This paper details the creation and implementation of a machine learning system designed to predict credit lending approval. The system comprises two main components: a predictive model (prediction_model.ipynb) developed in a Jupyter notebook environment, and a web application (app.py) built using the Dash framework. The predictive model employs a several models to predict credit approval based on various applicant features, while the web application serves as an interface for users to input applicant data and receive predictions.
The objective of the credit lending prediction system is to assist financial institutions in making an informed choice regarding approving credit applications. By automating the approval process, the system aims to increase efficiency and reduce the subjectivity in manual decision-making processes.
The system is structured into two main components:
Predictive Model: Developed in a Jupyter Notebook, this component utilizes scikit-learn’s various model classifier to model credit approval probabilities based on historical data. The model was trained, validated, and tested using a dataset comprising features such as income level, employment history, age, and other relevant financial information.
Web Application: Built using Dash, this component provides a user-friendly interface for inputting applicant data and retrieving predictions. The application integrates with the predictive model, allowing real-time predictions that facilitate immediate decision-making.
The predictive model was developed using the scikit-learn library in a Jupyter Notebook, providing a robust platform for iterative data analysis, visualization, and model training. The model development process involved several key steps:
Cleaning: The dataset was cleaned to remove inconsistencies and handle missing values through imputation techniques, ensuring that the data was suitable for training. Feature Engineering: New features were created based on existing data to enhance the model’s ability to learn significant patterns. This included combining categories, creating interaction terms, and deriving polynomial features.
Visualization tools such as histograms, box plots, and scatter plots were used to understand the distribution and relationships of features. Correlation matrices were employed to identify relationships between different features and the target variable, helping in feature selection.
After ananlyzing the performance of several models between logistic regression, decision tree classifier, random forest classifier, a k-nearest neighbors classifier, a naive bayes classifier, and a SVC model, A decision tree classifier was chosen for its interpretability and effectiveness in handling non-linear relationships. The model was trained using a split of training and validation data, allowing for the evaluation of its performance and the tuning of parameters to avoid overfitting.
The model was rigorously tested using unseen test data. Metrics such as accuracy, precision, recall, and the F1-score were calculated to assess its performance. A confusion matrix was generated to visualize true positives, false positives, true negatives, and false negatives, providing insights into the type of errors made by the model.
Once trained and validated, the model was serialized using the pickle module, enabling it to be saved and later loaded for prediction without retraining.
The application interface, built using the Dash framework, serves as the frontend through which users interact with the predictive model. Dash provides a powerful yet straightforward approach to building interactive applications purely in Python, without requiring direct handling of HTML, CSS, or JavaScript.
The layout is defined in a Python script using Dash’s HTML components. Each input field, such as dropdowns for categorical features and text boxes for numerical entries, is clearly labeled and organized logically for user ease. Interactive components allow users to select or input their data, which is then passed to the predictive model for real-time predictions.
As users input their data, the application checks for validity and completeness. For example, it ensures that all required fields are filled and that numeric inputs fall within plausible ranges.
When the user submits their data via a submit button, a callback function is triggered. This function collects all input values, formats them to match the model’s expectation (e.g., reshaping the input into a dataframe), and passes them to the loaded predictive model. The model then processes this input to predict whether the applicant should be approved for credit, and the prediction result is displayed to the user in real time.
The application provides immediate feedback based on the model's prediction, such as "Approved" or "Not Approved." It also includes error handling mechanisms that inform the user of any data input errors or issues with the model’s processing steps.
The application is designed to be deployed on servers or cloud platforms, making it accessible to users via web browsers without the need for local software installations.
Technology Stack: The model was implemented using Python, scikit-learn, and Jupyter Notebook. The application was developed with Python’s Dash framework and leverages HTML and CSS for frontend styling. Deployment: The application is deployed on a local server for demonstration purposes but can be scaled and hosted on cloud platforms for real-time use in production environments.
During the development process, challenges such as handling missing data, model overfitting, and integrating the model with the web application were addressed. Solutions included implementing data imputation strategies, using cross-validation for model tuning, and employing Pickle for model serialization and deserialization.
The credit lending prediction system demonstrates the potential of machine learning in financial decision-making. Future enhancements could include the integration of more complex algorithms, improvement of user interface aesthetics, and implementation of additional features based on user feedback and testing. I also plan on dockerizing and deploying this through AWS.
Documentation and libraries for scikit-learn, Dash, and Jupyter Notebook.
Previous research and methodologies in credit scoring and financial prediction models.
Avery Evans. "Application Data." Kaggle, https://www.kaggle.com/datasets/caesarmario/application-data/data.