🚀 Model Comparison for Loan Classification

4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.

🔍 The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.

Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! 💻
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6

Debugging and Troubleshooting in Python: A Developer’s Essential Guide
Debugging and troubleshooting are essential skills for any Python developer. While these tasks can be frustrating, they are a necessary part of the software development process. Proper debugging helps developers identify the root cause of issues and ensures smoother project delivery.

In this article, you will explore common debugging challenges, essential techniques, and how you can improve your debugging efficiency with Python. Whether you’re a beginner or an experienced developer, mastering debugging techniques will save you countless hours of frustration.

https://medium.com/@epythonlab/debugging-and-troubleshooting-in-python-a-developers-essential-guide-b3415f53b1e0

Understanding what kind of Data you should store in computer memory
https://youtu.be/1UN_iU4UGho

Python for Beginners | How to Code in Python | How to Store and Access Data with Variables in Python
https://youtu.be/yeRbfdvfWnU

Do you know how Python Executes your code? https://www.youtube.com/watch?v=az-7vPbfGYc

Python for Beginners | How to Work with Strings in Python | Create, Combine, Repeat, Store
https://youtu.be/vEhUfeT1ar4

🎯 Want to break into FinTech with Python and machine learning?

I just launched the FinTech ML Labs video series — a practical guide to building real-world financial systems using Python and modern ML libraries.

📌 Episode 1 is live:
"Build FinTech Machine Learning Projects with Python: Intro to FinTech ML"

Inside this episode:

What FinTech ML really is (and why it's in demand)

5 real-world ML applications: fraud detection, credit scoring, trading bots & more

How companies like Stripe, PayPal, and Robinhood use ML at scale

Tools we’ll use: Python, scikit-learn, XGBoost, spaCy, Hugging Face Transformers

💡 Every episode includes code, datasets, and walkthroughs so you can follow along.

🔗 Watch now: https://youtu.be/dy87uyYQWrg

If you’re a developer looking to build applied ML skills or transition into FinTech, this series is for you.

Let’s build real systems — not just toy models.

How to format Text in Python
https://youtu.be/Qs5Jtaxl7Lc

🚀 New Tutorial: Build a Credit Scoring Model in Python

🎯 Real-World FinTech Machine Learning Project – Episode 2: Watch the full tutorial here https://youtu.be/pWOoYpJsaDc


I have published a practical tutorial that demonstrates how to build a credit scoring model using Python, pandas, and scikit-learn. This project simulates a real-life use case from the fintech industry, focusing on predicting loan defaults based on applicant data.

📌 What you will learn:

Data cleaning and preprocessing for financial datasets

Logistic Regression for binary classification

Feature scaling and performance metrics (Precision, Recall, F1 Score)

Visualizing feature importance for interpretability

📊 Why this matters:

Credit scoring is a core component in lending, digital banking, and microfinance. Understanding how to implement this model can open doors in risk analytics, credit platforms, and fintech applications.



🔗 GitHub code and dataset are also available in the video description.


If you are building a career in data science, machine learning, or fintech, this project will give you strong, applicable experience.

String methods in Python: A comprehensive tutorial for beginners
https://youtu.be/9gniK8C6va0

How do you interpret the insights of the loan dataset distribution plot

Github https://github.com/epythonlab.com2/fintech-ml-labs/blob/main/notebooks%2Fcredit_scoring_model.ipynb😃

How to Index and Slicing Strings: A comprehensive Beginners Tutorial
https://www.youtube.com/watch?v=K-488Zr3Fe0

ETL Process Pipeline with Python: https://youtu.be/3J1D33US7NM

Test ETL Pipeline: https://youtu.be/78x6V5q34qs

🚀 Launching: ML for FinTech Projects – Real-World Implementations for ML Enthusiasts

I am excited to launch a practical, hands-on series dedicated to Machine Learning in FinTech. This initiative is designed for ML enthusiasts and professionals eager to explore real-world implementations of machine learning in financial systems.

In this series, you will learn step-by-step how to build and deploy FinTech solutions, including:

✅ Credit Scoring Models https://youtu.be/pWOoYpJsaDc
✅ Fraud Detection Systems
✅ Loan Default Predictions https://youtu.be/pWOoYpJsaDc
✅ Customer Segmentation
✅ Transaction Risk Analysis
...and much more.

Each episode will include:
🔹 Clear explanations of ML techniques in a FinTech context
🔹 Real datasets and coding walkthroughs
🔹 End-to-end project structure from data prep to model deployment

Stay tuned, subscribe, and get ready to build solutions that make a real impact.

