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🌍 Air Quality Prediction using Machine Learning

📌 Project Overview

This project predicts Air Quality Levels using Machine Learning algorithms such as:

  • 🌲 Decision Tree Classifier
  • 🌳 Random Forest Classifier

The system analyzes environmental and pollution-related parameters like temperature, humidity, PM2.5, PM10, NO₂, CO, and AQI to classify air quality into categories such as:

  • Good
  • Moderate
  • Unhealthy for Sensitive Groups
  • Unhealthy
  • Hazardous

🎯 Objectives

  • Load and analyze air quality datasets
  • Perform data preprocessing
  • Train Machine Learning models
  • Evaluate model performance
  • Visualize important insights
  • Predict air quality for new samples

🧠 Machine Learning Models Used

1️⃣ Decision Tree Classifier

A supervised learning algorithm that creates decision rules based on dataset features.

2️⃣ Random Forest Classifier

An ensemble learning algorithm that combines multiple decision trees for improved accuracy and reduced overfitting.

📂 Dataset Features

Feature Name Description
temperature_c Temperature in Celsius
humidity_pct Relative Humidity (%)
wind_speed_kmh Wind Speed (km/h)
pm25 Fine Particulate Matter
pm10 Coarse Particulate Matter
no2 Nitrogen Dioxide
co Carbon Monoxide
aqi Air Quality Index
city City Name

🎯 Target Column

air_quality_label

Possible labels:

  • Good
  • Moderate
  • Unhealthy for Sensitive
  • Unhealthy
  • Hazardous

⚙️ Technologies Used

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Scikit-learn

📦 Required Libraries

Install all dependencies using:

pip install pandas numpy matplotlib seaborn scikit-learn