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 Business names were inconsistent, categories overlapped, information was missing or outdated, and the same companies could appear differently across multiple sources.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 #PortfolioProject
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 eyJrIjoiMTVjMTdkMWEtZTQwOS00NDM0LTkxYTEtMjJmODY2ZDhmMTY0IiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9
Golden Wok Delivery Network Intelligence
From network pressure to service reliability and corridor-level action Golden Wok’s delivery network presents a connected operational challenge: understanding where delivery pressure originates, when service reliability begins to deteriorate, and which corridors require attention. The report follows a three-stage analytical journey: Network Performance → Service Reliability → Network Optimization 01 | Network Performance The first page explores where delivery pressure originates across the network. Delivery radius, traffic friction, weather conditions and zone tiers provide the context for understanding where operational pressure is concentrated and how those conditions affect delivery performance. Outcome: Identify where network conditions create the greatest delivery pressure. 02 | Service Reliability The second page focuses on what happens as delivery performance deteriorates. One of the clearest findings was the relationship between delivery duration and SLA risk. Breach rates increased sharply across longer delivery-time bands, while customer ratings remained comparatively stable. This made service reliability a stronger operational signal than the customer-rating response in the supplied data. Outcome: Understand when delivery performance begins to translate into significant SLA risk. 03 | Network Optimization The final page moves from diagnosis to action. I combined Profit Efficiency and SLA Risk to classify delivery corridors into Priority Intervention, Service Intervention, Economic Optimization, and Protect & Maintain. ABC classification then adds economic importance, helping distinguish valuable corridors that should be protected or repaired from weaker, higher-risk corridors that may warrant deeper review. Interactive tooltips provide additional corridor and intervention context without overcrowding the main report. Outcome: Prioritize corridor-level action based on economic value and service reliability. Validation Before Visualization The brief served as a checklist for testing key assumptions against the supplied data. Where discrepancies appeared, values were adjusted only when defensible or excluded when they could not be reliably interpreted. This kept the analysis grounded in the brief while allowing validated evidence to determine the final story. Final Takeaway Golden Wok became a simple decision journey: Diagnose → Understand → Prioritize The project reinforced the importance of validating assumptions before visualization and keeping each report page focused on a clear business question. A rich operational dataset does not need a crowded report. Every visual should help move the analysis closer to a decision. Data Source: September 2026 DataDNA – Golden Wok Food Delivery Analytics ChallengeMakeover Monday week 36 - Americans favourite season
At the start of the week I saw that at dm-p had developed and released a chart xkcd custom visual for Power BI so I thought I'd use it for this weeks Makeover Monday data on Americans favourite season. It's a very small dataset and not all of it ideal for the visuals but was a good opportunity to try it out, I even bought myself to create some pie and Doughnut charts. eyJrIjoiZjc4MTY4YjItMzNmMi00YzBiLTgyMGYtNTM3MDgxMTBmMGY1IiwidCI6IjgzNzBjZjE0LTE2ZjMtNGMxNi1iODNjLTcyNDA3MTY1NDM1NiIsImMiOjh9European Summer Getaways 2026
This report explores European Summer Getaways 2026, helping commercial and portfolio teams identify the strongest destination opportunities, understand the drivers of destination attractiveness, and evaluate how holiday packages can be positioned for different traveller needs. Through a three-page analytical journey, the report moves from opportunity prioritisation to destination analysis and concludes with traveller-focused package positioning recommendations. Portfolio: Where Should We Focus? The first page identifies the strongest portfolio opportunities by balancing destination appeal with package availability and portfolio depth. The analysis moves from destination types to countries and cities, helping identify where commercial attention and promotion may deliver the greatest opportunity. Outcome: Prioritise the destinations and markets that deserve greater commercial focus. Drivers: What Drives Destination Attractiveness? The second page explores why some destinations perform better than others. Weather conditions, temperature and