Built a Random Forest model for churn prediction. • Output: o Customer-level churn probability. o Risk categories (Low, Medium, High). o Feature importance (drivers of churn). • Exported predictions to Excel → Imported into Power BI. • Added Churn Risk Dashboard: o Distribution of churn risk. o Top churn drivers (feature importance bar chart). _
6. Transaction Forecasting Model (Python + Prophet) • Used Prophet (Facebook’s forecasting library) to model monthly transaction volumes. • Forecasted next 12 months with confidence intervals (yhat_lower, yhat_upper). • Exported results to Excel → Integrated into Power BI. • Added Transaction Forecasting Dashboard: o Actual vs Forecast line chart (with confidence band). o KPI cards (Next Month Forecast, YoY Growth). o Clustered column chart for recent 12 months. _
7. End-to-End Data & AI Pipeline • Data Source (Kaggle) → Power BI Dashboard → Automated Documentation → AI/ML Models → Power BI Insights.

File Details: File / Folder Name Description .idea/ PyCharm IDE configuration folder (auto-generated). Churn Prediction + Forecasting.py Main Python script for churn prediction (Random Forest) and transaction forecasting (Prophet). churn_model.pkl Saved machine learning model (Random Forest) for churn prediction. Churn_Predictions.xlsx Excel output of churn probabilities and risk categories per customer. Credit Card Financial Dashboard.pbix Power BI dashboard file (interactive BI report). Credit Card Financial Dashboard.pdf Exported PDF version of the Power BI dashboard. credit_card.xlsx Kaggle dataset (credit card transactions / account features). customer.xlsx Kaggle dataset (customer demographic and account info). DocumentationGenerator.py Python script that parses VPAX model and generates automated Power BI documentation. Feature_Importance.xlsx Feature importance scores from churn model (top churn drivers). forecast_model.pkl Saved Prophet model for forecasting monthly transactions. LICENSE License file for open-source/public sharing. model.vpax Exported Power BI data model (via DAX Studio) for documentation. PowerBI_Documentation.docx Word output of auto-generated Power BI documentation. PowerBI_Documentation.xlsx Excel output of auto-generated Power BI documentation. PowerBI_ER_Diagram.png Entity-Relationship diagram image generated from Power BI model. README.md Markdown summary file for GitHub/Kaggle. Transaction_Forecast.xlsx Excel output containing actuals + forecast (Prophet) with confidence bounds.

Turkish National Team Financial and Performance Data (2018-2024)

This dataset provides comprehensive financial and performance data for the Turkish National Football Team from 2018 to 2024. It covers various aspects of the team’s annual operations, including revenue sources, expenses, and match outcomes. This dataset is ideal for exploratory data analysis (EDA), data visualization, and machine learning projects focused on sports analytics, financial trends, and performance metrics.

Dataset Content The dataset includes the following key features:

Year: The year in which data was recorded (2018-2024). Fixture: Type of match, including "Friendly", "Qualifying", and "Tournament" games. Match Result: Outcome of the match, represented as "Win", "Lose", or "Draw". Goals: Total goals scored by the team in each match. Transport Cost (Euro): Expenses related to travel and logistics. Office Expenses (Euro): Administrative costs associated with managing the team. Ticket Revenue (Euro): Income generated from ticket sales for matches. Sponsor Revenue (Euro): Revenue from team sponsors. Attendance: Average number of spectators per match. Total Salary Expense (Euro): Annual total salary costs for players. Total Bonus Expense (Euro): Annual bonus expenses for players. Merchandise Revenue (Euro): Revenue from team merchandise sales. Advertising Revenue (Euro): Revenue generated from advertising, including match-day ads and sponsorships. Medical Expenses (Euro): Costs associated with player health and medical care. Key Features Null Values: Includes some null values to simulate real-world data, encouraging users to clean and preprocess data for analysis. Outliers and Variations: Financial and performance data may exhibit variations and outliers, reflecting realistic fluctuations in team revenues and expenses. Mixed Data Types: Data types include integers, floats, and categories, providing opportunities for various data processing and feature engineering techniques. Usage Examples This dataset can be used in a variety of projects:

Exploratory Data Analysis (EDA): Gain insights into the Turkish National Team's financial structure, performance patterns, and trends. Data Visualization: Create compelling visualizations using tools like Tableau and Power BI to showcase trends and insights. Machine Learning: Train models for predictive analytics, such as predicting match outcomes based on historical financial data or estimating future revenue growth. This dataset provides a realistic and multi-faceted view of the Turkish National Team's financial and operational metrics, making it a valuable resource for sports analysts, data scientists, and financial analysts interested in the sports industry. Enjoy exploring the data and uncovering insights about one of Turkey's most beloved national teams!

PSX Listed Companies Dataset

Overview

This dataset contains detailed information about companies listed on the Pakistan Stock Exchange (PSX). The PSX is the premier stock exchange in Pakistan, where companies from various sectors are publicly listed for trading. The data was scraped from the official PSX website and includes essential information about each listed company, its representative, and contact details. This dataset can be valuable for anyone interested in financial markets, business research, or investment opportunities within Pakistan.

