hazunanafaru/retention-prediction-demo

Retentio Prediction for bangkit 2022 demo

Retention Prediction for CC-25

🇮🇩 Part of Bangkit 2022

🛑 Please use it wisely! Remeber Bangkit 2022 Code of Conduct! Use it as reference only!

Stack used

1. 🤖 Tensorflow 2.9
2. 🍾 Flask 2.10

Google Cloud Service used

1. ⚡️ Cloud Run

Local requirements

1. 🐍 Python 3
2. 🐳 Docker
3. 🚦 Any IDE you like

👨🏽‍💻 How to use in local

📒 Notebook

1. Clone this repositroy
2. Run python install -r requirements.txt
3. Open the retention-prediction.ipynb

🔥 API (Flask)

Please ignore the expressjs folder. It's my failure 🥲

1. Clone this repositroy
2. Open flask folder
3. Run python install -r requirements.txt
4. Create .env file in root folder with this specifications

Variables	Function	Example
PORT	your_API_port	8081
MODEL_LAYER	your_model_layer_path	model.json
MODEL_WEIGHTS	your_model_weights_path	weights.h5
STANDARD_SCALER	your_standard_scaler_path (opt)	sc.pkl

5. Run docker build -t retention-pred:v1.0 .
6. Run export PORT=your_API_port change to match API port
7. Run docker run --name retention-prediction -p 0.0.0.0:$PORT:$PORT -d retention-pred:v1.0
8. Test with Postman or CLI

☁️ How to use in Cloud

⚡️ Deploy ML API with Cloud Run

✅ First, let's create our Service account

🚨 This still in development. Please jump to the second step

1. Run export PROJECT_ID=$(gcloud config get-value project)
2. Run export SA=retention-prediction
3. Run this to create retention-prediction service account

gcloud iam service-accounts create $SA \
    --description="retention-prediction SA" \
    --display-name="retention-prediction"

3. Run this to bind service account with Storage Admin role

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA@$PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/storage.admin"

4. Run this to get service account key as key.json

gcloud iam service-accounts keys create key.json \
    --iam-account=$SA@$PROJECT_ID.iam.gserviceaccount.com

5. Add s into the .env file

Variables	Function	Example
GOOGLE_APPLICATION_CREDENTIALS	your_service_account_path	./key.json

6. 🚨 Keep it secret okay. Don't push your service account key into the github!

✅ Then we're ready to deploy our Cloud Run

1. Run docker build -t gcr.io/$PROJECT_ID/retention-pred:v1.0 .
2. Run gcloud auth configure-docker and press Y
3. Run docker push gcr.io/$PROJECT_ID/retention-pred:v1.0
4. Run gcloud run deploy --image gcr.io/$PROJECT_ID/retention-pred:v1.0 --platform managed
5. Check your GCP Console > Cloud Run

Misc

• If you encounter error below, run sudo apt install python3-tk

ModuleNotFoundError: No module named 'tkinter'

References :

[1] https://www.digitalocean.com/community/tutorials/how-to-build-a-deep-learning-model-to-predict-employee-retention-using-keras-and-tensorflow

[2] https://cloud.google.com/run/docs/quickstarts/build-and-deploy/deploy-python-service