💳 Credit Card Default Prediction Using Classification ML (Part-3)💳

The Crystal Ball for Credit: How AI Forecasts Default

Fortifying Finances: AI-Powered Default Prevention

The Smart Way to Lend: Predicting Default with Artificial Intelligence.

Cracking the Code: AI Predicts Credit Card Default

From Data to Default: An AI Prediction Journey

End-to-End Machine Learning Project Blog (Part-3)

🎉 Welcome Back, Financial Data Warriors! 💼🧠 It’s Time for Part 3 The Modeling Begins!

Hey everyone 👋👋 whether you've been with us since the first line of code or just joined the mission now, welcome to Part 3 of our Credit Card Default Prediction Using Classification Machine Learning blog series!

In Part 1 , we laid the foundation by:

Loading a powerful dataset containing 30,000 customer entries .
Encoding categorical features like SEX, EDUCATION, and MARRIAGE.
Cleaning up inconsistencies in education and marital status fields.

Then in Part 2 , we dove into Exploratory Data Analysis (EDA) and uncovered some fascinating insights:

The dataset is imbalanced , with only 22.1% defaulters .
Payment history (PAY_0 to PAY_6) shows strong patterns in default behavior.
Credit limit (LIMIT_BAL) isn’t a strong predictor alone but plays well with other features.
Certain demographics (like singles and those with unknown education) are more likely to default.

Now it’s time to bring everything together and enter the most exciting part of any data science project:

🎯 What’s Coming in Part 3 ?

This is where theory meets practice and your AI brain starts making predictions:

📊 Advanced EDA & Feature Engineering

We’ll explore how bill amounts, payment trends, and delays influence default risk and create new features that help machines understand financial behavior better.

🧠 Model Selection & Training

You’ll train multiple classification models:

Logistic Regression
Random Forest Classifier
XGBoost & LightGBM
CatBoost & Gradient Boosting Trees

And yes we’ll compare which one performs best on this real-world financial dataset.

📈 Performance Evaluation

Once trained, we’ll test each model using key metrics:

Accuracy
Precision
Recall
F1-score
ROC-AUC Curve

This will help us pick the best-performing model not just based on accuracy, but also on its ability to catch defaulters early.

🔍 Confusion Matrix & SHAP Interpretation

We’ll visualize misclassifications and explain why certain customers were flagged as high-risk using SHAP values turning your model into an interpretable AI system .

💥 Why You’ll Love This Part

You're about to build something truly powerful:

An AI-powered credit default detection system that banks, fintech startups, and risk departments can use.
A full classification pipeline from preprocessing to prediction and interpretation.
A portfolio-worthy project that shows off your ability to work with financial data, classification modeling, and Explainable AI .

Whether you're doing this for fun, for your portfolio, or for career growth this part is pure gold. 💥

🙌 Thoughts?

You’ve already done the heavy lifting from loading text data and encoding categories to cleaning edge cases and performing deep EDA.

Now it’s time to finish strong by training powerful classifiers and understanding what drives defaulter predictions.

So grab your notebook, fire up your Python environment, and let’s dive into Part 3: Model Training & Advanced Evaluation ! 🚀💻

Let’s go💪🔥📊

Unveiling Feature Relationships Correlation Analysis for Credit Card Default Prediction

In our last step, we explored how LIMIT_BAL (credit limit) varies across defaulters and non-defaulters using a histogram.

Now it’s time to dive even deeper into understanding the relationships between different features in the dataset by performing correlation analysis .

In this step:

We’ll calculate the correlation matrix using df.corr().
Visualize correlations using a heatmap to identify which features are strongly related.
Gain insights into how different variables interact with each other especially in relation to the target variable (default).

This is where machine learning meets feature engineering. Let's uncover hidden patterns that drive credit card default behavior!

Why Does It Matter?

Correlation analysis matters because:

Feature Relationships : Helps you understand which variables are most predictive of defaults.
Redundancy Detection : Identifies highly correlated features that might cause multicollinearity.
Model Performance : Guides decisions about feature selection or preprocessing steps.

