This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This data set is perfect for practicing your analytical skills for Power BI, Tableau, Excel, or transform it into a CSV to practice SQL.

This use case mimics transactions for a fictional eCommerce website named EverMart Online. The 3 tables in this data set are all logically connected together with IDs.

My Power BI Use Case Explanation - Using Microsoft Power BI, I made dynamic data visualizations for revenue reporting and customer behavior reporting.

Revenue Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total Sales, Product Sales, or Categorical Sales. - Line Graph Visual that shows Total Revenue by Month of the entire year. This graph also changes to calculate Total Revenue by Month for the Total Sales by Product and Total Sales by Category if selected. - Bar Graph Visual showcasing Total Sales by Product. - Donut Chart Visual showcasing Total Sales by Category of Product.

Customer Behavior Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total or by continent selected on the map. - Interactive Map Visual showing key statistics for the continent selected. - The key statistics are presented on the tool tip when you select a continent, and the following statistics show for that continent: - Continent Name - Customer Total - Percentage of Products Sold - Percentage of Total Customers - Percentage of Total Transactions - Percentage of Total Revenue

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.

Change-To-Liabilities Time Series for Microsoft Corporation. Microsoft Corporation develops and supports software, services, devices, and solutions worldwide. The company's Productivity and Business Processes segment offers Microsoft 365 Commercial, Enterprise Mobility + Security, Windows Commercial, Power BI, Exchange, SharePoint, Microsoft Teams, Security and Compliance, and Copilot; Microsoft 365 Commercial products, such as Windows Commercial on-premises and Office licensed services; Microsoft 365 Consumer products and cloud services, such as Microsoft 365 Consumer subscriptions, Office licensed on-premises, and other consumer services; LinkedIn; Dynamics products and cloud services, such as Dynamics 365, cloud-based applications, and on-premises ERP and CRM applications. Its Intelligent Cloud segment provides Server products and cloud services, such as Azure and other cloud services, GitHub, Nuance Healthcare, virtual desktop offerings, and other cloud services; Server products, including SQL and Windows Server, Visual Studio and System Center related Client Access Licenses, and other on-premises offerings; Enterprise and partner services, including Enterprise Support and Nuance professional Services, Industry Solutions, Microsoft Partner Network, and Learning Experience. The company's Personal Computing segment provides Windows and Devices, such as Windows OEM licensing and Devices and Surface and PC accessories; Gaming services and solutions, such as Xbox hardware, content, and services, first- and third-party content Xbox Game Pass, subscriptions, and Cloud Gaming, advertising, and other cloud services; search and news advertising services, such as Bing and Copilot, Microsoft News and Edge, and third-party affiliates. It sells its products through OEMs, distributors, and resellers; and online and retail stores. The company was founded in 1975 and is headquartered in Redmond, Washington.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

The Data Visualization Tools Market size was valued at USD 10.39 billion in 2023 and is projected to reach USD 22.12 billion by 2032, exhibiting a CAGR of 11.4 % during the forecasts period. This unprecedented growth is attributed to the increasing demand for real-time data analysis, the need for effective decision-making, and the rising adoption of cloud-based data visualization tools. Additionally, government initiatives aimed at improving data literacy and the implementation of data visualization solutions in various industry verticals are further fueling market growth. Data visualization tools enable businesses and individuals to transform complex data into insightful visual representations. Tools like Tableau, Power BI, and Google Data Studio offer user-friendly interfaces to create interactive charts, graphs, and dashboards. They help analyze trends, patterns, and correlations, aiding decision-making processes. Advanced features include real-time data updates, collaboration capabilities, and integration with various data sources like databases and cloud services.

According to Cognitive Market Research, the global Business Intelligence market size is USD 16.9 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.50% from 2023 to 2030.

The demand for Business Intelligence s is rising due to the increasing data complexity and rising focus on data-driven decision-making.
Demand for adults remains higher in the Business Intelligence market.
The Business intelligence platform category held the highest Business intelligence market revenue share in 2023.
North American Business Intelligence will continue to lead, whereas the Asia-Pacific Business Intelligence market will experience the most substantial growth until 2030.

