In this repository you can find my Power BI projects:

• Adventure Works 2020 (from Microsoft Learning)

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!

Dataset

This dataset was created by AYUSH CHOUDHARY

Contents

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.

An interactive dashboard to visualize sales performance, product categories, regional performance, and key business KPIs.

📌 Description This project analyzes online sales data using Power BI, focusing on performance metrics such as Total Sales Amount, Profit, Quantity Sold, and Payment Modes. The dashboard provides detailed visualizations to identify top-performing categories, sub-categories, and locations. It aims to deliver actionable insights for business strategy, marketing decisions, and operational improvements.

The dataset is split across two CSV files:

• Orders.csv – contains customer and order metadata (date, name, location)

• Details.csv – contains order-level details (profit, quantity, payment mode, category)

🧩 Key Features - KPI Cards: Total Amount, Total Profit, Total Quantity, Profit Margin

• Pie Charts: Sales by Category, Sales by Payment Mode

• Donut Chart: Sales by State

• Bar Chart: Sales by Sub-Category

• Map: Quantity sold across Indian States

• Interactive Slicers and Filters

⚒️ Tools & Techniques Power BI Desktop

DAX Calculations

Custom Visual Design for Clean UI/UX

This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.

I'm excited to share my latest project—an interactive Power BI dashboard that provides a comprehensive analysis of bike sales data from 2019 to 2024!

Key Highlights of the Dashboard:

📈 Sales Trend Analysis: Understand how bike sales have fluctuated over the years, with peaks in specific months that give us clues about seasonal demand. 🏢 Sales by Store Location: See how different cities like New York and Phoenix lead in terms of total sales revenue. 🚴‍♀️ Customer Demographics: Almost equal contributions from male and female customers—showing the broad appeal of our products. 💳 Payment Method Preferences: Breakdown of the most used payment methods, with insights that can help improve our customer experience. 📊 Revenue by Bike Model: A detailed look at which bike models drive the most revenue, helping guide product focus and inventory management. This dashboard was built to provide actionable insights into the sales performance and customer behavior of a large dataset of 100K records. It highlights the power of data visualization in turning numbers into strategic insights!

Why Power BI? Power BI's flexibility and interactive capabilities made it the ideal tool for visualizing the data, allowing users to drill down into specific details using slicers for bike models and time periods. 💡

Would love to hear your thoughts or any feedback on this project! If you’re interested in how this dashboard was built or want to discuss data visualization, feel free to reach out. Let’s transform data into stories that drive success! 🌟

Dataset Overview: Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps. Useful for demand forecasting, price optimization, trend analysis, and business insights. Helps in understanding customer behavior and seasonal variations in online grocery shopping. Potential Use Cases: - Time Series Analysis: Analyze sales trends over different periods. - Demand Forecasting: Predict future product demand based on historical data. - Price Optimization: Identify the impact of pricing on sales and revenue. - Customer Behavior Analysis: Understand buying patterns and preferences. - Market Trends: Explore how different factors affect grocery sales performance. This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Fa633fb36dc370263696b5d2ec940c74f%2FScreenshot%202025-06-16%20082824.png?generation=1750086765806732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2F8843129c88c2f57d66006a3ac9d37dc7%2FScreenshot%202025-06-16%20084001.png?generation=1750086777975125&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Ffa4f29a8f4cc763a1cc66c7913c077e8%2FScreenshot%202025-06-16%20084007.png?generation=1750086787100561&alt=media" alt="">

This dataset is part of my portfolio project and is shared here to encourage exploration and further analysis. Feel free to use it as-is, build upon it, or integrate it into your own projects. Whether you're practicing data analysis, testing models, or just need a clean dataset to work with—this resource is available for you.

This is the filtered dataset of LA Census Tracts from the 500 Cities project 2017 release. This dataset includes 2015, 2014 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2015, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015, 2010-2014 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures from the 2014 BRFSS that are the same in the 2017 release as the previous 2016 release. More information about the methodology can be found at www.cdc.gov/500cities.

