In this step-by-step Dev.to tutorial, we’ll build LoanGuardian v2.1, an AI-powered loan approval prediction system using Python.

This guide is beginner-friendly — each section is broken into small code blocks with clear explanations, so you can follow along even if you’re new to machine learning or desktop apps.

🔍 What We’re Building

By the end of this tutorial, you’ll have:

A desktop GUI app built with Tkinter

An ML model (RandomForest) that predicts loan approval

Automatic handling of large CSV files

Feature detection for categorical & numeric data

A clean exportable prediction workflow

📦 GitHub repo: https://github.com/rogers-cyber/python-tiny-tools/tree/main/Loan-approval-prediction-system

🛠️ Requirements

Install the required libraries:

pip install pandas numpy scikit-learn ttkbootstrap tkinterdnd2 📁 Project Structure LoanGuardian/ ├── main.py └── requirements.txt

Everything will live inside main.py for simplicity.

🧠 Step 1: Import Required Libraries

We start by importing Python standard libraries, GUI tools, and machine learning packages.

import os, sys, threading
import tkinter as tk
from tkinter import filedialog, messagebox, ttk

These handle:

File selection

UI dialogs

Background processing (to keep the app responsive)

Now the data and ML libraries:

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder, MinMaxScaler

🎨 Step 2: Improve the UI with ttkbootstrap

ttkbootstrap gives us modern themes with minimal effort.

import ttkbootstrap as tb
from ttkbootstrap.constants import *

Optional drag-and-drop support:

try:
from tkinterdnd2 import TkinterDnD, DND_FILES
DND_ENABLED = True
except ImportError:
DND_ENABLED = False

🧩 Step 3: Create the Machine Learning Worker

We separate ML logic from the UI using a worker class.

class LoanWorker:
def __init__(self, files, callbacks, chunk_size=500):
self.files = files
self.callbacks = callbacks
self.chunk_size = chunk_size

Why?

Keeps UI clean

Allows background processing

Scales to large datasets

📊 Step 4: Train a Sample Loan Model

For demo purposes, we train on a small dataset.

data = pd.DataFrame({
"Gender": ["Male","Female","Female","Male"],
"ApplicantIncome": [5000,3000,4000,6000],
"LoanAmount": [200,100,150,250],
"Loan_Approved": ["Yes","No","Yes","Yes"]
})

Split features and labels:

X = data.drop("Loan_Approved", axis=1)
y = data["Loan_Approved"]

🔤 Step 5: Encode Categorical Features

Machine learning models require numbers — not text.

le = LabelEncoder()
X["Gender"] = le.fit_transform(X["Gender"])

We store encoders so we can reuse them when new CSV files are loaded.

📐 Step 6: Normalize Numeric Data

Normalization keeps values within a predictable range.

scaler = MinMaxScaler()
X[["ApplicantIncome", "LoanAmount"]] = scaler.fit_transform(
X[["ApplicantIncome", "LoanAmount"]]
)

🌲 Step 7: Train the RandomForest Model

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X, y)

Why RandomForest?

Beginner-friendly

Handles mixed data types

Strong baseline accuracy

📂 Step 8: Process Large CSV Files in Chunks

Instead of loading everything at once:

for chunk in pd.read_csv(path, chunksize=500):
processed = self._preprocess_chunk(chunk)

This makes the app memory-efficient and fast.

🖥️ Step 9: Build the GUI

Create the main window:

self.root = tb.Window(themename="darkly")
self.root.title("LoanGuardian v2.1")

Add buttons and file input:

tb.Button(text="Start Prediction", command=self.start)
tb.Button(text="Export Results", command=self.export_results)

📈 Step 10: Show Progress Feedback

We animate progress to improve UX.

self.progress = tb.Progressbar(maximum=100)
self.progress["value"] = percent

💾 Step 11: Export Prediction Results

with open(path, "w") as f:
for file, status in results:
f.write(f"{file} | {status}\n")

This allows easy reporting or auditing.

🚀 Final Result

You now have:

✅ A production-style ML workflow ✅ Desktop GUI ✅ CSV batch processing ✅ AI-powered predictions

🔗 Full Source Code

Clone the project:

git clone https://github.com/rogers-cyber/python-tiny-tools.git

Navigate to:

Loan-approval-prediction-system/ 🙌 What’s Next?

Replace dummy data with real datasets

Save trained models with joblib

Add probability scores

Convert to .exe using PyInstaller

Happy coding! 🚀