By running these diagnostics, you ensure your AI system is built on solid, meaningful features, not just random inputs.

What to Expect in This Step

In this step, you'll:

Learn how to compute the correlation matrix using df.corr().
Use sns.heatmap() to visualize correlations visually.
Understand which features are strongly correlated with the target variable (default).
Get ready to refine preprocessing and modeling based on these insights.

This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees.

Fun Fact

Did you know?

The credit card default prediction dataset contains numerical features like LIMIT_BAL, AGE, BILL_AMT1, and more.

And here’s the twist: Our correlation heatmap reveals fascinating patterns:

Certain features show strong positive or negative correlations with default.
Features like PAY_0 (payment delay) and BILL_AMT1 (bill amount) appear to have significant relationships with default behavior.

That’s exactly what we’re doing now only now, you’re the one making decisions based on real-world financial insights.

Real-Life Example Related to This Step

Imagine you're working for a fintech startup , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.

You’ve already loaded the dataset and taken a peek at its structure. Now, you want to:

Understand how different features correlate with default behavior.
Explore relationships between payment delays, bill amounts, and demographics.
Refine preprocessing steps based on initial observations.

By analyzing correlations:

You confirm that features like PAY_0 and BILL_AMT1 are strongly linked to default risk.
You identify potential areas for feature engineering, like combining payment delays or normalizing bill amounts.

These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!

Mini Quiz Time!

Let’s test your understanding of correlation analysis:

Question 1: What does df.corr() do?
a) Calculates SHAP values
b) Measures correlation between all pairs of numerical features
c) Just makes the code run faster

Question 2: Why do we use a heatmap to visualize correlations?
a) To make the code look fancy
b) To easily spot strong correlations
c) Just for fun

Drop your answers in the comments, I’m excited to hear your thoughts! 💬

Cheat Sheet


Calculate Correlations	Usedf.corr()to compute pairwise correlations	Builds intuition.
Visualize Correlations	Usesns.heatmap()to highlight strong relationships	Makes insights clear.
Interpret Heatmap	Look for high/low correlations with the target variable	Guides preprocessing.

Pro Tip for You

When interpreting correlation heatmaps:

Focus on strong correlations (close to ±1): These indicate meaningful relationships.
Check for multicollinearity : If two features are highly correlated, consider dropping one.
Look for target correlations : Identify which features are most predictive of default.

For example:

If PAY_0 shows a strong negative correlation with default, it suggests delayed payments increase default risk.
If BILL_AMT1 shows a moderate positive correlation, it indicates higher bills might lead to defaults.

What's Happening in This Code?

The code block performs the following tasks:

Calculate Correlation Matrix:

Uses corr = df.corr() to compute pairwise correlations between all numerical features.

Generate Heatmap:

Plots the correlation matrix using sns.heatmap(corr, annot=True, cbar=True, cmap='plasma').

By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.

Code

# Calculate Correlations

corr = df.corr()

# Plot Correlation Heatmap

plt.figure(figsize=(30, 20))

sns.heatmap(corr, annot=True, cbar=True, cmap='plasma')

plt.show()

Output:

Key Observations:

Strong Positive Correlations :

Features like BILL_AMT1, BILL_AMT2, ..., BILL_AMT6 show high positive correlations with each other, indicating they measure similar financial behaviors.
PAY_0, PAY_2, ..., PAY_6 also show strong correlations, suggesting consistent payment patterns over time.

Target Variable (default) :

PAY_0 shows a moderate negative correlation with default, indicating delayed payments (PAY_0 > 0) are associated with higher default risk.
BILL_AMT1 shows a moderate positive correlation , suggesting higher bills might increase default likelihood.

Multicollinearity :

Some features like BILL_AMT1 to BILL_AMT6 are highly correlated with each other, which could lead to multicollinearity issues during modeling.

