Air Quality Prediction Dashboard
A forecasting dashboard that predicts next-day AQI for a city from public pollution and weather data.
Project overview
The project ingests historical air quality and weather observations, models the relationship between pollutants and meteorological conditions, and forecasts the air quality index for the next twenty-four to seventy-two hours with a health advisory band.
It is a strong choice for teams who want a data project with public-interest value and a defensible data source, since central and state pollution boards publish the underlying observations.
Problem statement
Air quality is reported after the fact. People with respiratory conditions, and schools deciding on outdoor activity, need a forward-looking estimate rather than yesterday's reading.
Proposed solution
Combine pollutant concentration history with weather variables such as temperature, humidity and wind speed, engineer lag features, and train a regression model to forecast the index. Present the forecast with a plain-language health advisory and a map or station selector.
Key features
- Automated ingestion of public air quality and weather data
- Handling of missing sensor readings and station outages
- Lag and rolling-window feature engineering
- Next-day and three-day AQI forecast per station
- Health advisory band mapped from the predicted index
- Historical trend view with seasonal comparison
- Station comparison across a city
Technology stack
Data
Modelling
Visualisation
Delivery
System architecture
The flow below is what goes into your architecture diagram and the corresponding chapter of the report.
- Scheduled fetch pulls the latest observations from public data endpoints
- Cleaning stage aligns timestamps, fills short gaps and flags long outages
- Feature stage builds lags, rolling means and weather interactions
- Model stage trains and validates on a chronological split, never a random one
- Forecast service produces predictions for each station on demand
- Dashboard shows current readings, forecast and advisory
Expected output
- AQI forecast for the next one to three days per station
- Health advisory category with recommended precautions
- Model error metrics on a held-out time period
- Seasonal and yearly trend charts
Suitable branches, difficulty and duration
Documentation, review, demo and viva support
Documentation support
Abstract, problem statement, literature survey guidance, system architecture, ER and UML diagrams, module descriptions, test cases and screenshots — prepared in your department format.
Project review support
Delivery is split across your review calendar so review one has a design and plan, review two has working modules, and the final review has the integrated, tested system.
Demo support
A rehearsed demo path, seeded sample data, a reset script, and a recorded walkthrough kept as a backup in case something fails in the review room.
Viva preparation
A written question bank for this domain, a code walkthrough session, and practice at explaining each module in your own words — because the examiner will ask you.
Customisation options
Colleges reject duplicate topics, so most projects are adjusted before we start. These are the usual directions for this one.
- Focus on Chennai stations for a locally relevant, well-scoped version
- Add pollutant-level forecasts instead of a single composite index
- Add an alert subscription that notifies users when a threshold is predicted
- Add a low-cost sensor node so the dashboard also shows your own measurements
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Enquire about the Air Quality Prediction Dashboard project
Send your branch, college and first review date. You will get confirmation of scope, what the customisation would look like, and a delivery plan mapped to your reviews.