China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity: R data was reported at 7.000 Unit in 2002. This records an increase from the previous number of 6.000 Unit for 2001. China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity: R data is updated yearly, averaging 6.000 Unit from Dec 2000 (Median) to 2002, with 3 observations. The data reached an all-time high of 7.000 Unit in 2002 and a record low of 4.000 Unit in 2000. China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity: R data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under Global Database’s China – Table CN.KE: China Development Bank (CDB): Loan.

China CDB: Medium Size Project Loans: BI: Energy: Electricity: Urban data was reported at 4.522 RMB bn in 2002. This records a decrease from the previous number of 13.226 RMB bn for 2001. China CDB: Medium Size Project Loans: BI: Energy: Electricity: Urban data is updated yearly, averaging 11.244 RMB bn from Dec 2000 (Median) to 2002, with 3 observations. The data reached an all-time high of 13.226 RMB bn in 2001 and a record low of 4.522 RMB bn in 2002. China CDB: Medium Size Project Loans: BI: Energy: Electricity: Urban data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under Global Database’s China – Table CN.KE: China Development Bank (CDB): Loan.

Power BI Dashboard : https://www.mavenanalytics.io/project/3776

The IPL (Indian Premier League) is one of the most popular and widely followed cricket leagues in the world. It features top cricket players from around the world playing for various franchise teams in India. The league is known for its high-scoring matches, intense rivalries, and innovative marketing strategies.

If you are a data enthusiast or a cricket fan, you will be excited to know that there is a dataset available on Kaggle that contains comprehensive information about the IPL matches played over the years. This dataset is a valuable resource for anyone interested in analyzing the performance of players and teams in the league.

The IPL dataset on Kaggle contains information on over 800 IPL matches played from 2008 to 2020. It includes details on the date, time, venue, teams, players, and various statistics such as runs scored, wickets taken, and more. The dataset also contains information on the individual performances of players and teams, as well as the overall performance of the league over the years.

The IPL dataset is a goldmine for data analysts and cricket enthusiasts alike. It provides a wealth of information that can be used to uncover insights about the league and its players. For example, you can use the dataset to analyze the performance of a particular player or team over the years, or to identify trends in the league such as changes in team strategies or the emergence of new players.

If you are new to data analysis, the IPL dataset is a great place to start. You can use it to learn how to use tools such as Excel or Power BI to create visualizations and gain insights from data. With the right skills and tools, you can use the IPL dataset to create interactive dashboards and reports that provide valuable insights into the world of cricket.

Overall, the IPL dataset on Kaggle is an excellent resource for anyone interested in cricket or data analysis. It contains a wealth of information that can be used to analyze and gain insights into the performance of players and teams in one of the most exciting cricket leagues in the world.

This dataset contains points table and player Information. To view more data such as Match stats, Ball_by_ball data & Player innings data, Please visit the below links:

Match stats, Ball_by_ball data: https://www.kaggle.com/datasets/biswajitbrahmma/ipl-complete-dataset-2008-2022

Player innings data: https://www.kaggle.com/datasets/paritosh712/cricket-every-single-ipl-inning-20082022

Thanks to Biswajit Brahmma & Paritosh Anand for their dataset.

A data dashboard in the form of a document link to Microsoft Power BI Dashboard of the same name, prepared and maintained by the Department of Economic Prosperity and Housing. Data is updated quarterly.NOTE: This product and the information shown is provided "AS IS" and exists for informational purposes only. The City of Vancouver (COV) makes no warranties regarding the accuracy of such data. This product and information is not prepared, nor is suitable, for legal, engineering, or surveying purposes. Any sale, reproduction or distribution of this information, or products derived therefrom, in any format is expressly prohibited. Data are provided by multiple sources and subject to change without notice.

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.

Soluções completas em análise de dados: estruturação, governança, visualização e preditiva com Power BI, SQL e Python.

Data Science Dashboard

** Exciting Data Science Dashboard Project Overview **

I'm thrilled to share my latest Power BI dashboard project focusing on the salaries and employment trends of Data Science professionals from 2020 to 2022. Dive into the details of this insightful dashboard that sheds light on various aspects of employee demographics, compensation, and work trends.

