This project addresses the challenge of understanding air quality trends across various states and cities in India, leveraging extensive time-series data from 2010 to 2023. The goal is to analyze patterns, seasonal variations, and pollutant levels to aid in environmental monitoring and health-related research.
This repository provides a comprehensive analysis using Python, with libraries such as Pandas, Matplotlib, Seaborn, and Plotly for data manipulation and visualization. It covers data loading, preprocessing, exploratory data analysis, visual representation of trends, and statistical analysis of pollutant levels. It enables users to identify critical hotspots of pollution and understand long-term trends and seasonal impacts on air quality.
Data extraction and preprocessing
Exploratory Data Analysis (EDA)
Time series decomposition
Trend and seasonality identification
Autocorrelation analysis
Visualization of pollutant distribution across different regions
This solution assists policymakers, researchers, and public health officials to make informed decisions based on data-driven insights into air quality trends.