HyBeek/air-quality-analytics-and-forecasting

End-to-end data analytics and time series forecasting of PM2.5 air pollution using real-world environmental data, with a focus on Nigeria. # Air Quality Forecasting in Nigeria ## Overview This repository contains an end-to-end data analytics and forecasting project focused on PM2.5 air pollution in Nigeria. The project applies exploratory data analysis, time series modeling, and forecasting techniques to understand pollution patterns and predict future air quality levels. ## Objectives - Analyze historical PM2.5 air pollution data - Identify trends, seasonality, and anomalies - Build and evaluate time series forecasting models - Generate insights relevant to public health and environmental policy ## Data Sources - OpenAQ (global air quality data) - Meteorological datasets for contextual analysis ## Methodology 1. Data collection and ingestion 2. Data cleaning and preprocessing 3. Exploratory data analysis (EDA) 4. Time series modeling and forecasting 5. Model evaluation and interpretation ## Tools & Technologies - Python - Pandas, NumPy - Statsmodels, Scikit-learn - Matplotlib, Seaborn - Jupyter Notebook ## Project Status Repository initialized. Data ingestion and exploratory analysis will follow. ## Author HyBeek