Duks31/air_quality

Abuja (Nigeria), Air Quality Prediction ## Project Overview The project aims to develop an air quality predictor using machine learning techniques. It utilizes a workflow depicted in the image "aqp_workflow.png" located in the "images" directory. The repository contains various documents and scripts that are essential for the project. The project overview includes the following key components: * Data Collection: The project involves gathering air quality data from multiple sources, such as sensors, weather stations, and government databases. * Data Preprocessing&Backfilling: The collected data is preprocessed to handle missing values, outliers, and other data quality issues. Additionally, historical data is backfilled to ensure a complete dataset for model training. * FTI Pipeline: * Feature Pipeline: The feature pipeline extracts relevant features from the preprocessed data and transforms them into a format suitable for model training. * Model Training: The project trains a machine learning model using the processed data to predict air quality levels based on historical data. * Batch Inference: The trained model is used to make predictions on new data, providing insights into future air quality levels. ## Project Structure ``` sh │ .env │ .gitignore │ hopsworks-api-key.txt │ README.md │ requirements.txt │ ├───.github │ └───workflows │ air-quality-daily.yaml │ ├───data │ abuja-air-quality.csv │ ├───docs │ │ index.html │ │ │ └───air-quality │ └───assets │ └───img │ pm25_forecast.png │ pm25_hindcast_1day.png │ ├───images │ aqp_workflow.png │ pm25 forecast.png │ └───notebooks │ .cache.sqlite │ air_quality_batch_inference.ipynb │ air_quality_feature_backfill.ipynb │ air_quality_feature_pipeline.ipynb │ air_quality_training_pipeline.ipynb │ ├───air_quality_model │ model.json │ ├───functions │ util.py │ └───images feature_importance.png pm25_hindcast.png ``` ## Demo NOTE: The demo …