Insights:

The heatmap reveals strong relationships between payment delays (PAY_0 to PAY_6) and default behavior.
Bill amounts (BILL_AMT1 to BILL_AMT6) also show meaningful correlations, highlighting their importance in predicting defaults.
These results give us confidence in moving forward with advanced preprocessing and modeling steps.

We’re officially off to a great start in building an AI-powered credit default predictor !

Insight

From this step, we can conclude:

The correlation heatmap confirms that payment delays (PAY_0 to PAY_6) and bill amounts (BILL_AMT1 to BILL_AMT6) are strongly correlated with default behavior.
Features like SEX, EDUCATION, and MARRIAGE show weaker correlations, suggesting they might play secondary roles.
These insights provide a solid foundation for refining preprocessing and deploying a transparent, interpretable AI system .

We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems .

Potential Next Steps and Suggestions

Handling Multicollinearity : Address highly correlated features like BILL_AMT1 to BILL_AMT6.
Advanced Feature Engineering : Create new features like total bill amounts or average payment delays.
Model Training : Train multiple classifiers like Logistic Regression, Random Forest, XGBoost, etc.
Advanced Evaluation : Generate ROC curves, confusion matrices, and SHAP analysis for interpretability.
Iterative Refinement : Explore ensemble methods or deep learning approaches for potential improvement.

Stay tuned for the next exciting steps in our journey to build a world-class credit card default predictor using machine learning! 🚀

Training Our AI to Predict Credit Card Defaulters Meet the Champions of Classification!

In our last step, we performed a deep dive into feature correlations using the correlation heatmap , uncovering strong relationships between payment delays (PAY_0–PAY_6) and bill amounts (BILL_AMT1–BILL_AMT6).

Now it’s time for the real magic training 9 powerful classification models to predict whether a customer will default on their credit card payments.

In this step:

We’ll split the dataset into features (x) and target (y) then apply train-test splitting.
Use StandardScaler to normalize numerical features before feeding them to models like KNN and SVM.
Train a wide range of classifiers:

Logistic Regression
Random Forest
Gradient Boosting
XGBoost
LightGBM
CatBoost
Support Vector Classifier (SVC)
KNN
Naive Bayes

And finally we’ll evaluate each model using accuracy scores to see which one reigns supreme!

This is where machine learning meets real-world financial risk assessment. Let's see how well our AI can predict defaults!

Why Does It Matter?

Training multiple classification models matters because:

Model Comparison : Helps you identify which algorithm performs best on your specific dataset.
Feature Scaling : Ensures fair performance across models sensitive to input magnitude.
Baseline Building : Gives you a starting point for advanced evaluation with metrics like precision, recall, and F1-score.

By running these diagnostics, you ensure your AI system isn't just accurate it's robust and reliable in production environments.

What to Expect in This Step

In this step, you'll:

Learn how to perform train-test splits with test_size=0.2.
Understand why feature scaling is important for certain models.
Train multiple classification algorithms and compare their accuracy side-by-side.
Get ready to refine modeling using cross-validation, hyperparameter tuning, or SHAP analysis.

This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees.

Fun Fact

Did you know?

Even though CatBoost and LightGBM scored the highest accuracy (82.1% and 81.98% , respectively), accuracy alone doesn’t tell the full story.

And here’s the twist: Some models may be better at catching actual defaulters than others which is critical when predicting financial risk.

That’s exactly what we’re doing now only now, you’re the one making decisions based on real-world financial insights.

Real-Life Example Related to This Step

Imagine you're working for a banking institution , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.

You’ve already loaded and preprocessed the data now, you want to:

Try several classification algorithms to find the best performer.
Compare accuracy across models like Logistic Regression, Random Forest, and CatBoost.
Refine the final model using advanced metrics like precision , recall , and F1-score .

By running this comparison:

You confirm that CatBoost gives the highest accuracy but also understand that Gradient Boosting and Random Forest are close behind.
Stakeholders gain confidence in deploying this AI system for real-world use cases.

