A web-based Machine Learning application designed to predict delays in ERP processes using logistic regression. Built with Flask, Bootstrap, and a clean modular architecture for scalability.
- Predict ERP delays using trained ML model
- Predict ERP delays using Random Forest Machine Learning model
- Intuitive Bootstrap UI
- Modular structure for maintainability
- REST API for prediction
- Cleaned and preprocessed ERP dataset
- Python (Flask)
- Scikit-learn
- Pandas, NumPy
- HTML/CSS (Bootstrap)
- GitHub + Git
erp-delay-predictor/
│
├── api.py # REST API using Flask
├── train_model.py # Script to train and save model
├── model/ # Folder storing trained model (.pkl)
├── requirements.txt # Project dependencies
└── README.md # Documentation
# Clone the repository
git clone https://github.com/Vsandeep-ai-dev/erp-delay-predictor.git
cd erp-delay-predictor
# Create a virtual environment
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
# Install dependencies
pip install -r requirements.txt- Upload or input data via UI or API.
- Data is cleaned using
cleaner.py. - Prediction is made using the trained model.
- Train the model (required before running API) python train_model.py.
- Output is shown in browser. 🔍 Model
The model used is a Random Forest Classifier, trained on a custom ERP dataset to predict delivery delays based on key features like quantity, price, rating, vendor score, shipping speed, and demand.
python api.pyVisit http://localhost:5000 in your browser.
- Endpoint:
/predict - Method: POST
- Payload Example:
{
"quantity": 50,
"price": 15.0,
"rating": 4,
"vendor_score": 8,
"ship_speed": 2,
"demand": 60
}
...
- Machine Learning
- Flask Web App
- ERP Systems
- Logistic Regression
- API Deployment
- UI with Bootstrap
- Random Forest Classifier
✅ Public and ready to showcase.
- Developer: Sandeep Reddy
- GitHub: @Vsandeep-ai-dev