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š Turning Data into Decisions with AI! š¤
Excited to share my latest AI-Powered Retail Sales Analytics & Forecasting Dashboard, developed using Python, Streamlit, Pandas, Scikit-Learn, and Machine Learning.
This project transforms raw sales data into actionable business insights through:
ā
Sales & Revenue Analytics
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Interactive KPI Dashboard
ā
Product Category Performance Analysis
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Holiday Impact Analysis
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Marketing Spend vs Revenue Insights
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30-Day Sales Forecasting using Machine Learning
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AI-Driven Business Recommendations
By leveraging data analytics and predictive modeling, businesses can make smarter decisions, identify growth opportunities, and forecast future performance with confidence.
š¹ Tools & Technologies:
Python | Streamlit | Pandas | NumPy | Scikit-Learn | Matplotlib | Machine Learning | Data Analytics
hashtag#DataScience hashtag#MachineLearning hashtag#Python hashtag#Streamlit hashtag#BusinessIntelligence hashtag#DataAnalytics hashtag#SalesForecasting hashtag#PredictiveAnalytics hashtag#ArtificialIntelligence
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ā¢ 2d
Wrote and published a comprehensive guide on Medium covering the evolution and real-world impact of Machine Learning ā from its 1940s origins to today's deep learning revolution. Article breaks down supervised, unsupervised, and reinforcement learning, explores applications across healthcare, finance, retail, and transportation, and addresses ethical challenges like bias and data privacy. Received 50 claps on Medium.
I've been watching Formula 1 since 2014. Through the Mercedes era, the 2021 title fight that broke my heart and rebuilt it, and the Sunday-afternoon alarms for flyaway races, F1 has been a constant for me for over a decade.
So I finally built the thing I always wanted to exist: a real-time F1 race dashboard that doesn't just show what's happening but predicts what's about to.
šļø What it does:
A live track map with every car moving in real time, built from real GPS data.
Live timing, tires, gaps, and pit stops.
And the part I'm most proud of: AI predictions for tire life, pit windows, safety-car risk, and where each car will actually finish
The honest results (because numbers should mean something):
Its finishing-position guess is on average within ~2 places of where a car really ends up. So if it says "P5," reality is usually P3āP7.
It gets about 3 in 4 of its podium calls right while the race is still running.
For safety cars: when it says there's roughly a 1-in-8 chance of one soon, that's about how often one actually appears, i.e., the percentages are honest, not vibes.
What I learned the hard way: in a sport this strategic, the live running order is already a brutally good predictor. Beating it isn't about more data; it's about modeling the chaos (safety cars, rain, and DNFs). That reframing was the whole project.
Built with Python, FastAPI, React, and a Monte Carlo simulation engine. Every predicted number is labelled so it's never confused with real telemetry.
From fan to builder, easily the most fun I've had with a side project. š
Repo + write-up in the comments. Would love feedback from other F1 + data folks.
AI WhatsApp Bot & RAG Integration
I developed an intelligent WhatsApp chatbot for a client, integrating their Wix Website Development services with automated customer support.
My client struggled with handling diverse customer inquiries across text, voice, and document formats efficiently. Processing varied media types manually created severe bottlenecks in their daily operations and delayed critical responses.
I designed an n8n workflow utilising advanced routing to process audio, images, and PDFs automatically. MongoDB vector search and OpenAI integrations power the central knowledge agent, ensuring accurate data retrieval and seamless WhatsApp communication."
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