Growing Emphasis on Data-Driven Decision-Making to Provide Viable Market Output

In the Business Intelligence Tools market, the increasing recognition of the strategic importance of data-driven decision-making serves as a primary driver. Organizations across various industries are realizing the transformative power of insights derived from BI tools. As the volume of data generated continues to soar, businesses seek sophisticated tools that can efficiently analyze and interpret this information. The ability of BI tools to convert raw data into actionable insights empowers decision-makers to formulate informed strategies, enhance operational efficiency, and gain a competitive edge in a data-centric business landscape.

In June 2020, SAS and Microsoft established a comprehensive technology and go-to-market strategic alliance. As part of the collaboration, SAS's industry solutions and analytical products will be moved to Microsoft Azure, SAS Cloud's preferred cloud provider.

Source-news.microsoft.com/2020/06/15/sas-and-microsoft-partner-to-further-shape-the-future-of-analytics-and-ai/#:~:text=and%20SAS%20today%20announced%20an,from%20their%20digital%20transformation%20initiatives.

Rise in Adoption of Advanced Analytics and Artificial Intelligence to Propel Market Growth

Another significant driver in the Business Intelligence Tools market is the escalating adoption of advanced analytics and artificial intelligence (AI) capabilities. Modern BI tools are incorporating AI-driven functionalities such as machine learning algorithms, natural language processing, and predictive analytics. These technologies enable users to uncover deeper insights, identify patterns, and predict future trends. The integration of AI not only enhances the analytical capabilities of BI tools but also automates processes, reducing manual efforts and improving the overall efficiency of data analysis. This trend aligns with the industry's pursuit of more intelligent and automated BI solutions to derive maximum value from data assets.

In March 2020, IBM created a new, dynamic global dashboard to display the global spread of COVID-19 with the assistance of IBM Cognos Analytics. The World Health Organization (WHO) and state and municipal governments provide the COVID-19 data displayed in this dashboard.

Source-www.ibm.com/blog/creating-trusted-covid-19-data-for-communities/

Market Dynamics of the Business Intelligence tool Market

Key Drivers for Business Intelligence tool Market

Increasing Demand for Data-Driven Decision Making Across Various Sectors: As companies produce vast amounts of data, there is an escalating requirement for tools that can analyze and convert raw data into actionable insights. Business Intelligence (BI) tools facilitate quicker and more precise strategic decisions in areas such as sales, finance, operations, and customer service.

Transition to Cloud-Based BI Solutions for Enhanced Scalability and Accessibility: Organizations are progressively shifting from on-premise BI systems to cloud-based solutions, which provide real-time access, foster collaboration, and reduce infrastructure expenses. This transition enhances scalability and accommodates hybrid or remote work settings.

Incorporation of AI and Machine Learning for Enhanced Predictive Analytics: Sophisticated BI tools are incorporating artificial intelligence and machine learning technologies to deliver predictive forecasting, anomaly detection, and natural language querying—thereby improving the accuracy of business forecasts and enhancing user accessibility.

Key Restraints for Business Intelligence tool Market

High Initial Setup and Customization Costs for SMEs: Small and medium-sized...

Sales_Analysis_Project

Graded Power BI Mini Project - Sales Analysis Using Power BI | Data Science Course

🎓 Data Science & Business Analytics Program – IIT Guwahati (via Emeritus)

Objective

Design and develop an interactive dashboard using Power BI to analyze customer order data. This includes deriving insights related to:

• ✅ Total order volume
• ✅ Sales trends by product category
• ✅ Regional customer segmentation
• ✅ Monthly growth patterns
• ✅ Key customer behavior metrics

Learning Outcomes

• Translate complex datasets into engaging, data-driven stories
• Build structured, audience-focused narratives to support strategic decisions
• Apply visualization and DAX techniques to highlight business KPIs

🗂️ Dataset Components

🧑 Customers Table

Captures demographic & regional attributes: - CustomerID, Name, Age, Gender, Region, CustomerSince, Email

🛒 Orders Table

Captures purchase activity: - OrderID, CustomerID, ProductID, OrderDate, Quantity, TotalSales

📦 Products Table

Provides product and pricing metadata: - ProductID, ProductName, Category, UnitPrice, Supplier

