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https://github.com/gusgitmath/o3_aqi_emission_ml

Analyzing O3 Air Quality Index trends (2000-2023) in the U.S., this project identifies regions with rising pollution. Utilizing exploratory data analysis and time-series modeling, it offers actionable insights for informed policy decisions on urgent O3 pollution issues.
https://github.com/gusgitmath/o3_aqi_emission_ml

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Analyzing O3 Air Quality Index trends (2000-2023) in the U.S., this project identifies regions with rising pollution. Utilizing exploratory data analysis and time-series modeling, it offers actionable insights for informed policy decisions on urgent O3 pollution issues.

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# Ozone AQI Trends Project

## Important Viewing Information
For the best viewing experience, especially due to the size and complexity of the notebooks, please view this project on Kaggle or download the notebooks.
- **Project Proposal Notebook**: [Proposal on Kaggle](https://www.kaggle.com/code/guslovesmath/o3-aqi-trends-in-high-impact-regions-proposal)
- **Main Project Notebook**: [Project on Kaggle](https://www.kaggle.com/code/guslovesmath/o3-aqi-trends-in-high-impact-regions-project)

## Overview

This repository hosts notebooks and data for analyzing Ground-level Ozone (O3) Air Quality Index (AQI) trends in critical U.S. regions from 2000 to 2023. The project aims to offer insights into O3 pollution to support informed environmental policy-making.

## Data
- The data, initially compiled by BrendaSo and ANGELA KIM, was further enriched by me for the years 2021-2023.
- **Data Repository**: [US Pollution Data on Kaggle](https://www.kaggle.com/datasets/guslovesmath/us-pollution-data-200-to-2022/data)

## Notebooks

### Detailed Analysis Notebooks:
1. **Project Proposal Notebook**
- Initial proposal with preliminary EDA and regression analysis of O3 emission trends.
2. **Main Project Notebook**
- Comprehensive analysis including:
- Detailed EDA and trend analysis from 2000-2023
- Data transformation, train-test split for forecasting
- Grid search for hyperparameter tuning, ACF and PACF analysis
- Forecasting using ARIMA, SARIMAX, and Holt-Winters methods

## Project Summary

The O3 pollution project delivers actionable insights through extensive exploratory data analysis (EDA) and advanced time-series modeling techniques. These insights are documented in two primary notebooks.

## Dependencies

- **Programming Language**: Python 3.11
- **Libraries**:
- Pandas 2.1.1
- Numpy 1.26.2
- Scikit-learn 1.3.1
- Statsmodels 0.14.0
- SciPy 1.11.3
- Matplotlib 3.8.0
- Plotly 5.18.0
- Seaborn 0.13.0