Fantasy Premiere League Analytics Hub v3
Multi Seasonal Report to help Fantasy managers track player performance discover hidden gems and undervalued assets Main changes from previous version: Changed the pipeline from just python > powerbi to python >sql databae > powerbi' This was done to handle multi season analysi as the previous pipeline wasnt fit to do so Utilised bronze silver gold staging in sql More contextualised kpis Customiseable charts (parameters) Utilised HTML visuals for KPIs, Visuals (player explorrer page) and tooltips Star Schema Data Model eyJrIjoiMTVjMTdkMWEtZTQwOS00NDM0LTkxYTEtMjJmODY2ZDhmMTY0IiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9F&B Sales Performance Dashboard
F&B Sales Intelligence Dashboard An interactive dashboard developed as part of my data analytics portfolio to explore sales performance across three F&B outlets in Indonesia. It uses the Anonymous Transactional Dataset from Mendeley Data, containing historical POS transactions and product metadata for January–September 2025. The dashboard covers sales KPIs, outlet performance, weekly trends, transaction intensity by day and operating hour, and product rankings. It also incorporates RFM-based transaction segmentation using K-Means, replicating a published methodology with the full dataset. Four transaction segments are summarized by volume, average spending, and total transaction value. The analysis covers 53,820 transactions with a total transaction value of approximately IDR 1.32 billion. Key findings include SHOP001 contributing nearly 49% of total transaction value and high-value transactions contributing a substantial share despite their smaller volume. Tools: Power BI and Google Colab (Python) Skills: Data modeling, DAX, KPI development, interactive visualization, RFM analysis, K-Means clustering, and insight communication. Dataset: https://data.mendeley.com/datasets/kcgf45y24m/2 Feedback on the dashboard’s design, analysis, and usability is welcome.U.S. Airsoft Market Analysis
📊 From almost no structured data to a complete Power BI market analysis I was approached by people who are considering launching an airsoft business and asked to analyze the U.S. airsoft market, with a particular focus on Washington State. And the biggest challenge became obvious almost immediately: 🔎 There was very little structured market data available. For the first time, I had to use Python not only for analysis, but also for data collection. I gathered information from Google, Facebook, company websites, and other online sources, using APIs where available. 🧩 Collecting the data was only the beginning. This turned out to be one of the most challenging parts of the project. I am not a web-scraping specialist, and the raw data was far from analysis-ready. Business names were inconsistent, categories overlapped, information was missing or outdated, and the same companies could appear differently across multiple sources. Honestly, this part exhausted me. 😅 I started building the Power BI report several times, only to discover another issue in the underlying data and go back to cleaning and validating it again. Eventually, I was able to build a dataset that was consistent enough for analysis. ⚙️ The overall pipeline looked like this: Data Collection → Data Cleaning → Business Verification → Data Enrichment → Pricing & Services Analysis → Customer Review Analysis → Power BI Data Model → Interactive Dashboard → Business Scenario 🇺🇸 U.S. Market The final dataset included 851 U.S. businesses, from which I identified 194 confirmed airsoft operators. I then focused on Washington State, where I verified 10 active game operators and analyzed their locations, pricing, services, ratings, and customer feedback. 📍 Washington Market The analysis showed: 10 verified game operators • $22.50 median published open-play price • 4.8 average Google rating • 1,096 Google reviews across the verified operators 💬 Customer Voice I also analyzed customer review text to understand not just how businesses were rated, but what customers were actually talking about. The analysis highlighted recurring issues around operations & communication, price & value, rental equipment, and consistency in safety & rule enforcement. 📈 From analysis to a business scenario Finally, I brought everything together in Power BI and built an interactive business scenario where assumptions such as daily attendance, operating days, admission price, rental usage, and private bookings can be changed to explore their impact on estimated monthly revenue. 💡 What this project became For me, this project became much more than a Power BI dashboard. It was an end-to-end data project: finding the data → building the dataset → validating it → analyzing it → turning it into something that could support a real business discussion. And, perhaps most importantly, it taught me how much work can happen before the first chart ever appears on a dashboard. 😅 #PowerBI #DataAnalytics #Python #DataVisualization #BusinessIntelligence #MarketResearch #DataAnalysis #PortfolioProjectSpotify analytics