Power BI Implementation

🔗 Data Modeling

• Built relationships between Customers, Orders, and Products
• Created a star schema for efficient filtering and aggregation

Visualizations Created

DAX Measures

• Average Order Value
• Monthly Sales Growth (%) = (Current - Previous) / Previous

Design & Formatting Strategy

• Used theme-consistent color palettes and data labels
• Structured layout to support intuitive storytelling
• Added headers, custom tooltips, and cleaned slicers
• Saved as: Sales_Analysis_Dashboard.pbix

Skills & Tools Applied

Reflections

This project deepened my ability to merge technical BI skills with business storytelling. By transforming raw sales and customer data into visual narratives, I learned how to:

• Identify drivers of revenue by product and region
• Segment and profile customers by behavior and demographics
• Present actionable insights to stakeholders in a concise format

“Data becomes meaningful when it tells a story that leads to better decisions.”

Crafted with ♥ by Kanak Baghel | LinkedIn

Data Import and Table Selection: Import Excel data into Power BI. Select specific tables (Calendar, Customer, Product, Sales, Terriority). Data Modeling: Design star schema architecture in Model view. Establish relationships between tables. Data Transformation: Filter Calendar table for years 2017 and 2018. Remove unnecessary columns from the Calendar table. Utilize Power Query Editor for data manipulation. DAX Measures: Create measures for analyzing sales data. Use DAX functions to calculate total sales, tax amount, total orders, distinct product count, etc. Add comments to DAX measures for clarity. Visualization: Create matrices to display summarized data. Format measures (e.g., change to currency). Utilize visual elements like icons and tooltips for better understanding. Drill-Down Analysis: Implement drill-down functionality to explore data hierarchically. Additional Measures: Calculate total customers and percentage of distinct customers. Analyze product-related metrics (e.g., max price, weight values). Data Quality Analysis: Identify and analyze empty cells in specific columns. Multiple Sheets and Visuals: Create multiple sheets with different matrix tables. Utilize slicers for interactive filtering. Implement visual filters for dynamic data exploration. Advanced DAX Functions: Utilize SUMX function for calculating total sales including tax. Calculate dealer margin using SUMX function. Conclusion: Summarize the project and its focus on measures, matrix tables, and advanced DAX functions. Overall, your project plan covers various aspects of data analysis and visualization in Power BI, from data import to advanced calculations and visualization techniques, providing a comprehensive guide for analysis and decision-making.

The contoso_TR.accdb dataset is a Microsoft Access relational database representing a localized version of the well-known Contoso retail business scenario, tailored for the Turkish market (TR). It provides a rich, realistic sample of sales, product, customer, and financial data that can be used for learning, reporting, and analytics purposes.

🧾 Dataset Description This dataset simulates the operations of Contoso Ltd., a fictitious retail company that sells electronic products and accessories through various sales channels across Turkey. The database is designed to support a wide range of data-driven tasks such as:

Data modeling and relationship design

SQL querying and data transformation

Business intelligence and reporting

Dashboard creation using Power BI or Excel

Training in Access VBA and macros

🌍 Localization Language: Turkish (column names and values are adapted)

Currency: Turkish Lira (₺)

Region: Turkey-specific location data (e.g., cities, regions, and stores)

Date format: gg.aa.yyyy (Turkish date format)

✅ Use Cases Practicing Access SQL queries

Creating forms and reports in Microsoft Access

Developing ETL pipelines using sample business data

Preparing Power BI dashboards with Turkish-language data

Learning how to normalize and relate data in a business context

📌 Notes The dataset is static and does not reflect real-time data.

No real customer information is included; all data is synthetic.

It is ideal for educational and demonstration purposes.

If you'd like, I can help you:

Design a Power BI report using this dataset

Convert it to SQL Server or another format

Write SQL queries to extract business insights

🚘 Electric Vehicle (EV) Sales Analysis & Forecasting

The Electric Vehicle (EV) Sales Analysis & Forecasting project explores India’s EV adoption trends from 2014 to 2024, combining BigQuery for data analytics and Power BI for visualization. This end-to-end data project demonstrates expertise in SQL-based data analysis, time-series forecasting using BigQuery ML (ARIMA+), and interactive dashboard design. This repository presents an in-depth data analysis and visualization of Electric Vehicle (EV) sales trends across India using Microsoft Excel, SQL, Python and Power BI.

