Air quality_project

This project focuses on analyzing and predicting air quality levels using machine learning techniques.

The goal is to understand pollution patterns and classify or predict air quality based on environmental data such as temperature, humidity, and pollutant concentrations (e.g., PM2.5, PM10, CO, NO2).

The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, and building machine learning models to predict air quality levels or Air Quality Index (AQI).

Various algorithms such as Linear Regression, Random Forest, or other classification/regression models are used depending on the problem formulation.

The final output helps in understanding pollution trends and can support environmental monitoring and decision-making.