** Detailed Visualizations: **

Total Employee Count: Track the growth of the Data Science team over the three-year period. Average Salary Analysis: Explore the average salaries for each year (2020, 2021, and 2022) to identify trends and fluctuations. Company Size Distribution: Visualize the distribution of employees across small, medium, and largesized companies where Data Science professionals worked. Salary Distribution by Job Titles: Gain insights into salary distribution across different job titles through a clustered bar chart. Experience Level and Employment Type: Analyze the distribution of employees based on experience levels and employment types through an informative table. Remote Work Ratio: Understand the proportion of remote work over time through a stacked area chart, correlated with salary levels in USD. Interactive Slicers: Use drop-down slicers for job titles and work years to customize your data exploration experience. **Key Takeaways and Insights: **

Identify hiring trends and patterns in the Data Science field over the three-year period. Understand salary distributions based on job titles and experience levels. Gain insights into the prevalence of remote work and its correlation with salary levels. Explore the impact of company size on employment opportunities and compensation. ** Unlocking Insights for Decision-Making: **

This Power BI dashboard provides valuable insights for HR professionals, hiring managers, and Data Science enthusiasts alike. Use the interactive features to drill down into specific segments and extract actionable insights for strategic decision-making. Leverage the visualizations to inform recruitment strategies, salary negotiations, and workforce planning initiatives. **Ready to Explore the Future of Data Science Employment? **

Dive into this comprehensive dashboard to uncover trends, patterns, and insights that drive the Data Science industry forward. Let's connect to discuss how these insights can inform your business strategies and propel your organization towards success.

China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity data was reported at 157.000 Unit in 2002. This records an increase from the previous number of 120.000 Unit for 2001. China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity data is updated yearly, averaging 151.000 Unit from Dec 1997 (Median) to 2002, with 5 observations. The data reached an all-time high of 165.000 Unit in 1998 and a record low of 104.000 Unit in 1997. China CDB: Number of Medium Size Project Loans: BI: Energy: Electricity data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under Global Database’s China – Table CN.KE: China Development Bank (CDB): Loan.

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, I had to ask questions that could help extract and explore information that would help decision-makers improve and evaluate performance. But before that, I did some operations in the data to help me to analyze it accurately: 1- Understand the data. 2- Clean the data “By power query”. 3- insert some calculation and columns like “COGS” cost of goods sold by power query. 4- Modeling the data and adding some measures and other columns to help me in analysis. Then I asked these questions: To Enhance Customer Loyalty What is the most used ship mode by our customer? Who are our top 5 customers in terms of sales and order frequency? To monitor our strength and weak points Which segment of clients generates the most sales? Which city has the most sales value? Which state generates the most sales value? Performance measurement What are the top performing product categories in terms of sales and profit? What is the most profitable product that we sell? What is the lowest profitable product that we sell? Customer Experience On Average how long does it take the orders to reach our clients? Based on each Shipping Mode

Then started extracting her summaries and answers from the pivot tables and designing the data graphics in a dashboard for easy communication and reading of the information as well. And after completing these operations, I made some calculations related to the KPI to calculate the extent to which sales officials achieved and the extent to which they achieved the target.

Global Revenue & Customer Insights with Power BI

Just wrapped up an interactive Power BI Dashboard analyzing 2011 retail data! This project highlights key trends in global revenue, demand, and customer behavior.

🔎 Key Insights: ✅ Monthly Revenue Trends 📉 ✅ Country-Wise Demand 🌍 ✅ Customer Revenue Segmentation 🛒 ✅ Seasonal Analysis with Filters 🌸❄

💡 Skills Applied: 🔹 Power BI for Data Visualization 🔹 DAX for Advanced Calculations 🔹 Data Transformation with Power Query 🔹 Data Storytelling for Business Insights

🚀 Business Impact: ✔ Identify growth opportunities ✔ Understand customer preferences ✔ Optimize sales strategies