Introduction to Air Quality Prediction
In recent years, air pollution has become a daily reality for millions of people around the globe. From smog-filled cities to alarming health advisories, poor air quality affects how we live, breathe, and move. But what if we could predict tomorrow’s Air Quality Index (AQI) just like we predict the weather — and take action before the air turns toxic? That’s where machine learning steps in.
Why Predict Air Quality?
Air pollution isn’t just an inconvenience — it’s a health crisis. According to the WHO, it’s responsible for nearly 7 million premature deaths each year. High AQI levels are associated with asthma attacks, respiratory diseases, cardiovascular issues, and even cognitive decline. If we can predict AQI accurately, communities can receive early warnings, urban planners can design smarter, cleaner cities, and individuals can decide when it’s safe to go for a jog or send kids to play outside.
Can Machine Learning Help?
The million-dollar question is: Can machine learning help us predict air quality in time to protect our health? Let’s find out. We will be using the Air Quality in India dataset from Kaggle, which tracks pollutant levels and AQI values across Indian cities from 2015 to 2020.
Dataset Overview
The dataset contains daily air quality data from major cities across India, collected between 2015 and 2020. It includes concentrations of various pollutants, meteorological parameters, and calculated AQI values. We will be using the city_day.csv file, which contains daily air quality data per city.
Our Approach: Classical ML vs Ensemble Learning
We will walk through a complete ML workflow using Python to forecast AQI, comparing two models: Linear Regression and Random Forest Regressor. Linear Regression is simple, fast, and interpretable, while Random Forest Regressor is powerful, robust, and accurate.
Step 1: Loading the Data
We’re using the Air Quality in India dataset from Kaggle. This dataset includes measurements for PM2.5, PM10, NO₂, CO, and other major pollutants.
Step 2: Cleaning Things Up
Like most real-world data, this one’s a bit messy. So, let’s tidy it up by dropping rows with missing target values, converting the date column to datetime format, and filling missing values with the median.
Model Evaluation
We will evaluate the performance of both models using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Score.
Comparative Analysis: Linear Regression vs Random Forest
We will compare the performance of both models and discuss when to use each. Linear Regression is suitable for small, clean datasets where interpretability is important, while Random Forest is suitable for large, complex datasets where accuracy is paramount.
When to Use What?
Choose Linear Regression if you want interpretable results, your dataset is small or clean, and you want a fast, lightweight model. Choose Random Forest if you need high accuracy, your data has non-linear relationships, and you’re okay with a black-box approach.
Conclusion
Machine learning isn’t just about numbers on a screen — it’s about unlocking insights that can change lives. By harnessing environmental data, we’re not only predicting air quality — we’re empowering people to act before it becomes dangerous. Whether it’s helping parents decide if it’s safe for their kids to play outside or aiding city planners in reducing pollution hotspots, every insight brings us one step closer to healthier communities.
FAQs
- Q: What is Air Quality Index (AQI)?
A: Air Quality Index (AQI) is a measure of the level of air pollution in a given area. - Q: How can machine learning help predict air quality?
A: Machine learning can help predict air quality by analyzing historical data and identifying patterns and relationships between pollutant levels and meteorological parameters. - Q: What are the benefits of predicting air quality?
A: Predicting air quality can help communities receive early warnings, urban planners design smarter, cleaner cities, and individuals decide when it’s safe to go for a jog or send kids to play outside. - Q: What is the difference between Linear Regression and Random Forest Regressor?
A: Linear Regression is a simple, fast, and interpretable model, while Random Forest Regressor is a powerful, robust, and accurate model that can handle non-linear relationships and complex datasets.
