The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
: Built an interactive Excel dashboard for retail sales analysis — 4 KPI cards, region/category breakdowns, monthly trends, and a salesperson leaderboard, all powered by live formulas (SUMIFS, SUMPRODUCT, COUNTIF) with no hardcoded numbers.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Trending
Claude
Claude has entered the design space. How are you using it?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
Brand Design
The best brand designers are on Contra. Scroll to see what's trending in brand design. What are you building?
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?
Related posts
Hype, hype and hype... It's the first page of the notebook that I write in tells me.
I was building automated workflows around Signalwatch, and it got me thinking about all the hype.
Hype around just one piece of the puzzle, LLMs. They just predict the next word while generating... No magic.The better they are trained, the better the output. Super helpful in a lot of contexts...
Tool calling. Helpful, but if LangChain and RAG + LLMs, for example, are not included where appropriate, ta ta, bye bye.
Automation. Someone bringing me a glass of water, for example, isn't something to be automated or waste resources or tokens on...
This brings me back to structural understanding, especially python + machine learning basics where you train a model based on a small piece of dataset you download from hugging face, for example... a lot becomes clear here
Built an end-to-end used car price prediction system using regression on a dataset of 50,000 used car listings (manufacturer, model, engine size, fuel type, year, mileage, and price).
I compared three models, linear regression, random forest, and XGBoost, and selected XGBoost as the production model based on its strong performance (MAE ~$195, R² 0.9996). Feature engineering included car age, mileage per year, and a premium-brand flag to boost predictive accuracy. SHAP was used to interpret the model, revealing that car age, engine size, and mileage are the strongest drivers of price.
The final model is deployed as an interactive Streamlit dashboard with three sections: a Price Estimator for instant valuations, a Market Explorer for browsing trends across brands/models, and a Model Insight page showing feature importance and evaluation metrics.
Built end-to-end using Python, pandas/numpy, scikit-learn, XGBoost, SHAP, Streamlit, and Plotly.
Interactive Power BI dashboard analyzing sales performance, customer behavior, product categories, regional trends, and payment insights to support data-driven business decisions.
