🌍 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

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🎯 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

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🧠 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.

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📂 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

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⚙️ Technologies Used

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

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📦 Required Libraries

Install all dependencies using:

pip install pandas numpy matplotlib seaborn scikit-learn