Avoid Type Error Master Python Data Type Conversion FAST | Type Conversion Tutorial
https://youtu.be/ovmjYmU8Jrc

➡️ Beginner's Guide to Python Programming:  https://youtube.com/playlist?list=PL0nX4ZoMtjYGSy-rn7-JKt0XMwKBpxyoE&si=N8rHxnIYnZvF-WBz

This tutorial is designed for absolute beginners, with no prior experience required. Learn the basics, build real projects, and confidently grow your skills.

🔔 Subscribe for more learning resources and updates!

🚨 Fraud Isn’t Just a Risk—It’s a Reality. Here’s How We’re Fighting Back with ML in Fintech. 💡https://youtu.be/kQHpXSH4G_E

In the fast-moving world of fintech, trust is currency. And nothing erodes trust faster than fraud.

Recently, I took a deep dive into building a fraud detection engine using classification algorithms in Python—but not just with the traditional plug-and-play mindset.

Instead of asking “Which model performs best?”, I asked: 🔍 How can we build a system that understands fraud like a human analyst would—but at scale and in real time?

📊 Here's the approach:

1. Behavioral Pattern Recognition: Mapped transaction flows to user behavior signatures, not just features. Outliers aren’t always fraud—but often they are.


2. Hybrid Classification Stack: Instead of relying on one algorithm (e.g., Random Forest or Logistic Regression), I built a layered model that integrates explainable models with high-performance black-box learners.


3. Anomaly-Aware Sampling: Balanced class imbalance with strategic undersampling, but retained edge-case patterns using synthetic minority over-sampling (SMOTE with domain tweaks).


4. Real-World Feedback Loop: Built an active learning system that retrains from confirmed fraud cases—turning human analysts into model trainers.



🧠 The result? A system that doesn’t just flag suspicious activity—but learns from every incident.

🎯 Tools used:

Python, Scikit-learn, XGBoost

Pandas, Seaborn (for EDA)

SHAP (for interpretability)

Flask + Streamlit for dashboarding


💬 Fintech peers: How are you balancing accuracy vs explainability in fraud detection models?

Let’s connect if you’re working on ML in fintech—especially in risk, fraud, or anomaly detection. Happy to exchange ideas and build smarter, safer systems together. 🔐📈

#Fintech #MachineLearning #FraudDetection #Python #AI #Classification #DataScience #XAI #MLinFinance #CyberSecurity

💰 Machine Learning is Reshaping Fintech — and we're just getting started.
FinTech ML Labs: https://www.youtube.com/playlist?list=PL0nX4ZoMtjYFuTnUcwv0aFnxN9pEyjVez

Two of the most mission-critical areas where ML is making a real-world impact today are:

1. 🔎 Credit Scoring

Traditional credit scoring often overlooks those without a deep financial history. With ML:

We analyze alternative data (e.g., transaction patterns, mobile usage, utility payments)

Apply classification algorithms to predict creditworthiness

Enable inclusive lending for underbanked populations


✅ Outcome: More accurate risk assessment + financial inclusion.


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2. 🛡️ Fraud Detection

Fraudsters evolve fast. ML evolves faster.

We train models on millions of transactions, identifying subtle anomalies

Use a mix of real-time classification, unsupervised anomaly detection, and behavioral modeling

Continuously improve through feedback loops and active learning


🚨 ML helps flag suspicious activity before it turns into loss.


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🔧 Tech Stack: Python | Scikit-learn | XGBoost | SHAP | FastAPI | Streamlit | AWS

🔄 The future of fintech is predictive, not reactive.

If you’re building intelligent financial systems—whether it’s for lending, fraud prevention, or personalization—let’s connect and exchange notes. 🚀

#Fintech #MachineLearning #CreditScoring #FraudDetection #ArtificialIntelligence #DataScience #FinancialInclusion #ResponsibleAI #Python #MLinFinance

How to use f-strings
https://youtu.be/eLuqL4w6sBE

🚨 New Video Alert: Predicting Customer Churn with Machine Learning 🚨
https://youtu.be/da_xqw1oAD8
Churn is one of the biggest silent killers for subscription-based businesses. In this new tutorial, I break down how to predict customer churn using real-world data and three powerful models:

🔍 Logistic Regression
🌲 Random Forest
⚡️ XGBoost

We explore:
✅ Data exploration & preprocessing
✅ Handling class imbalance
✅ Building scalable ML pipelines
✅ Model evaluation using F1-score, precision, and recall
✅ Hyperparameter tuning with GridSearchCV
✅ Professional tips to improve churn detection accuracy