Title: Interactive Loan Data Analysis in the Browser with TensorFlow.js
Overview
In today’s data-driven world, making insights accessible and interactive is crucial for understanding complex datasets. For this project, I created an in-browser loan data analysis tool using TensorFlow.js to offer real-time data insights on loan data directly from the browser, without the need for backend processing. This allows users to upload a CSV, analyze loan-related metrics, and even train a machine learning model on the fly, all in the comfort of their browser.
In this post, I’ll share the main components of the project, from setup to deployment, and discuss how various technologies were integrated to deliver a responsive and user-friendly experience.
Project Goals
This project aimed to:
- Enable users to upload a CSV of loan data for instant analysis.
- Provide built-in question prompts for quick insights into loan data, such as average loan amount and risk levels.
- Allow users to ask their own questions, which the tool attempts to answer by parsing key phrases.
- Train a simple neural network model to identify high-risk loans using TensorFlow.js.
Project Structure
The core of the project consists of three main files:
- index.html: Defines the structure and layout, with Bootstrap for styling and the ability to upload CSV files.
- loanAnalyzer.js: Contains JavaScript for handling file uploads, data parsing, and question answering. TensorFlow.js is used here to build and train a simple neural network model.
- CSV Generator.py: A supplementary file that assists with generating sample CSV data if users want to test the tool without their own dataset.
HTML Structure (index.html)
The HTML layout is designed to guide users through the process. It includes:
- File Upload Section: Users can upload a CSV file, which is parsed using PapaParse, a JavaScript library for handling CSV data in the browser.
- Canned Questions Section: Predefined buttons provide quick answers to common questions like “What’s the average loan amount?” and “How many high-risk loans are there?”.
- Question Input Section: This section allows users to type custom questions to query the data. Based on keyword matching, the tool generates insights.
- Results Section: Displays analysis results, including a summary of loan risk predictions and other insights based on the uploaded data.
JavaScript and TensorFlow.js Logic (loanAnalyzer.js)
In loanAnalyzer.js, the main functions handle:
- Data Parsing and Preprocessing: Once a CSV is uploaded, PapaParse converts it into JSON format for easier processing.
- Question Answering: Users can type questions into the tool, which maps keywords in their question to corresponding functions. For example, “average loan amount” triggers a calculation of the mean loan amount from the dataset.
- Model Training and Prediction: Using TensorFlow.js, a basic neural network is trained on the uploaded data to identify high-risk loans. The model uses loan features like amount, interest rate, income, and credit score to predict risk level.
Here’s a snapshot of some of the core functionality:
The model architecture is simple, yet effective for binary classification of high-risk vs. low-risk loans. Once trained, it predicts each loan’s risk level, which is then displayed in the Results Section.
Challenges and Solutions
Data Parsing in JavaScript: Parsing and handling large CSV files in the browser can be challenging. Using PapaParse helped streamline this process, offering fast parsing with minimal code.
Training Model in Browser: One limitation of in-browser training is processing power. TensorFlow.js, however, makes the process smoother by leveraging WebGL for hardware acceleration, making it feasible to run smaller models directly in the browser.
Dynamic Question Answering: Mapping user questions to predefined answers required careful design to ensure accuracy. By keyword-mapping each question to a function, the tool provides responsive feedback to common loan analysis questions.
Try It Out
Check out the GitHub repository for the code, and feel free to fork it and customize it to fit other data analysis needs.
GitHub Repository: GitHub – In-Browser Loan Data Analyzer (replace yourusername with your actual GitHub username).
Conclusion
This project demonstrates the power of in-browser data processing and machine learning. With TensorFlow.js, we can make predictive models accessible and interactive for everyday users, helping them gain insights from their data without any backend requirements.