About the dataset: A synthetic heart-disease dataset built for 2026: HbA1c, full lipid panel, wearable step counts, sleep and stress - the risk factors the classic 300-row heart datasets never recorded. Ideas to get you started: - Build a heart-disease classifier and check which factors carry it. - Do wearable owners actually show a lower risk profile? Test it properly - and watch for confounding by age. - Which matters more: the lipid panel or the lifestyle columns? Compare model families. - Is the sleep-risk relationship really U-shaped? Bin sleep_hours and plot it. - Build a simple interpretable risk score and compare it to a gradient-boosted model. - Explore HDL vs LDL - does the ratio beat either one alone?

I wanted to share a Power BI project I recently completed using the IBM HR Analytics Employee Attrition & Performance dataset. My goal was not just to create charts, but to practice taking a real business question → transforming the data → building an interactive dashboard → generating actionable insights. 🎯 Business Question Why are employees leaving, and what patterns can HR leadership identify to improve employee retention? 📊 Dashboard Includes - KPI cards for Total Employees, Attrition Rate, Average Monthly Income & Average Years at Company - Attrition by Department - Attrition by Gender - Age vs. Monthly Income - Attrition by Education Level - Attrition by Work-Life Balance - Job Satisfaction × Job Role × Attrition analysis - Interactive slicers for Marital Status, Age, Department & Gender - Key Insights section 🔎 Some Interesting Findings 1️⃣ Overall Attrition Rate: 16.12% This provides the baseline for understanding employee turnover. 2️⃣ Younger employees show higher turnover The analysis shows a noticeable concentration of attrition among employees around the 20–30 age range, which could indicate opportunities around career development, compensation and employee engagement. 3️⃣ Work-life balance matters The analysis shows interesting differences in attrition across work-life balance categories, highlighting an area HR could investigate further. 4️⃣ R&D has the highest number of attrition cases However, this is an important lesson: attrition count ≠ attrition rate. Because R&D has a larger workforce, the absolute number of departures needs to be considered alongside the department's total employee population. 5️⃣ Education, job role and satisfaction reveal additional patterns Combining these dimensions can help HR identify employee segments that may benefit from targeted retention strategies. 🧠 What I'm Practicing ✅ Power BI ✅ Power Query ✅ DAX ✅ Data Modeling ✅ KPI Development ✅ Data Visualization ✅ Interactive Dashboard Design ✅ Conditional Formatting

Which DAX function counts table rows ?

Please find attached my completed project submission: the Power BI dashboard and the accompanying 1-page Executive Report for the Netflix dataset analysis. I'd like to apologize for the delay in submitting this assignment. A family emergency required me to travel unexpectedly, due to which I was unable to attend the last session.

Now presenting Executive Summary below: Kindly go through my dashboard uploaded earlier today and suggest any changes in the dashboard to improve it Thank you). Executive Summary: Netflix Content Strategy Dashboard The Goal We built this dashboard to answer one main question: How can Netflix spend its content budget smarter to keep growing globally? It helps us see exactly where our content is, what our subscribers want, and where we should invest next. What the Data is Telling Us - We rely too much on a few countries: The US, India, UK, Japan, and South Korea make up about 60% of our entire library. We are missing big growth opportunities in emerging markets like Africa, Latin America, and Southeast Asia. - TV Shows are the new growth engine: We used to add mostly movies. But since 2021, TV show additions have caught up to (and sometimes passed) movies. People love binge-watching, and serialized content keeps them subscribed longer. - Niche genres are booming: While Dramas and Comedies are still our foundation, categories like Anime, K-Dramas, and True Crime have seen massive, triple-digit growth since 2019. - We need more family-friendly content: Only about 10% of our library is rated for kids or families (TV-G, PG). This is a missed opportunity, especially as our ad-supported subscription tier continues to grow. - Timing is on our side: The data shows we naturally add the most content in the fourth quarter (Oct–Dec), which perfectly matches when people watch the most. What We Should Do Next 1. Expand beyond our usual markets: Shift a portion of our content budget to emerging regions to capture new, untapped subscribers. 2. Bet big on TV shows and niche hits: Focus our acquisitions on serialized content and fast-growing genres like Anime, which give us a great return on investment. 3. Build up our family-friendly library: Actively license and produce more G and PG-rated content to support our ad-supported tier and attract family households.