tourism pressure provide context around destination attractiveness, while the performance matrix highlights the trade-off between strong appeal and visitor pressure. Outcome: Understand the factors behind destination performance instead of relying on ranking alone. Travellers: Which Holiday Is Right for Which Traveller? The final page shifts from destination performance to package value and positioning. The analysis compares traveller spending, accommodation pricing, travel convenience and city-level value to understand how attractive destinations can translate into commercially relevant holiday packages. Outcome: Support package positioning around value, convenience and different traveller needs. Final Takeaway One of the biggest challenges with such a rich dataset was deciding what not to show. Rather than trying to analyse every available field, I focused each page on a clear business question and built the report around a simple journey: Opportunity → Understanding → Action A rich dataset does not need a crowded report. Every visual should earn its place by helping answer the business question. Data Source: FP20 Analytics ZoomCharts Challenge 40 | Summer 2026 Holiday Dataset
Live Weather Dashboard
Live IoT Weather Dashboard This is a real-time weather dashboard powered by a custom-built DIY IoT weather station. While many weather dashboards available online use static datasets or merely change color schemes on the same template, this project is fundamentally different. It is a live system that bridges the gap between physical hardware and digital analytics. Technical Overview Data Collection: Unlike dashboards that rely on CSV uploads, this station utilizes an ESP8266 microcontroller paired with DHT11 and BMP280 sensors. Live readings for Temperature, Humidity, and Pressure are logged directly to Google Sheets in real-time. Region-Specific Calibration: Specifically calibrated for the unique tropical climate of the Trichy, Tamil Nadu region, ensuring localized accuracy that global APIs often miss. Advanced Analytics: Using only four raw data points (Timestamp, Temp, Humidity, and Pressure), I have developed a custom suite of DAX measures. These calculate complex weather trends, "feels like" temperatures (Heat Index), and atmospheric dew points. Dynamic Visuals: The interface isn't just a static skin; it features weather-dependent iconography and a custom refresh schema that ensures the data you see is as fresh as the air outside. Why this stands out Most dashboards you see are "re-skins" of existing templates. This project demonstrates a full-stack data engineering pipeline: Sensor → Microcontroller → Cloud Storage → Power BI Logic → End-User Visualization. eyJrIjoiYTVjNzg4MzMtZDJlMS00NmY5LWI1ZDItNzRhMjRmNDdkZWIwIiwidCI6IjBjM2QwNTc2LTFkOWYtNGM4Ny05OTNjLTg2YjQ0MGE1YjA3OCJ9The 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 #MicrosoftPowerBIPower BI Financial Dashboard – Take Control of Your Business Performance
Are You Struggling to Make Sense of Your Financial Data? Many businesses drown in spreadsheets and guesswork, missing critical opportunities to boost revenue, cut costs, and maximize profits. Without clear financial visibility, you risk: Blind spots in revenue performance – Are you hitting targets or falling short? Uncontrolled expenses – Which costs are draining your profits? Unclear profitability by product/service – What’s really driving your bottom line? Reactive decision-making – No early warnings for cash flow dips or seasonal slumps. Stop guessing. Start optimizing. This ready-to-use Power BI dashboard gives you real-time, visual, and interactive financial insights—so you can track performance, spot trends, and act fast. Key Problems It Solves: Revenue vs. Targets Tracking – Instantly see if you’re on track or need to adjust strategy. Profitability Breakdown by Product/Service – Identify high-margin winners and underperformers. Expense Control & Cost Optimization – Pinpoint wasteful spending before it hurts profits. Seasonal & Trend Analysis – Predict dips and plan ahead to stabilize cash flow. Why This Dashboard? No Complex Setup – Plug in your data, and it works instantly. Interactive & Visual – No more messy spreadsheets—just clear, actionable reports. Customizable for Any Business – Works for eCommerce, retail, SaaS, and more. Real-Time Decision Making – Spot risks and opportunities before competitors do. Who Needs This? Finance Teams – Simplify reporting and present insights in seconds. CEOs & Business Owners – Get a 360° view of financial health. Startups & SMBs – Make data-driven decisions without a finance expert. Consultants & Analysts – Deliver stunning, professional reports to clients. eyJrIjoiYTBkODhlMTUtMGY1ZS00ZGUyLThkMjMtYTE2YmY4NjEyOWE5IiwidCI6ImE5NDUyNTg2LTJmNGMtNGNiMS04ZDJlLTI2ODkyODllZTcyNiIsImMiOjl9Pizza 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
