Loan Default Prediction

Every bank makes a tough call, every single day. Someone applies for a loan. The bank has minutes to decide: repay, or default? Get it wrong too often, and money walks out the door. Get it right, and everyone wins.

That’s the real-world problem behind today’s mini project. In Episode 68 of the Intelevo Machine Learning Series on YouTube, we build a complete Loan Default Prediction system, from raw applicant data to a working prediction. This article follows the video step by step. So, grab your notes, and let’s get started.

You can watch the full walkthrough here: Watch Episode 68 on YouTube. Below, you’ll find the same journey in writing, along with every code snippet from the video.

Why Loan Default Prediction Matters

Picture a loan officer named Priya. A new applicant, Alex, walks in asking for a loan. Priya has a stack of numbers in front of her: income, credit history, existing debt. Her job is simple to describe, but hard to do well. She must decide, yes or no.

Here’s the catch. Ask five different officers the same question, and you might get five different answers. Human judgment varies. Gut feelings differ. And every wrong call carries a cost. A missed default drains money straight from the bank. A wrongly rejected applicant walks away, and takes their business elsewhere.

Multiply that single decision by millions of applications a year, and you see why banks lean on data. This is exactly where machine learning steps in. Instead of one officer’s opinion, we let a model learn from thousands of past decisions. As a result, predictions become consistent, fast, and grounded in real evidence.

A Quick Recap Before We Begin

This project builds on everything we’ve covered in the series so far. So, let’s recap quickly.

Earlier episodes introduced individual models, like Logistic Regression, Decision Trees, and k-Nearest Neighbors. Each one forms its own single opinion about the data. Individually, they work well. But they also have blind spots.

Later episodes solved that problem with ensembles. Bagging, boosting, voting, and stacking all combine multiple opinions into one stronger answer. In other words, many imperfect models can outperform a single, “perfect” one.

Today, we don’t add new theory. Instead, we apply everything at once, on one real dataset, from start to finish. That’s what makes this a true mini project, rather than another lesson.

The Goal, In One Sentence

Let’s keep this simple. Here’s our entire mission, in a single sentence:

Given an applicant’s data, predict whether they will default on the loan — Yes or No.

That’s it. No extra complexity. No hidden steps. Just one clear, binary decision.

Why does this matter so much? Because banks approve millions of loans every year. Every wrong call either loses money on a default, or loses a good customer through an unfair rejection. Getting this decision right protects both the business and the people it serves.

Our approach falls under supervised learning, specifically binary classification. We already know how thousands of past applicants turned out. So, the model learns directly from that history, then applies those lessons to brand-new applicants.

Meet The Dataset

Before we train anything, we need data. Our dataset uses six simple signals to describe each applicant.

Income — the applicant’s annual earnings
Credit Score — their track record of repaying debt
Loan Amount — how much they want to borrow
Employment Years — years spent at their current job
Existing Debt — other loans they already owe
Age — the applicant’s age

Then comes the most important column of all: Default, marked simply as Yes or No. This is our target. It tells us what actually happened to each past applicant. Every row represents one real decision, and one real outcome.

Once the model studies enough of these rows, it starts to notice patterns. Low credit score plus high existing debt, for example, often points toward risk. The model doesn’t guess this randomly. It learns it directly from the data.

From Raw Data To Ready Data

Raw data is never ready to feed straight into a model. Instead, we clean it up first, using five steps you’ll recognize from earlier episodes in this series.

Clean — fix or remove missing values.
Encode — turn text categories into numbers the model can understand.
Scale — make sure one feature, like income in the lakhs, doesn’t overpower another, like employment years.
Split — divide the data into training and testing portions.
Ready — now, and only now, is the data ready to train a model.

Nothing here is new. We’ve used these exact steps before. Today, we simply apply them to a fresh, real-world dataset.

Practicing Before The Exam

Before we jump into code, let’s talk about one crucial idea: the train-test split.

Imagine studying for an exam using only the exact questions that will appear on the test. You’d score perfectly. But you’d also learn nothing useful. A fair test always uses different questions than the ones you practiced on.

The same logic applies here. We hold back part of our data, and hide it from the model until after it finishes learning. Typically, eighty percent trains the model, while the remaining twenty percent tests it afterward.

Here’s how that looks in code:

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv("loan_data.csv")
X = data.drop("default", axis=1)
y = data["default"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42)

Let’s break this down, line by line.

First, we import pandas, along with the train_test_split function from scikit-learn. Next, we load our file, loan_data.csv, into a table called data. Then, we separate it into two parts: X, which holds every feature except the target, and y, which holds only the default column, our answer key.

Finally, we call train_test_split, passing in X and y, along with test_size=0.2 and random_state=42. The test size controls our eighty-twenty split. The random state simply keeps that split identical every time we re-run the code. This single line gives us four ready-to-use pieces: X_train, X_test, y_train, and y_test.

Picking Our Model

With clean, split data in hand, it’s time to choose a model. We have two familiar options.