These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!

Mini Quiz Time!

Let’s test your understanding of model training:

Question 1: What does StandardScaler() do?
a) Just makes the code run faster
b) Normalizes features so models like KNN and SVC work better
c) Calculates SHAP values

Question 2: Why do we compare multiple models?
a) To impress stakeholders
b) To find the best-performing model for deployment
c) Just for fun

Drop your answers in the comments, I’m excited to hear your thoughts! 💬

Cheat Sheet


Train-Test Splitting	Usetrain_test_split()to divide data	Ensures fair evaluation.
Feature Scaling	ApplyStandardScaler()	Improves performance for distance-based models.
Multiple Model Training	Train Logistic, RF, GB, XGB, LGBM, CatBoost, and more	Identifies top performers.

Pro Tip for You

When evaluating accuracy:

Always check other metrics like precision and recall especially for imbalanced datasets.
Consider class weights or sampling techniques if defaulters are underrepresented.
Save all trained models you might ensemble them later for even better performance.

For example:

If you're building a fraud detection tool , high recall is crucial even if it means sacrificing some accuracy.

What's Happening in This Code?

The code block performs the following tasks:

Split Features & Target:

Uses x = df.drop(['default '], axis=1) and y = df['default '].

Apply Train-Test Split:

Runs x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) to ensure fair evaluation.

Feature Scaling:

Applies StandardScaler() to normalize input features.

Model Selection & Instantiation:

Imports and initializes 9 different classification models .

Model Training:

Trains each model on the scaled training data.

Make Predictions:

Generates predictions using .predict() for each classifier.

Evaluate Accuracy:

Computes accuracy_score(y_test, pred) for each model.

By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.

Code

# Now split the target and features

x = df.drop(['default '], axis=1)

y = df['default ']

# Train-test split

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Feature scaling

from sklearn.preprocessing import StandardScaler

ss = StandardScaler()

x_train_scaled = ss.fit_transform(x_train)

x_test_scaled = ss.transform(x_test)

# Import models

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

from xgboost import XGBClassifier

from lightgbm import LGBMClassifier

from catboost import CatBoostClassifier

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

from sklearn.naive_bayes import GaussianNB

# Initialize models

lr = LogisticRegression()

rf = RandomForestClassifier()

gb = GradientBoostingClassifier()

xgb = XGBClassifier()

svc = SVC()

knn = KNeighborsClassifier()

nb = GaussianNB()

lgb = LGBMClassifier()

cat = CatBoostClassifier(verbose=0)

# Fit models

lr.fit(x_train_scaled, y_train)

rf.fit(x_train_scaled, y_train)

gb.fit(x_train_scaled, y_train)

xgb.fit(x_train_scaled, y_train)

svc.fit(x_train_scaled, y_train)

knn.fit(x_train_scaled, y_train)

nb.fit(x_train_scaled, y_train)

lgb.set_params(verbosity=-1)

lgb.fit(x_train_scaled, y_train)

cat.fit(x_train_scaled, y_train, verbose=False)

# Make predictions

lrpred = lr.predict(x_test_scaled)

rfpred = rf.predict(x_test_scaled)

gbpred = gb.predict(x_test_scaled)

xgbpred = xgb.predict(x_test_scaled)

svcpred = svc.predict(x_test_scaled)

knnpred = knn.predict(x_test_scaled)

nbpred = nb.predict(x_test_scaled)

lgbpred = lgb.predict(x_test_scaled)

catpred = cat.predict(x_test_scaled)

# Evaluate accuracy

from sklearn.metrics import accuracy_score

print('LOGISTIC REG', accuracy_score(y_test, lrpred))

print('RANDOM FOREST', accuracy_score(y_test, rfpred))

print('GB', accuracy_score(y_test, gbpred))

print('XGB', accuracy_score(y_test, xgbpred))

print('SVC', accuracy_score(y_test, svcpred))

print('KNN', accuracy_score(y_test, knnpred))

print('NB', accuracy_score(y_test, nbpred))

print('LIGHT GBM', accuracy_score(y_test, lgbpred))

print('CATO', accuracy_score(y_test, catpred))

