Diabetes Analytics Dashboard – Power BI 🩺📊 This practice dashboard is built for Data Analytics, Data Visualization, and Data Science learning. It provides meaningful insights into diabetes risk factors using interactive visuals and advanced analytics.

🔹 Key Metrics – Total patients, BMI, glucose, blood pressure, and insulin levels. 🔹 Diabetes Risk Segmentation – Categorized into High, Medium, and Low risk groups. 🔹 Trends & Distribution – Glucose vs. Age, BMI categories, and Blood Pressure analysis. 🔹 Correlation Analysis – Exploring the relationships between glucose, BMI, and diabetes risk. 🔹 Gauge & Pie Charts – Visualizing risk percentage, BMI distribution, and glucose levels. 🔹 Interactive Filters & Drilldowns – Allowing deeper exploration of specific patient groups. 🔹 Predictive Insights – Identifying potential risk patterns through visual analytics.

This project helps in understanding data-driven healthcare insights using Power BI. Thanks to Kaggle for the dataset!

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.

A high-quality, clean dataset simulating global cosmetics and skincare product sales between January and August 2022. This dataset mirrors real-world transactional data, making it perfect for data analysis, Excel training, visualization projects, and machine learning prototypes.

📁 Dataset Overview

🧾 Sample Products

• Hydrating Face Serum
• Vitamin C Cream
• Aloe Vera Gel
• Charcoal Face Wash
• SPF 50 Sunscreen
• Niacinamide Toner
• Anti-Aging Serum
• Face Sheet Masks
• Hair Repair Oil
• Lip Balm Pack
• Body Butter Cream
• Salicylic Acid Cleanser

🌏 Countries Covered

• India
• USA
• UK
• Canada
• Australia
• New Zealand

📊 Quick Stats

• Total Rows: 374
• Date Range: Jan 1, 2022 – Aug 31, 2022
• Revenue Range: Varies from ~$100 to ~$20,000 per order
• Box Quantity Range: 10 – 500 boxes

🎯 Ideal For

• Excel Practice (VLOOKUP, IF, AVERAGEIFS, INDEX-MATCH, etc.)
• Pivot tables & data cleaning tasks
• Power BI / Tableau dashboards
• Sales trend forecasting
• Exploratory Data Analysis (EDA)
• Retail analytics & product demand modeling

📌 Suggested Projects & Questions

• Which salesperson generated the highest revenue overall?
• What’s the average amount per order in each country?
• Which product was most frequently sold?
• What month had the highest total boxes shipped?
• Create a dashboard comparing revenue across countries.

✅ Clean Data Guarantee

• ✅ No missing/null values
• ✅ No duplicates
• ✅ Realistic values
• ✅ Globally relatable product categories
• ✅ Ready for ML, BI, and teaching use cases

𝗢𝗻𝗹𝗶𝗻𝗲 𝗦𝘂𝗽𝗲𝗿 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 📊

Hello Kaggle Community!👋 Check out my new Unguided Power BI End-to-End Practice Project. The objective is to conduct a complete analysis of historical online sales data to identify trends, patterns, and anomalies impacting revenue growth. Quantify the impact of key performance indicators (KPIs) on overall sales performance and provide data-driven recommendations. Deliver a detailed report outlining findings, insights, and strategic recommendations to stimulate revenue growth and enhance business performance. 📈📊

I decided to try something new this time by recording myself and giving an overview of the entire project as if I were presenting to senior stakeholders. I thought it would help me improve my storytelling skills, according to the current industry. There's definitely a lot of room for improvement and your invaluable feedback will be instrumental in identifying those areas.

DataAnalytics #DataAnalysis #DataAnalyst #OnlineStoreSales #PowerBI #DataStoryTelling #BusinessIntelligence #DataVisualization #DataDrivenInsights

This dataset represents simulated sales data for an electronics shop operating in the United States from 2024 (January to November). It is designed for individuals who want to practice data analysis, visualization, and machine learning techniques. The dataset reflects real-world sales scenarios, including various products, customer information, order statuses, and sales channels. It is ideal for learning and experimenting with data analytics, business insights, and visualization tools like Power BI, Tableau, or Python libraries.

Dataset Features ProductID: Unique identifier for each product. ProductName: Name of the electronic product (e.g., Phone, Laptop, Drone). ProductPrice: The price of the product is in USD. OrderedQuantity: Number of units ordered by the customer. OrderStatus: Status of the order (e.g., Delivered, In Process, On Hold, Canceled). CustomerName: Name of the customer who placed the order. State: State of the customer in the United States (e.g., California, Texas). City: City of the customer within the state. Latitude & Longitude: Geographic coordinates of the customer's location for mapping purposes. OrderChannel: Channel through which the order was placed (e.g., Website, Phone, Physical Store, Social Media). OrderDate: Date of the order (range: January 1, 2024, to November 30, 2024).

Potential Use Cases

Exploratory Data Analysis (EDA): Analyze sales trends across months, states, or product categories. Identify the most popular sales channels or products. Examine the distribution of order statuses.

Data Visualization: Create dashboards to visualize sales performance, customer demographics, and geographic distribution. Plot order locations on a map using latitude and longitude.

Machine Learning: Predict future sales trends using historical data. Classify order statuses based on product and order details. Cluster customers based on purchase behavior or location.

Business Insights: Analyze revenue contributions from different states or cities. Understand customer preferences across product categories.

Technical Details File Format: Excel (with a .xlsx extension) Number of Rows: 11000 Period: January 1, 2024, to November 30, 2024 Simulated Data: The data is entirely synthetic and does not represent real customers or transactions.

Why Use This Dataset? This dataset is tailored for individuals and students interested in: Building their data analysis and visualization skills. Learning how to work with real-world-like business datasets. Practicing machine learning with structured data. Acknowledgment This dataset was generated to mimic real-world sales data scenarios for educational and research purposes. Feel free to use it for learning and projects, and share your insights with the community!

Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

I'm excited to share my recent project where I dived deep into the world of data analysis to gain valuable insights into Tata Motors' sales data for the fiscal year 2021-2022. 📈

Project Highlights:

1. Data Processing and Cleaning: I meticulously cleaned and processed the dataset, ensuring accuracy and reliability in the analysis.

2. In-Depth Analysis: Through advanced analytical techniques, I uncovered patterns, trends, and key metrics within the data, helping to reveal critical business insights.

3. Data Visualization: I transformed the complex sales data into clear and insightful visual representations, making it easier for stakeholders to grasp the findings.

4. Interactive Dashboard: I designed an interactive dashboard that allows users to explore the data dynamically, facilitating a deeper understanding of the sales performance.

5. Findings: Tata Motors achieved 105% growth in sales, marking an impressive 126% profit increase compared to the year 2021.

This remarkable growth not only showcases the company's resilience but also the effectiveness of their strategies and operations. It's a testament to the hard work and dedication of the entire Tata Motors team.

This dataset can be used for creating an Inventory Dashboard. We can find the: - ABC Inventory Classification - XYZ Classification - Inventory Turnover Ratio - Calculation of Safety Stock - Reorder points - Stock Status Classification - Demand Forecasting on Power BI It is extremely useful for Warehouse/ In-plant Inventory Managers to effectively control the Inventory levels and also maintain the Service Levels.