Скачать или смотреть ML Model for Loan Approval: Predicting Client Default with Excel | FinTech Assignment Demo

In this short demo, we walk through how to train a machine learning model to predict whether a client should be accepted or rejected for a loan based on demographic and financial indicators. Using Excel and a simple classification algorithm, we estimate the probability of default based on features like income, age, and credit score.

✅ Key steps covered:

Data cleaning and setup

Training a binary classification model

Calculating and interpreting default probabilities

Making accept/reject decisions based on thresholds

📊 Dataset features:

Income

Age

Credit Score

Employment & Marital Status

Education

Previous Default History

🎯 Assignment Task:
Replicate the workflow, train your own model, and record a 4–6 minute video explaining your process and decision logic.


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