Sales Prediction on Advertising Expenditures
About the project
The objective of the project is to predict the sales based on advertising expenses.
- 'TV' - expenses on TV advertising
- 'Radio' - expenses on Radio advertising
- 'Newspaper' - expenses on Newspaper advertising
- 'Sales' - target variable: Sales
Developed ML model is realized as a web service and deployed to the Google Cloud Platform.
Structure of the repository
The repository contains the next files and folders:
README.md- project documentationadvertising.csv- advertising dataset (https://www.kaggle.com/datasets/bumba5341/advertisingcsv/data)EDA and Model Training.ipynb- a notebook with exploratory data analysis and model trainingtrain.py- a python script to train the model with Random Forest Regressormodel.bin- a stored Random Forest Regressor modelPipfileandPipfile.lock- files with virtual environment for projectpredict.py- a python script to create a web service on the base of developed ML modelDockerfile- to containerize the developed ML modelpredict_local.py- a python file to test and work with the locally deployed modelpredict_cloud.py- a python file to test and work with the model, deployed to Google Cloud Platform
Virtual environment
Virtual environment of the project is provided by Pipfile and Pipfile.lock. These files contain all information about libraries and dependencies for the project. To create a virtual environment with libraries and dependencies required for the project, one should install pipenv library:
pip install pipenv
Then it's necessary to clone this repository from GitHub, open a terminal in the folder with this repository, and run the following commands:
pipenv install # to install project virtual environment
pipenv shell # to activate virtual environment
Running a web service in a local server
Developed final model is implemented in a web service. To run it, it's necessary to install Docker, create a container (which contains all system dependencies, libraries, scripts and others) and run it.
Docker may be installed from the official site https://www.docker.com/
File Dockerfile of the current repository (or cloned to your PC) contains all specifications to a container to be built: python, virtual environment, scripts and model file etc. To build a container one should start a Docker, open a terminal or command window and enter the next command:
docker build -t advertising . (For Linux)
docker buildx build --platform linux/amd64 -t advertising . (For Apple M1)
Once docker container is built, you can run it with the next command:
docker run -it --rm -p 9696:9696 advertising:latest
A script predict_local.py sends to the local server a condo with following features in json format:
data = { 'TV': 180.8, 'Radio': 10.8, 'Newspaper': 58.4, }
The result of script's work should be as follows:
{ "predicted_advertising": 2.9011847847125116 }
Running a web service in a Google Cloud
The web service is also deployed to Google Cloud Platform (Cloud Run) with the next commands:
gcloud config set project <project_ID> # create a project (In this project, the project ID is galvanic-circle-401908)
docker images # obtain a list of docker images to get exactly the name of needed image
docker tag advertising:latest gcr.io/galvanic-circle-401908/advertising_server # create a tag to image
docker push gcr.io/galvanic-circle-401908/advertising_server # push image to Google Container Registry
gcloud run deploy advertising --image gcr.io/galvanic-circle-401908/advertising_server --port 9696 --platform managed --region us-central1 # deploy image
Web service API is available on URL: https://advertising-ogkgw3gxza-uc.a.run.app/predict
A script predict_cloud.py sends to Google cloud server a condo with the following features in json format:
data = { 'TV': 180.8, 'Radio': 10.8, 'Newspaper': 58.4, }
The result of script's work should be as follows:
{ "predicted_advertising": 2.9011847847125116 }
I would like to thank DataTalksClub: Machine Learning Zoomcamp team for your insightful lectures.