Tobai24/Air-Quality-Lagos

Welcome to the "Air Quality in Lagos Time Series Analysis" repository! This project is dedicated to analyzing and predicting PM2 levels in Lagos, Nigeria, using a dataset sourced from low-cost sensors. Our goal is to provide valuable insights into air quality trends and the effectiveness of these sensors in measuring PM concentrations. # Air-Quality-Lagos # Air Quality in Lagos Time Series Analysis Welcome to the "Air Quality in Lagos Time Series Analysis" repository! This project is dedicated to analyzing and predicting PM2 levels in Lagos, Nigeria, using a dataset sourced from low-cost sensors. Our goal is to provide valuable insights into air quality trends and the effectiveness of these sensors in measuring PM concentrations. ## Project Overview This project focuses on time series analysis for predicting PM2 (particulate matter with a diameter of 2.5 micrometer or less) using a dataset containing PM, temperature, and humidity readings collected from low-cost sensors. The dataset provides information on sensor types, date, time, location, and specific measurements for temperature, humidity, PM1, PM2.5, and PM10. This dataset is valuable for researchers and individuals interested in studying air quality and the efficacy of low-cost sensors in PM measurement. ## Dataset Overview - **File Format:** CSV - **Features:** - **Date:** Date of the reading - **Time:** Time of the reading - **Location:** Location where the reading was taken - **Sensor Type:** Type of sensor used - **Temperature (C):** Temperature measurement in Celsius - **Humidity (%):** Humidity measurement in percentage - **PM1:** Particulate matter with a diameter of 1 micrometer or less - **PM2.5:** Particulate matter with a diameter of 2.5 micrometers or less - **PM10:** Particulate matter with a diameter of 10 micrometers or less - **Note:** In the dataset, 'P0' represents PM1, 'P2' represents PM2.5, and 'P1' represents PM10. ## Contributing Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on GitHub. ## License This project is licensed under the MIT License. ## Acknowledgements We acknowledge the creators of the dataset for providing valuable data for research and analysis. ## Contact For any inquiries or feedback, plea …