Oracle e-Business: Building a sample payment prediction machine learning model using oracle ERP data
Oracle e-Business: Payment Prediction Machine Learning Model
- AI Applications:
- AI can analyze customer payment patterns to predict when invoices will be paid.
- Natural Language Processing (NLP) can extract key payment terms from contracts or emails.
- Benefits:
- Enhanced cash flow forecasting.
- Better planning for financial operations.
Below is an example Python code for a Payment Prediction model using machine learning. This code uses the Scikit-learn library to build a predictive model based on historical payment data.
Payment Prediction Code
Steps in the Code:
Load and Prepare Data: The dataset includes historical payment data. Columns like
InvoiceAmount
,PaymentTerm
, andCustomerCreditScore
are features, andDaysToPay
is the target variable.Handle Missing Data: Missing values are replaced with the mean of the respective column for simplicity.
Split Data: Data is split into training and testing sets for model evaluation.
Build the Model: A Random Forest Regressor is used for its robustness in handling non-linear data and feature interactions.
Train and Evaluate: The model is trained on the training set and evaluated using metrics like Mean Absolute Error (MAE) and R² Score.
Predict New Data: The trained model predicts payment days for new invoices.
Installation Steps
1. Set Up a Virtual Environment (Optional but Recommended)
To avoid conflicts between libraries, create a virtual environment:
2. Install Required Libraries
Run the following command to install the required Python libraries:
3. Prepare the Dataset
- Save your historical payment data in a file named
payment_data.csv
(or another name). - Ensure the file includes columns:
InvoiceAmount
,PaymentTerm
,CustomerCreditScore
, andDaysToPay
.
Execution Instructions
1. Run the Python Script
Save the provided Python code in a file named payment_prediction.py
. To execute it, run:
2. Interpret Output
The script will:
- Train the machine learning model using historical data.
- Output evaluation metrics like Mean Absolute Error (MAE) and R² score.
- Predict the days to payment for new invoice data.
3. Add New Predictions
To predict payment days for new invoices, update the new_data
DataFrame in the script with your data.
Optional Steps
1. Save and Reload the Model
If you want to reuse the trained model without retraining, the script already saves it as payment_prediction_model.pkl
. To load the model for future predictions, use: