{"id":20977357,"url":"https://github.com/gusgitmath/o3_aqi_emission_ml","last_synced_at":"2026-04-21T22:31:21.663Z","repository":{"id":255378340,"uuid":"849440588","full_name":"GusGitMath/O3_AQI_Emission_ML","owner":"GusGitMath","description":"Analyzing O3 Air Quality Index trends (2000-2023) in the U.S., this project identifies regions with rising pollution. 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The project aims to offer insights into O3 pollution to support informed environmental policy-making.\n\n## Data\n- The data, initially compiled by BrendaSo and ANGELA KIM, was further enriched by me for the years 2021-2023.\n- **Data Repository**: [US Pollution Data on Kaggle](https://www.kaggle.com/datasets/guslovesmath/us-pollution-data-200-to-2022/data)\n\n## Notebooks\n\n### Detailed Analysis Notebooks:\n1. **Project Proposal Notebook**\n   - Initial proposal with preliminary EDA and regression analysis of O3 emission trends.\n2. **Main Project Notebook**\n   - Comprehensive analysis including:\n     - Detailed EDA and trend analysis from 2000-2023\n     - Data transformation, train-test split for forecasting\n     - Grid search for hyperparameter tuning, ACF and PACF analysis\n     - Forecasting using ARIMA, SARIMAX, and Holt-Winters methods\n\n## Project Summary\n\nThe 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.\n\n## Dependencies\n\n- **Programming Language**: Python 3.11\n- **Libraries**:\n  - Pandas 2.1.1\n  - Numpy 1.26.2\n  - Scikit-learn 1.3.1\n  - Statsmodels 0.14.0\n  - SciPy 1.11.3\n  - Matplotlib 3.8.0\n  - Plotly 5.18.0\n  - Seaborn 0.13.0\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgusgitmath%2Fo3_aqi_emission_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgusgitmath%2Fo3_aqi_emission_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgusgitmath%2Fo3_aqi_emission_ml/lists"}