LoanSense: A predictive analysis application for loan defaults

Abstract

LoanSense is a machine learning-based application designed and developed in Python to predict and manage loan defaults. The app uses a logistic regression model to analyze customer data and provide risk assessments for loan approvals.

I Developed this application as part of my role at Hexaware Technologies, where I integrated advanced data analysis and ensured a user-friendly interface design to streamline loan risk evaluation processes.

Part 1: Project Overview

Introduction

I designed a user-friendly app for predicting loan defaults using a logistic regression model. By conducting thorough UX research and analysis, I created an interface using the Streamlit library in Python, achieving a 95% prediction accuracy. This project demonstrates my ability to integrate data science, programming and effective UX design.

The Problem

Instalend, a loan company, faced challenges in managing loan formalities efficiently and accurately. The task was to develop a machine learning model and integrating it with a user-friendly application. The application transforms data predictions into a simple, user interface, making it accessible for non-technical users to automate and enhance the loan approval process seamlessly.

The Solution

To address the issue of managing large datasets and automating the loan approval process, I developed a logistic regression model on Python and integrated it with a user-friendly application which I developed using the Streamlit library in Python for ease of access for both technical and non-technical users.

My role

My role at Hexaware Technologies was a Software Developer, which involved conducting a thorough statistical analysis and data cleaning, then training the model in Python. I then integrated this model with the user-friendly application I developed.

Disclaimer:

Due to a non-disclosure agreement (NDA) with Hexaware Technologies, I am unable to provide access to the application. However, I have documented the detailed process and methodology I employed in developing the app, which highlights my approach, techniques, and the steps taken to create a functional and user-friendly solution for predicting loan defaults and have attached a recording of the application usage.

Part 2: Developing the model

Data Collection

The dataset was obtained from Instalend, a loan company, which contained detailed records of loan applicants. This comprehensive dataset included almost one million entries and encompassed various attributes such as customer ID, loan amount, loan duration, interest rate, annual income, employment duration, and other pertinent features. The data collection process ensured that all relevant variables that could influence loan default decisions were included, facilitating a robust analysis and model building.

Data Analysis

the dataset was loaded into a Python environment using libraries such as Pandas and NumPy for data manipulation and analysis. Visualizations were created using Matplotlib to identify key trends and anomalies in the data. This process involved univariate analysis to summarize the distribution of each variable and bivariate analysis to explore the relationships between different pairs of variables. Correlation matrices and heatmaps were employed to highlight the relationships between features, particularly how they relate to loan defaults. The analysis also included data cleaning to handle missing values and outliers, ensuring that the data used for modeling was accurate and reliable.

Feature selection

My goal was to identify and retain only the features that contribute to predicting loan defaults while eliminating irrelevant or redundant variables. The selection process began with a statistical analysis of each feature's relationship with the target variable (loan default status). Techniques such as correlation analysis and feature importance ranking were used to evaluate each feature's predictive power. Features like state and loan application year, which showed little to no correlation with loan default status, were discarded.

Model development

Logistic regression was chosen as the primary algorithm due to its suitability for binary classification tasks. The model was trained using the refined dataset, where features were transformed and scaled to optimize performance. The dataset was divided in the ratio 80:20, where 80% of the data was used for training the dataset and 20% of the data was used to test the trained model. The logistic regression model was then trained and tested, achieving an impressive accuracy of 96%. This high accuracy indicates the model's effectiveness in distinguishing between loan applicants who are likely to default and those who are not.

StreamLit

Streamlit is a software company offering an open-source platform for machine learning and data science teams to create data applications with Python. The platform uses python scripting, APIs, widgets, instant deployment, team collaboration tools, and application management solutions Applications created using Streamlit range from applications capable of real time object detection, geographic data browsers, deep dream network debuggers, to face-GAN explorers.

Part 3: Designing an interactive application

Need for an application

The trained model provided a 96% accurate output. However, it was not easy to input new data as the entire code required to be edited to predict for a new dataset. To help overcome this, I designed a simple UI using the streamlit library in Python by integrating the trained model with UI elements to make the prediction process smooth and easy for both technical and non-technical users.

Developing the Application

Note

For privacy reasons, sensitive data appears in the "Show data" page and has not been shown in this video. The functionality showcased remains accurate and representative of the application's capabilities.

Part 4: Outcomes and Learnings

Outcomes

The project resulted in the successful development of a user-centric loan default prediction application, which was designed using Streamlit, provided a streamlined and efficient workflow for loan officers, allowing them to input data and obtain risk predictions quickly and accurately. This significantly enhanced the loan approval processes by reducing manual effort and improving decision-making speed. The application’s design focused on user experience, ensuring ease of navigation and clarity of information, which minimized risks for the company.

Key Takeaways

Through this project, I gained significant insights in both machine learning and user experience design. I understood the importance of data visualization to access complex data and integrating user-friendly design for the efficient usage of the application. Overall, I understood that designing for real-world applications requires balancing technical complexity with user accessibility.

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