Sales Store dashboard and Sentiment analysis of social media
Budget: ₹1,500 – ₹12,500 INR
I will supply a mix of tidy CSV/Excel tables and raw social-media text, and I need the whole lot turned into clear, decision-ready insight. You’ll start by cleaning and modelling both the structured and unstructured data in Python and SQL, documenting every step so that the workflow is fully reproducible.
Once the data layer is solid, I want two analytical streams delivered:
• Sales Store Dashboard (built in Power BI). The must-have metric is Total Sales by Store, visualised so managers can compare outlets at a glance. I’d like the data model kept flexible enough for future add-ons such as monthly trends or customer demographics.
• Social Sentiment Analysis. Use NLP to classify posts as positive, negative or neutral, then surface the results in an intuitive visual inside the same Power BI environment.
If you happen to have GIS skills, a map layer that plots store performance would be a welcome bonus.
Final hand-off should include: the cleaned datasets, well-commented Python notebooks/scripts, any SQL queries, the .pbix file with interactive visuals, and a short guide that explains how to refresh or extend the model. Code quality, scalability and clarity are more important to me than quick hacks, so please approach the build with best practices in mind.
Once the data layer is solid, I want two analytical streams delivered:
• Sales Store Dashboard (built in Power BI). The must-have metric is Total Sales by Store, visualised so managers can compare outlets at a glance. I’d like the data model kept flexible enough for future add-ons such as monthly trends or customer demographics.
• Social Sentiment Analysis. Use NLP to classify posts as positive, negative or neutral, then surface the results in an intuitive visual inside the same Power BI environment.
If you happen to have GIS skills, a map layer that plots store performance would be a welcome bonus.
Final hand-off should include: the cleaned datasets, well-commented Python notebooks/scripts, any SQL queries, the .pbix file with interactive visuals, and a short guide that explains how to refresh or extend the model. Code quality, scalability and clarity are more important to me than quick hacks, so please approach the build with best practices in mind.