Spotify Music Analytics This interactive Power BI report, made with use of Claude design and inspired by works of PauloGrijo Gusbavia nayarahellen , and MariusHelle explores features of a large catalogue of Spotify tracks and what separates the songs people love from the ones they skip. Instead of a single dashboard, it's built as a guided experience across several connected pages, combining exploratory analysis and storytelling. What's inside: Overview — a landing page that surfaces the headline findings up front, with quick-read summary cards so you can see the main takeaways before diving deeper. Evolution of Sound — a chapter-based walkthrough of how the sound of popular music has shifted over the decades, with a narrative rail and supporting charts for each theme. Popularity Question — the analytical core: a "fingerprint" radar comparing the average popular track against the average unpopular one, a simple model view showing which features carry the most predictive weight, per-feature scatter plots, and breakdowns by genre and era. Listening Room — a fully filterable track explorer. Filter by genre, release year and audio-feature ranges. Make your own Hit! — an interactive tool where you set several feature levels to generate your own track. Note: for public demo purposes the tracks have been pre-generated using the Suno API. Key takeaways: Sound alone doesn't make a hit: a track's audio features explain only ~3% of its popularity — the rest is fame, marketing, playlists and timing. Music has drifted moodier and more produced. Over 60 years songs got less acoustic, more danceable, louder, shorter, and measurably less cheerful. Hits and flops sound almost the same. The audio "fingerprints" of popular and unpopular tracks nearly overlap. Data sources: - https://www.kaggle.com/datasets/jashanjeetsinghhh/songs-dataset - https://www.kaggle.com/datasets/salvatorerastelli/spotify-and-youtube Contact: Robert van de Vorst - https://www.linkedin.com/in/robertvandevorst/ eyJrIjoiZTI3NzNhNTgtMjBkMS00NTU2LThjN2EtZDM0NDAwYWNhZTQ2IiwidCI6Ijg5NzU5Zjk5LWQxNzEtNDZjMC04YzZjLWY3ZWNhYTJlOGI2ZSIsImMiOjh9The Finance Dashboard
The Finance Dashboard | Power BI Project 📊 Excited to share my latest Finance Dashboard built using Microsoft Power BI! This project focuses on Financial Data Analytics, Business Intelligence, and Interactive Data Visualization. Key Features: - Total Amount, Tax, Fees & Final Amount KPIs - Country-wise Financial Analysis - Company-wise Amount Analysis - Transaction Status Monitoring - Tax Analysis by Country & Company - Year & Account Type Filters - Interactive Dashboard Design This dashboard helps transform raw financial data into meaningful insights for better business reporting and data-driven decision-making. Tools: Microsoft Power BI | Data Analytics | Business Intelligence | Financial Reporting I am passionate about Data Analytics, Power BI, and turning data into actionable business insights. Your feedback is welcome! #PowerBI #PowerBIDashboard #DataAnalytics #BusinessIntelligence #FinancialAnalytics #DataVisualization #FinanceDashboard #PowerBIDeveloper #DataAnalyst #MicrosoftPowerBIYour Dashboard Can’t Answer This… But This AI Can (Power BI + PowerMind)
Dashboards are powerful… but they have a fundamental limitation. They can only answer the questions they were designed for. Every new business question often means: • Creating new DAX measures • Updating the data model • Rebuilding visuals And over time, reports become heavier, more complex, and harder to maintain. --- 💡 This demo explores a different approach: What if your report could answer questions dynamically, without constantly adding new logic? --- 🚀 In this report, an AI layer (PowerMind) is integrated directly into Power BI. It allows business users to: • Ask questions in natural language • Get instant insights and explanations • Explore scenarios without modifying the report • Access answers that were never predefined --- 📉 Impact on report design: Instead of continuously adding: → Dozens of DAX measures → Complex logic for every scenario You can: → Keep a leaner data model → Reduce the number of measures → Let the AI handle exploratory and unexpected questions --- 🧠 From a business perspective: Users often rely on analysts to: • Interpret results • Answer follow-up questions • Provide deeper insights This creates delays and bottlenecks. With this approach: → The report becomes an assistant → Users get answers instantly → Decision-making becomes faster and more autonomous --- ✨ User experience: Beyond functionality, this also makes reports more engaging and intuitive. In this demo, the AI is integrated in three ways: 1. Embedded directly in the main page. 2. Full-screen experience (see second page) 3. Popup assistant (click the icon in the bottom-left corner) This allows different interaction styles depending on the user’s needs. 🔗 Try the interactive report: https://app.powerbi.com/view?r=eyJrIjoiZjAxNmQxZGEtNTliNi00MGRmLWIzMjktMGNkNWVmYzdmYTZhIiwidCI6IjkyMDE4ZTVmLWI1MzctNDQ1MS1iYWVhLThhNTQ5MTRiYmVhMSIsImMiOjF9 --- 🧠 Powered by PowerMind (Querixa) If you're working with Power BI and exploring how to make your reports more intelligent, lighter, and more interactive: 👉 https://querixa.com Happy to hear your feedback and thoughts. #PowerBI #MicrosoftFabric #BusinessIntelligence #DataAnalytics #AI #GenerativeAI #ConversationalAI #SelfServiceBI #DecisionMaking #DataDriven #Analytics #CustomVisual #PowerBICommunity #DigitalTransformation #AugmentedAnalytics #ModernBI #Copilot eyJrIjoiZjAxNmQxZGEtNTliNi00MGRmLWIzMjktMGNkNWVmYzdmYTZhIiwidCI6IjkyMDE4ZTVmLWI1MzctNDQ1MS1iYWVhLThhNTQ5MTRiYmVhMSIsImMiOjF9Pizza Sales Report
Five business insights from data Most Pizza Selling In 16-20 Hours. Most Selling Large Pizza Name of Thai Chicken. At 8242, Friday Had the Highest Sum Of Quantity And Was 36.57% Higher Than Sunday, Which Had The Lowest Sum Of Quantity At 6035. Quarter 2 Has Increase Pizza Quantity. Quarter 4 Has Decrease Pizza Quantity eyJrIjoiOTA1OGYwZWYtMjVhNS00NTY0LTgzNmYtNGJhZWJkNGE1ZTViIiwidCI6ImJmYjZiODdiLTViODUtNDkxMS1hYWMxLTJkODIyMThiOGQ4ZCIsImMiOjl9
