Comparison and Optimization on ML Models for Loan Application Prediction

Deep Dive into Common Machine Learning Models for Model Selection, Validation, and Optimization

Luke Sun
Towards Data Science
8 min readJul 3, 2020

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Img from Pixabay via link

In this article, we will elaborate on ML model selection, validation, and optimization using online loan application data. What you will learn is how to create, evaluate, and optimize ML models. Specifically, we will focus on Logistic Regression, Support Vector Machine, and Random Forest. It is split into 7 parts.

  1. Business challenge
  2. Data review
  3. EDA
  4. Data processing
  5. Model building
  6. Model validation
  7. Parameter tuning
  8. Takeaways

Now, let’s begin the journey 🏃‍♀️🏃‍♂️.

1. Business challenge

We are tasked by a loan lending company to predict quality applicants. The job is to develop a model to predict the interest of applicants, by analyzing applicants’ data entered during the application process. If the applicant is interested, he or she will e-sign the product, otherwise not.

2. Data review

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ML Enthusiast, Data Scientist, Python Developer. Love to share articles about technology.