AQI-Python-Analysis

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AQI-Python-Analysis

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Air Quality Index data analysis using Python for pollution trend identification and visualization.

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# Air Quality Index (AQI) Data Analysis

## Project Overview
This project performs exploratory data analysis (EDA) on Air Quality Index (AQI) data to understand pollution trends and environmental patterns. The analysis focuses on identifying relationships between different air pollutants and AQI levels using Python-based data analysis and visualization techniques.

## Objectives
- Analyze air quality data to identify pollution trends
- Study the impact of different pollutants on AQI levels
- Perform data cleaning and preprocessing
- Visualize environmental patterns using graphs and charts

## Dataset
The project uses a real-time Air Quality Index dataset containing information about various air pollutants and AQI values.

File included:
- Real Time Air Quality Index Dataset.csv

## Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook

## Project Workflow
1. Data loading and inspection
2. Data cleaning and preprocessing
3. Exploratory data analysis (EDA)
4. Data visualization
5. Pattern and trend identification

## Key Features
- Handling missing values and data inconsistencies
- Statistical analysis of pollution levels
- Visualizations of AQI trends
- Comparison of air quality indicators

## Repository Structure
AQI-Data_Analysis/
│
├── Real Time Air Quality Index Dataset.csv
├── eda AQI.py

## Results
The analysis provides insights into pollution patterns and helps understand variations in air quality levels using data-driven visualization techniques.

## Author
Soumya
GitHub: https://github.com/soumya8204

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