The Electric Vehicle Sales Dashboard is an interactive and visual analytics solution built using Power BI, focused on analyzing EV adoption trends across India. It includes KPIs, comparative charts, and advanced metrics to assist stakeholders in understanding EV sales performance from 2014 to 2024.

⚙️ Tech Stack

Google BigQuery → Data storage, cleaning, and analysis using SQL

BigQuery ML (ARIMA_PLUS) → Time series forecasting for future EV sales prediction

Power BI → Interactive dashboard for visualization and storytelling

Pyhton → For eda and visualization

Excel / CSV → Initial dataset preparation and transformation

🔹 Key Features: ✅ Total EV Sales Trends: Year-over-year and month-over-month change

✅ State-wise EV Performance: Compare EV adoption across Indian states

✅ Vehicle Category Breakdown: 2W, 3W, 4W, Buses, Others

✅ Dynamic Filtering: Use slicers to filter by Year, Month, State.

✅ KPI Cards: Total Sales, Average Sales

📊 Dashboard Overview Components:

Section Description 🔹 KPI Cards Highlight key performance indicators like Total Sales, Average Sales. 📈 Trend Charts Year-wise and month-wise sales volume (bar/line charts) 🗺️ Geo Analysis State-wise sales with ranking and contribution 🚗 Vehicle Type Pie Distribution of sales by 2W, 3W, 4W, Bus, Others 📅 Time Series Filtering and Interactive slicers for Year, Month, and State. 📊 YOY / MOM Change Dedicated visual section with % growth/dip over time

Dashboard using Power BI

https://github.com/user-attachments/assets/eca3b9c3-b446-4447-a38a-d61f5572a712" alt="DashBoards">

Dashboard using Excel

https://github.com/user-attachments/assets/f7cc0949-4581-4cb3-b3ab-ea9242bce803" alt="Dashboard excel">

🗓️ Year-on-Year EV Sales (2014–2024) - EV sales have grown from 2.4K in 2014 to 1.5M in 2023. - Peak growth observed in: - 2021 → 2022: +209% YoY growth - 2020 → 2021: +165% YoY growth - 2024 saw a drop (as of current data): -90.61% https://github.com/user-attachments/assets/02965465-d6cf-45bf-b127-40feb6fe9963" alt="yoy">

📆 Month-on-Month (MoM) Analysis - Highest sales months: November, December, and January - Noticeable drop in February followed by recovery in March https://github.com/user-attachments/assets/83bce2dd-b2ae-43b9-bdb2-def5b9dbe37d" alt="mom">

📍 State-wise Performance - Top Performing States: - 🥇 Uttar Pradesh: ~730K units - 🥈 Maharashtra: ~400K units - 🥉 Karnataka: ~320K units - Lower adoption in northeastern and union territory regions - https://github.com/user-attachments/assets/1ee491b5-9016-4a1e-850f-34e8b9a9aab3" alt="state wise ev">

EV State wise Sale correlation https://github.com/user-attachments/assets/4c5fc55d-389d-4bed-a9bb-eebe8ce31a11" alt="EV corr">

Top 5 State KDE Plot https://github.com/user-attachments/assets/1b9e2f69-9ea0-4ac4-8c26-0350fb75bd75" alt="EV kde plot">

🚗 EV Sales by Vehicle Type - 2-Wheelers: 50.3% of total sales - 3-Wheelers: 45.1% - 4-Wheelers and Buses: Small but growing segment https://github.com/user-attachments/assets/2fc64426-4e3d-4a14-b058-f7c67040663b" alt="vtype wise ev">

📅 Year-wise EV Sales Trend EV adoption has grown significantly from 2,392 units in 2014 to over 1.5 million units in 2023

Fastest growth period: 2020–2022, with YoY gains of 165% and 209%

2024 shows a decline (-90.6%) due to possible data incompleteness https://github.com/user-attachments/assets/d60567fb-fc61-463f-b0e8-34ddd14f1b8d" alt="Year wise ev">

------------------------------...