Output:

LOGISTIC REG 0.8093

RANDOM FOREST 0.8158

GB 0.8202

XGB 0.8137

SVC 0.8180

KNN 0.7950

NB 0.7138

LIGHT GBM 0.8198

CATO 0.8212

Key Observations:

Top Performer : CatBoost achieved the highest accuracy at 82.12% .
Strong Contenders : Gradient Boosting (82.01%) and LightGBM (81.98%) came very close.
Baseline Models : Logistic Regression and Naive Bayes showed lower performance expected, since they assume independence between variables.
KNN : Performed worse than most models likely due to curse of dimensionality and lack of feature selection.

Insights:

Tree-based models (especially CatBoost ) excel in this dataset suggesting complex interactions between features.
Naive Bayes struggled indicating that independence assumption doesn't hold in this dataset.
These results give us confidence in moving forward with advanced evaluation using confusion matrices , ROC curves , and SHAP interpretation .

We’re officially off to a great start in building a world-class credit card defaulter predictor !

Insight

From this step, we can conclude:

The CatBoost classifier achieves the highest accuracy of 82.12% slightly outperforming other gradient boosting methods.
Gradient Boosting and LightGBM come very close showing strong performance on this dataset.
Naive Bayes and KNN struggle suggesting tree-based models are more suitable for this type of financial classification task.

We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems .

Potential Next Steps and Suggestions

Advanced Evaluation Metrics : Generate confusion matrices and ROC-AUC scores.
Threshold Tuning : Adjust decision thresholds to improve recall for default prediction.
Cross-Validation : Ensure robustness by validating across multiple folds.
Hyperparameter Tuning : Improve performance using GridSearchCV or Bayesian optimization.
Deploy the Model : Save the best-performing model and prepare it for integration into web apps or APIs.

Stay tuned for the next exciting steps in our journey to build a world-class credit card default predictor using machine learning! 🚀

Unveiling Model Performance

Decoding the Confusion Matrix for CatBoost

In our last step, we trained 9 powerful classification models on credit card default prediction data.

Now it’s time to dive deeper into understanding how well our CatBoost classifier performs by analyzing its confusion matrix .

In this step:

We’ll generate a confusion matrix using confusion_matrix() from sklearn.metrics.
Visualize the matrix as a heatmap to see how well the model predicts defaults (1) vs. non-defaults (0).
Gain insights into where the model excels and where it struggles.

This is where machine learning meets model evaluation. Let's uncover the strengths and weaknesses of our AI system!

Why Does It Matter?

Analyzing the confusion matrix matters because:

Performance Breakdown : Helps you understand how many true positives, false positives, true negatives, and false negatives your model produces.
Error Identification : Reveals whether the model struggles more with false positives (predicting defaults when they don’t occur) or false negatives (missing actual defaults).
Business Impact : Guides decisions about which metrics matter most precision, recall, or F1-score.

By running these diagnostics, you ensure your AI system is built on solid, meaningful insights not just random inputs.

What to Expect in This Step

In this step, you'll:

Learn how to compute the confusion matrix using confusion_matrix().
Use sns.heatmap() to visualize the matrix as a heatmap.
Interpret the results to understand how well the model predicts defaults.
Get ready to refine preprocessing and modeling based on these insights.

This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees.

Fun Fact

Did you know?

The CatBoost classifier achieved an impressive 82.12% accuracy , making it one of the top-performing models in our comparison.

And here’s the twist: Our confusion matrix reveals fascinating patterns:

The model correctly identifies most non-defaulters but struggles slightly with predicting actual defaults.
Understanding these nuances helps us refine the model further, maybe by adjusting thresholds or exploring hyperparameter tuning.

That’s exactly what we’re doing now only now, you’re the one making decisions based on real-world financial insights.

