https://github.com/rauhanahmed/store-sales-forecasting
End-to-End Python based ML project focusing on forecasting multiple multivariate time series with production grade deployment techniques.
https://github.com/rauhanahmed/store-sales-forecasting
darts docker dockerhub dvc fastapi git github github-actions machine-learning mlops-workflow multiple-time-series multivariate-timeseries python python3 time-series webhook
Last synced: about 2 months ago
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End-to-End Python based ML project focusing on forecasting multiple multivariate time series with production grade deployment techniques.
- Host: GitHub
- URL: https://github.com/rauhanahmed/store-sales-forecasting
- Owner: RauhanAhmed
- License: gpl-3.0
- Created: 2023-10-21T20:04:40.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-03T12:04:21.000Z (over 2 years ago)
- Last Synced: 2025-03-02T11:33:24.729Z (over 1 year ago)
- Topics: darts, docker, dockerhub, dvc, fastapi, git, github, github-actions, machine-learning, mlops-workflow, multiple-time-series, multivariate-timeseries, python, python3, time-series, webhook
- Language: Jupyter Notebook
- Homepage: https://store-sales-api.onrender.com
- Size: 38.7 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Dynamic Sales Forecasting with Multiple-Multivariate Time Series
This Python, [ML-based API](https://store-sales-api.onrender.com) with an impressive Normalised Mean Squared Error of 0.039 and Normalised Mean Absolute Error of 0.10 for forecasts of this multiple, multivariate timeseries data on a 15-day window. This POC aims to empowers retailers with data-driven sales forecasting to optimize inventory management and improve profitability.
This comprehensive solution predicts short-term sales (up to 30 days) with outstanding accuracy and performance metrics. By integrating seamlessly with existing workflows, it enables retailers to:
- Make informed inventory decisions.
- Reduce stockouts and overstocking.
- Improve operational efficiency.
- Boost customer satisfaction.
## Screenshots and Demo
## Model Performance Metrics (15-day forecast)
| MSE
(on normalised data) | MAE
(on normalised data) |
|------------------|------------------|
| 0.03967 | 0.10960 |
## The Approach
- Fetching of the data from the MongoDB database using Python's PyMongo library.
- Performing in-depth analysis of data in a Jupyter Environment.
- Performing Feature Engineering to prepare the data for modelling.
- Forecasting oil prices using a LightGBM Model for preparing the covariates for the actual model.
- Fitting another LightGBM Model to forecast sales with the target values and covariates.
- Converting the Jupyter code to modular format for building code pipelines efficiently.
- Setting up the github repository, dockerhub repository using git and docker dekstop.
- Running DVC and Git on the built artifacts during the training process for file versioning and tracking.
- Creating the REST API using Python's FastAPI.
- Creating a Dockerfile for building the docker image for containerization of the api.
- Signing up and logging in on [Render Cloud]("https://render.com/") and creating a web service with the configuration of dockerized deployments.
- Setting up Github Actions and creating a workflow with the jobs of building the docker image, pushing the image to Dockerhub, and finally use Render Cloud's webhook to trigger deployment of the api once a push is made to dockerhub.
## Tech Stack
Built on a robust open-source ecosystem, it leverages:
- **Darts :** For efficient time series operations and forecasting.
- **MongoDB :** For storage and retrieval of data.
- **LightGBM :** To accurately predict covariate and target features.
- **Scikit-learn :** For creating data pipelines.
- **DVC, Git, and Github :** For seamless data and code versioning.
- **Evidently AI:** To check for data drift/target drift.
- **FastAPI :** For building a user-friendly API for model accessibility.
- **Docker and Dockerhub :** For secure and streamlined deployment to the Render Cloud Platform.
- **Github Actions :** For automating the CI/CD pipeline.
- **Render Cloud :** A PaaS for deployment of web apps, api's, etc.
## Run Locally
Clone the project
```bash
git clone https://github.com/rauhanahmed/store-sales-forecasting
```
Go to the project directory
```bash
cd store-sales-forecasting
```
Install dependencies
```bash
pip install -r requirements.txt
```
Start the server
```bash
uvicorn app:app --reload
```
## Run the Train Pipeline
For running the train pipeline, follow the below steps:
- Create a [MongoDB Atlas]("https://www.mongodb.com/cloud/atlas/register") account, or sign in if already exists.
- Create an environment file, named *secrets.env* in the project's main directory with the keys of environment variables as *MONGODB_USERNAME* and *MONGODB_PASSWORD* and fill their respective values in [URL encoded format]("https://www.mongodb.com/docs/atlas/troubleshoot-connection/#special-characters-in-connection-string-password")
- Install the project's requirements by using the command:
```bash
pip install -r requirements.txt
```
- run the train_pipeline by using the command:
```bash
!python /src/pipelines/train_pipeline.py
```
## Deployment
For deployment of the application via dockers, follow the below steps:
- Create a docker image of the application using the below command, replacing and after installing docker desktop and signing up on [DockerHub](https://dockerhub.com).
```bash
docker build -t : .
```
- Test the container locally
```bash
docker run -p 8000:8000 :
```
- Push to DockerHub replacing , and the with the dockerhub username, image name and the image tag respectively
```bash
docker push /:
```
- Sign up on [Render Cloud](https://render.com)
- Create a Web Service on Render and choose to deploy via dockers
- Specify the url of the dockerhub repository and start build
- Use the URL provided by render after successful deployment of application after few minutes :)
## Connect with me
[](https://www.linkedin.com/)
[](https://github.com/rauhanahmed/)
[](https://instagram.com/ahmed.rauhan)
## Support
For support, email rauhaan.siddiqui@gmail.com. I would be happy to help!
## License
[MIT](https://choosealicense.com/licenses/mit/)