Sriti-28

Sriti-28 / Air-Quality-Prediction-Analysis

Public

Air Quality Prediction using Machine Learning with EDA, regression modeling, and pollutant impact analysis

16
0
100% credibility
Found Apr 16, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This project analyzes global air pollution data to predict Air Quality Index values and examines how pollutants like PM2.5 impact air health.

How It Works

1
🔍 Discover the Project

While curious about air pollution, you find this helpful guide on predicting air quality.

2
📖 Read the Big Picture

You learn how it studies worldwide air data to forecast clean or dirty air levels.

3
📊 Explore Pollution Patterns

You check out charts showing links between tiny particles, gases, and overall air health.

4
🤖 See Air Quality Predictions

The clever tool guesses future air scores from current pollution readings with spot-on accuracy.

5
🔬 Play What-If Games

You imagine bumping up pollution by 10% and watch how it worsens the air quality.

🌍 Gain Earth-Smart Insights

You walk away knowing tiny particles rule air quality and ready for eco-friendly choices.

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Star Growth

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AI-Generated Review

What is Air-Quality-Prediction-Analysis?

This Jupyter Notebook project takes global air pollution data and predicts the Air Quality Index (AQI) with regression models like linear regression and Random Forest, while running exploratory data analysis (EDA) to spot pollutant impacts. It solves the challenge of understanding how PM2.5, NO2, CO, and Ozone drive AQI levels, complete with what-if simulations for pollutant changes like a 10% increase. Built on Python with Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, users get clean notebooks for prediction, correlation insights, and sensitivity analysis right away.

Why is it gaining traction?

It stands out with practical sensitivity analysis that quantifies AQI shifts from pollutant tweaks, plus handling real-world quirks like AQI capping at 500—features missing in basic air quality index scripts on GitHub. Developers grab it for the high R² score around 0.97 and PM2.5 dominance insights, making it a quick win for air quality prediction and analysis using machine learning. Searches for air quality monitor tools or air quality map prototypes often land here over fancier air quality sensor home assistant integrations.

Who should use this?

Data scientists prototyping air quality berlin or air quality bangkok dashboards, environmental analysts testing pollutant effects, or ML students tackling regression on public datasets. It's ideal for researchers building air quality detector models or what-if scenarios without starting from scratch, especially if you're eyeing air quality hamburg trends.

Verdict

Skip for production—12 stars and 1.0% credibility score signal it's an early prototype with just a README and no tests. Solid learning resource for air quality prediction basics, but wait for real-time data or LSTM upgrades before relying on it.

(178 words)

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