Background

• A newly established e-commerce company has high traffic but a low web-to-purchase conversion rate. Therefore, we predict the likelihood of each user making a purchase and implement targeted discount policies to encourage them to buy.

Achievements

• RFM Analysis: Conducted detailed RFM (Recency, Frequency, Monetary) analysis to classify users based on their purchasing behavior.
• Purchase Propensity Model: Used a propensity model to forecast user behavior and identify individuals who need encouragement to make a purchase.

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Run Docker

Ensure that Docker is installed on your machine. Check by running:

docker --version

Step 1: Build Docker Image

Navigate to the directory containing the Dockerfile for the Customer-propensity-to-purchase application:

cd Customer-propensity-to-purchase

Build the Docker image from the Dockerfile:

docker build -t customer-propensity-to-purchase .

Step 2: Run the Customer-propensity-to-purchase Application

Run the Customer-propensity-to-purchase application in a Docker container:

docker run -p 8000:8000 customer-propensity-to-purchase

The application will run and listen on port 8000.

Step 3: Log in to Docker Hub

Log in to Docker Hub from the terminal:

docker login

Enter your username and password when prompted.

Step 4: Tag and Push Docker Image to Docker Hub

Tag your Docker image with the repository name on Docker Hub:

docker tag customer-propensity-to-purchase:latest diends/yourname:image_name

Push the Docker image to Docker Hub:

docker push diends/yourname:image_name

Step 5: Run Application from Docker Hub

Now, you can run the backend application from the image pushed to Docker Hub:

docker run -it --name customer-propensity-to-purchase -p 8000:8000 diends/yourname:image_name

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Make sure to replace diends with your Docker Hub username and yourname with your application/yourname.