Data Features

The dataset contains the following columns:

• Representative: Name of the company representative.
• Designation: The representative's role or title within the company.
• Company: Name of the listed company.
• Address: Official mailing address of the company.
• Phone: Primary contact number.
• Phone 2: Additional contact number (if available).
• Fax: Fax number (if available).
• Date of Listing: The date when the company was listed on the PSX.
• Email: The contact email address of the representative or company.
• URL: The company's website.
• Registrar Details: Information about the company's registrar, which handles the company's share-related matters.

Sectors Represented

The dataset includes companies from a wide variety of sectors, reflecting the diversity of industries on the PSX. Some key sectors include: - Automobile Assembler - Cement - Commercial Banks - Fertilizer - Food & Personal Care Products - Pharmaceuticals - Technology & Communication - Textile Composite

And many more, totaling 37 different sectors.

Usage

This dataset can be used for multiple purposes: 1. Financial Analysis: Explore the performance of different sectors and companies listed on the PSX. 2. Investment Research: Identify key players in different industries for investment opportunities. 3. Business Development: Build contact lists for companies within a specific sector. 4. Data Science & Machine Learning Projects: Use this dataset for clustering, classification, or sentiment analysis in financial markets.

Data Format

The dataset is available in CSV format, making it easy to load into data analysis tools like Pandas, Excel, or Power BI. It's structured for easy exploration and can be integrated into financial models or research projects.

Acknowledgements

The data was scraped from the official PSX website using a custom Python script. Special thanks to the open-source community for tools like Selenium, BeautifulSoup, and Pandas, which made this project possible.

License

This dataset is provided for educational and research purposes. Please give proper attribution when using this dataset in your work.

Feel free to explore, analyze, and share your insights!

This dataset contains historical stock price data for Tesla, Inc. (TSLA) starting from its IPO date, June 29, 2010, to January 1, 2025. The dataset includes daily records of Tesla's stock performance on the NASDAQ stock exchange. It is ideal for time-series analysis, stock price prediction, and understanding the long-term performance of Tesla in the stock market.

The dataset consists of the following columns:

1. Date: The trading date.
2. Open: Opening stock price on the given date.
3. High: The highest stock price during the trading day.
4. Low: The lowest stock price during the trading day.
5. Close: The closing stock price for the day.
6. Adj Close: Adjusted closing price (corrected for dividends and stock splits).
7. Volume: The number of shares traded during the day.

Use Cases of Tesla Stock Historical Data

1. Time-Series Analysis

   • Analyze trends in Tesla's stock prices over time.
   • Identify seasonality, volatility, and long-term patterns in Tesla’s performance.

2. Stock Price Prediction

   • Develop predictive models to forecast future stock prices using techniques such as ARIMA, LSTMs, or regression.

3. Investment Strategy Evaluation

   • Backtest trading strategies by simulating trades based on historical price movements.
   • Analyze returns of investment strategies such as moving averages, RSI, or Bollinger Bands.

4. Market Sentiment Analysis

   • Correlate Tesla’s stock performance with news sentiment, earnings reports, and market events.

5. Portfolio Diversification

   • Evaluate Tesla’s performance compared to other stocks or indices to assess its role in a diversified portfolio.

6. Risk Management

   • Calculate volatility, beta, and other risk metrics to assess the risk associated with investing in Tesla stock.

7. Economic and Market Studies

   • Study how macroeconomic indicators (like inflation, interest rates) influence Tesla’s stock price.
   • Analyze Tesla’s performance during major economic events such as the COVID-19 pandemic or policy changes.

8. Stock Splits and Adjustments Analysis

   • Examine the impact of Tesla’s stock splits on price and trading volume.

9. Educational Purposes

   • Serve as a dataset for academic projects, coursework, or tutorials on financial data analysis.

10. Correlation with Sector Trends

    • Compare Tesla’s stock performance with other automotive or renewable energy companies.

11. Data Visualization and Dashboarding

    • Create dashboards using tools like Tableau, Power BI, or Python libraries to visualize Tesla’s stock performance metrics.

12. A/B Testing for Financial Applications

    • Use historical stock data for controlled experiments in finance-related applications to improve decision-making tools.

This project compares the financial performance and business strategies of Burger King and McDonald’s between 2021 and 2023.

The workflow began with raw data for both companies, which was cleaned and structured in Excel. Once cleaned, the datasets were converted into CSV files for use in Python. Using Python (with Pandas), the data was transformed, aggregated, and prepared for visualization.

The cleaned datasets for both Burger King and McDonald’s were then visualized using Power BI to highlight trends in system-wide sales, revenue breakdowns, franchise vs. company-operated structures, and store expansions across U.S. and international markets. The Power BI dashboard, which captures all these insights in a visual format, was exported to PDF and included as part of this project.

The uploaded files include:

Two CSV files: one for McDonald's and one for Burger King

A PDF containing the Power BI dashboard with visual comparisons

Key insights covered in the dashboard include:

Year-over-year sales growth

Differences in revenue models (franchise-heavy vs. company-operated)

Store count and expansion trends in different regions

Post-COVID recovery patterns

Metrics like revenue per store, goodwill, capital expenditure, and efficiency ratios

This project is a complete pipeline — starting from raw data, moving through Excel cleaning and Python transformation, and ending with a visual storytelling layer in Power BI. It’s designed to show how data can be taken from spreadsheet form and turned into actionable business insights.