End-to-end machine learning pipeline to predict customer churn.
Live Demo: http://54.89.48.203:8501

• Organized the code in different directories

  root_directory/
  │
  ├──app.py
  ├── utils.py
  ├── data/
  ├── models/
  ├── notebooks/
  ├── prompts/
  ├── README.md
  └── requirements.txt
  

• Pre-processing
  • Fitted StandardScaler only with the train data
  • Saved fitted scaler to use during inference
• Training
  • Added a CatBoostClassifier model
  • Set hyperparmeters for tree-based models
• Inference
  • Added pre-processing to properly handle inference data
• Prompt Engineering:
  • Simplified and reorganized prompts
  • Provided feature importance values as JSON
• Deployment
  • Deployed Streamlit web app with AWS

1. Create an EC2 instance in AWS:

   • OS: Ubuntu
   • Instance Type: t2.micro
   • Create a new Key pair to access the instance via SSH

2. Download the .pem file amd set permissions:

   chmod 400 <your-key-file.pem>

3. Connect via SSH

   ssh -i </path/to/your-key-file.pem ubuntu@your-instance-public-ip>

4. Clone repo

   git clone https://github.com/matteopilotto/customer-churn-prediction.git
   

5. Create a .env file in the root directory of the repo and add the Groq api key:

   GROQ_API_KEY=<your_groq_api_key>
   

6. Update sytem, set up Python environment, and install required libraries:

   chmod +x setup.sh
   source setup.sh

7. Create a new tmux session:

   tmux new -s streamlit_session
   

8. Launch streamlit app:

   streamlit run app.py --server.address 0.0.0.0 --server.port 8501
   

9. To detach from the tmux session press CTRL+B + D

10. Access the Stramlit app via the EC2 public IP address:

    http://<your-ec2-public-ip>:8501
    

• To reattach to a tmux session, use the following command:

  tmux attach -t <your-tmux-session-name>
  

• To list the active tmux sessions, use the following command:

  tmux ls
  

• To terminate a tmux session, use the following command:

  tmux attach -t <your-tmux-session-name>
  

• Headstarter.co
• Matteo Pilotto