Football Manager Players Dataset

Overview

Dive into the ultimate treasure trove for football enthusiasts, data analysts, and gaming aficionados! The Football Manager Players Dataset is a comprehensive collection of player data extracted from a popular football management simulation game, offering an unparalleled look into the virtual world of football talent. This dataset includes detailed attributes for thousands of players across multiple leagues worldwide, making it a goldmine for analyzing player profiles, scouting virtual stars, and building predictive models for football strategies.

Whether you're a data scientist exploring sports analytics, a football fan curious about your favorite virtual players, or a game developer seeking inspiration, this dataset is your ticket to unlocking endless possibilities!

Dataset Description

This dataset is a meticulously curated compilation of player statistics from five CSV files, merged into a single, unified dataset (merged_players.csv). It captures a diverse range of attributes for players from various clubs, nations, and leagues, including top-tier competitions like the English Premier Division, Argentina's Premier Division, and lower divisions across the globe.

Key Features

• Rich Player Attributes: Over 70 columns covering essential metrics such as:
  • Basic Info: UID, Name, Date of Birth (DOB), Nationality, Height, Weight, Age
  • Club & Position: Club, Position (e.g., AM, DM, GK), Based (league/division)
  • Performance Stats: Caps, Appearances (AT Apps), Goals (AT Gls), League Appearances, League Goals
  • Technical Skills: Acceleration, Passing, Dribbling, Finishing, Tackling, and more
  • Mental Attributes: Work Rate, Vision, Leadership, Determination
  • Physical Attributes: Pace, Strength, Stamina, Agility
  • Market Value: Transfer Value (e.g., $0 to millions)
  • Miscellaneous: Preferred Foot, Media Handling, Injury Proneness
• Global Coverage: Players from diverse regions, including Europe (England, Spain, Italy), South America (Argentina, Brazil), Asia (South Korea, China), Africa (Ivory Coast, Burkina Faso), and North America (USA, Mexico).
• Varied Player Types: From young prospects (15–18 years old) to veteran stars (up to 45 years old), including amateurs, youth players, and professionals.
• Realistic Insights: Includes attributes like Media Description (e.g., "Young winger," "Veteran striker") and injury status, mirroring real-world football dynamics.

Dataset Size

• Rows: Thousands of player records (exact count depends on deduplication).
• Columns: 70+ attributes per player.
• File: merged_players.csv (UTF-8 encoded for compatibility with special characters).

Potential Use Cases

• Sports Analytics:
  • Analyze player attributes to identify key traits for success by position (e.g., what makes a top goalkeeper?).
  • Predict transfer values based on skills, age, and performance stats.
  • Cluster players by playing style or potential using machine learning.
• Scouting & Strategy:
  • Build a dream team by filtering players based on specific attributes (e.g., high Pace and Dribbling for wingers).
  • Compare young talents vs. experienced veterans for team-building strategies.
• Gaming & Modding:
  • Create custom Football Manager databases or mods.
  • Analyze game balance by studying attribute distributions.
• Visualization:
  • Develop interactive dashboards to explore player stats by league, nationality, or position.
  • Map player origins to visualize global football talent distribution.
• Education & Research:
  • Use as a teaching tool for data science, exploring data cleaning, merging, and analysis.
  • Study correlations between mental/physical attributes and in-game performance.

Why This Dataset Stands Out

• Comprehensive: Covers every aspect of a player's profile, from technical skills to personality traits.
• Diverse: Includes players from top-tier to lower divisions, offering a broad spectrum of talent.
• Engaging: Perfect for football fans and data enthusiasts alike, blending gaming with real-world analytics.
• Ready-to-Use: Merged and cleaned for immediate analysis, with consistent column structure across all records.

Getting Started

1. Download: Grab merged_players.csv and load it into your favorite tool (Python/pandas, R, Excel, etc.).
2. Explore: Check out columns like Transfer Value, Position, and Media Description to start your analysis.
3. Analyze: Use Python (e.g., pandas, scikit-learn) or visualization tools (e.g., Tableau, Power BI) to uncover insights.
4. Share: Build models, visualizations, or scouting reports and share your findings with the Kaggle community!

Example Questions to Explore

• Which young players (<18 years) have the highest poten...

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