Logistic Regression draws a smooth boundary through the data, and outputs a probability of default. Decision Trees, on the other hand, split applicants using simple yes-or-no rules, almost like a flowchart.

Rather than pick just one, we already know a better trick from Episode 67: combine them through voting. This way, we get the strengths of both models, without fully depending on either one alone.

Here’s the only formula you really need for this entire project:

P(default) = 1 / (1 + e^-z)

If that probability lands above 0.5, we predict default. Otherwise, we predict no default. Don’t worry about computing this by hand, though. scikit-learn handles the math for you, quietly, behind the scenes.

Training And Predicting, In Code

Now, let’s bring everything together. We reuse our ensemble tools from Episode 67, and apply them here.

from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier

model = VotingClassifier(estimators=[
    ("lr", LogisticRegression()),
    ("dt", DecisionTreeClassifier())
], voting="soft")

model.fit(X_train, y_train)
predictions = model.predict(X_test)

Let’s walk through this too.

First, we import VotingClassifier, along with LogisticRegression and DecisionTreeClassifier. Next, we build our model as a VotingClassifier, and pass in a list of two named estimators: "lr" for Logistic Regression, and "dt" for Decision Tree. We also set voting="soft", which means the model averages predicted probabilities, rather than simply counting votes.

Then comes training, which takes exactly one line: model.fit(X_train, y_train). This single call lets both models learn the patterns behind past defaults. Finally, we generate predictions with model.predict(X_test). This returns a Yes or No for every applicant in our test set.

That’s the entire training process. Five working lines of code, and our model is ready to make decisions.

How Good Is Our Model, Really?

Predictions alone don’t tell the full story. So, next, we need to measure how well our model actually performs.

Enter the confusion matrix: a simple two-by-two grid that compares our predictions against reality. Two of the four boxes mean we got it right.

True Negative — we correctly said “No.”
True Positive — we correctly said “Yes.”

The other two boxes mean we got it wrong.

False Positive — a false alarm, where we predicted risky, but the applicant was fine.
False Negative — a missed risk, where we predicted safe, but the applicant defaulted anyway.

From these four numbers, we calculate accuracy, using one straightforward formula:

Accuracy = Correct predictions / Total predictions

Four numbers, one grid. That’s the entire scorecard for any classifier, and it works for far more than just loans.

Not All Mistakes Cost The Same

Here’s a detail that many beginners overlook. Not every mistake costs a bank the same amount.

A false negative is the costly one. Here, the model says “safe,” but the applicant defaults anyway. As a result, the bank hands out money it never gets back.

A false positive, on the other hand, is the safer mistake. Here, the model says “risky,” but the applicant was actually fine. This only costs a missed customer, not a lost loan.

So, banks often care more about catching defaulters, a measure called recall, than they do about raw accuracy alone. In short, understanding your errors matters just as much as counting them.

Three Ways To Make It Even Better

Before we wrap up, let’s look at three quick upgrades for anyone who wants to push this project further.

Balance the classes. Defaults are naturally rare in real-world data. Setting class_weight="balanced" stops the model from lazily guessing “No” every single time.
Tune the threshold. The value 0.5 isn’t sacred. Lowering it helps the model catch more risky applicants, though it also raises the chance of a few extra false alarms.
Check feature importance. Find out which signal, income or credit score or something else, really drives the model’s decisions. This builds genuine trust in what the model is doing, and why.

Each of these tweaks takes only a few extra lines of code. Yet, together, they turn a decent model into a genuinely reliable one.

It Really Is This Simple

Let’s bring everything together, one more time.

Data goes in, and past defaulters, labeled and ready, come out. That’s supervised learning. Next, we split the data fairly, so our model gets tested on questions it has genuinely never seen. Then, we fit it, in one single line of scikit-learn. Finally, we score it, using a confusion matrix, not accuracy alone.

If you can explain your model’s decision in one plain sentence, then you’ve truly understood it. And really, that’s the entire point of this mini project.

Watch The Full Walkthrough

Reading through code helps, but watching it run helps even more. In Episode 68 of the Intelevo Machine Learning Series, we build this Loan Default Prediction project live, step by step, on screen.

Watch Episode 68 on YouTube

If this article helped you, please consider liking the video, subscribing to the channel, and leaving a comment with your thoughts or questions. Your feedback genuinely shapes what we build next.

What’s Next: Unsupervised Learning

Every model in this series, so far, has relied on an answer key. In Episode 69, we remove that safety net completely, and introduce Unsupervised Learning. Without labels to guide it, how does a model still find meaningful patterns, all on its own? We answer that question next.

Until then, keep practicing, keep experimenting, and remember: even the most powerful models start with one simple, well-explained idea.

This article accompanies Episode 68 of the Intelevo Machine Learning Series on YouTube. For more tutorials, code walkthroughs, and mini projects, explore the rest of the series at intuitivetutorial.com.

Loan Default Prediction: A Beginner-Friendly Machine Learning Mini Project