Android Step Implementation

1. Choosing the architecture for the android apps = MVVM
2. Creating UI and Navigation in Android Studio
3. Add validation and logic into the android apps
4. Connecting Android apps with cloud computing firebase
5. Implementing Machine learning into android apps
6. make sure that the apps is functioning as intended

Cloud Computing Implementation

1. Providing database for the application with firebase and firestore in GCP
2. Deploy Machine Learning model with cloud storage and AI platform
3. Deploy the application with app engine
4. Manage and monitor the billing and usage of gcp

Machine Learning Implementation

1. Modeling using Keras with Supervised Algorithm (Neural Network)
2. Using pandas to represent data frames. balances the dataset from 4:1 to 1:1. Divide the data into training and testing datasets using Sklearn with test_size = 0.2
3. Train the model and generate a file with .h5 format then convert it to .tflite to be implemented on android
4. The data obtained after testing and training reach 97% for accuracy training and 98% for validation accuracy