Olamilekan002/Drone-Fault-Tester

UmojaHack Africa 2022 #3: Faulty Air Quality Sensor Brief Description The objective of this challenge is to create a classification model to identify a device has an off set fault or not, regardless of the device. The model can be used by AirQo to automatically flag a device that is returning faulty data., have a look on Zindi. # Umoja-Hack-Africa-2022 🥇 ### UmojaHack Africa 2022 #3: Faulty Air Quality Sensor Brief Description. The objective of this challenge is to create a classification model to identify a device has an off set fault or not, regardless of the device. The model can be used by AirQo to automatically flag a device that is returning faulty data. Have a look on Zindi. Deployment link. ### Approach ⏬ * Import all necessary Libraries * Load Data (Test and Train) * EDA * Feature Engineering * Modelling * Deployment #### Load Data 📉 The train contain 293563 records of time series data and 7 fields, dated from 2021 - 2022. * The missing values were filled using forward fill approach because it is a time series data. * Outliers were replaced with mean in some features. #### Featuring Engineering ☸️ * Date diff was extracted * Day, month and hour of report was extracted * More features were generated from other features #### Model 🚀 The base model was RandomForest with an accuracy of 93% using stratifiedKFold for data splitting. Other models include: * Catboost: Accuracy 96% * LightGBM: Accuracy 94% * Xgboost: Accuracy 93% #### Deployment ✈️ The model was deployed to Heroku using flask and other necessary libraries. link