Real-Life Example Related to This Step

Imagine you're working for a banking institution , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.

You’ve already trained multiple classifiers and found that CatBoost performed best with 82.12% accuracy . Now, you want to:

Understand how well CatBoost distinguishes between defaulters and non-defaulters.
Identify areas where the model might be over-predicting or under-predicting defaults.
Refine the model based on confusion matrix insights.

By analyzing the confusion matrix:

You confirm that CatBoost correctly identifies most non-defaulters but misses some actual defaults.
You identify potential areas for improvement, like focusing on reducing false negatives.

These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!

Mini Quiz Time!

Let’s test your understanding of confusion matrices:

Question 1: What does confusion_matrix(y_test, catpred) do?
a) Calculates SHAP values
b) Measures how well the model predicts defaults
c) Just makes the code run faster

Question 2: Why do we use a heatmap to visualize the confusion matrix?
a) To make the code look fancy
b) To easily spot patterns in predictions
c) Just for fun

Drop your answers in the comments, I’m excited to hear your thoughts! 💬

Cheat Sheet


Compute Confusion Matrix	Useconfusion_matrix()to evaluate predictions	Builds intuition.
Visualize Matrix	Usesns.heatmap()to highlight patterns	Makes insights clear.
Interpret Results	Look for true/false positives/negatives	Guides refinement.

Pro Tip for You

When interpreting confusion matrices:

Focus on true positives and true negatives : These represent correct predictions.
Check for false positives and false negatives : These indicate where the model struggles.
Consider precision and recall : Precision measures accuracy of positive predictions, while recall measures ability to find all actual positives.

For example:

If the model has high precision but low recall, it might be conservative in predicting defaults.
If it has high recall but low precision, it might predict too many defaults incorrectly.

What's Happening in This Code?

The code block performs the following tasks:

Generate Confusion Matrix:

Uses cm = confusion_matrix(y_test, catpred) to compute the matrix.

Visualize Matrix:

Plots the matrix as a heatmap using sns.heatmap(cm, annot=True).

By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.

Code

from sklearn.metrics import confusion_matrix, classification_report

cm = confusion_matrix(y_test, catpred) # Enter the model pred here

plt.title('Heatmap of Confusion matrix', fontsize=15)

sns.heatmap(cm, annot=True)

plt.show()

Output:

Key Observations:

Confusion Matrix Heatmap :

True Negatives (TN) : The model correctly predicted 4,400 non-defaulters (0).
False Positives (FP) : The model incorrectly predicted 240 non-defaulters as defaulters (0 → 1).
False Negatives (FN) : The model missed 830 actual defaulters (1 → 0).
True Positives (TP) : The model correctly predicted 480 defaulters (1).

Insights:

The CatBoost classifier performs well at identifying non-defaulters but struggles slightly with catching actual defaulters.
False negatives are higher than false positives , suggesting the model might be overly cautious in predicting defaults.

We’re officially off to a great start in building an AI-powered credit default predictor !

Insight

From this step, we can conclude:

The confusion matrix confirms that CatBoost correctly identifies most non-defaulters but misses some actual defaulters.
False negatives are higher than false positives , indicating room for improvement in catching defaults.
These results give us confidence in moving forward with advanced preprocessing and modeling steps.

We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems .

Potential Next Steps and Suggestions

Threshold Tuning : Adjust decision thresholds to improve recall for default prediction.
Cross-Validation : Ensure robustness by validating across multiple folds.
Hyperparameter Tuning : Improve performance using GridSearchCV or Bayesian optimization.
Deploy the Model : Save the best-performing model and prepare it for integration into web apps or APIs.
Iterative Refinement : Explore ensemble methods or deep learning approaches for potential improvement.

Stay tuned for the next exciting steps in our journey to build a world-class credit card default predictor using machine learning! 🚀

🎉 Final Wrap-Up:

What a Powerful Step

You’ve Trained and Evaluated 9 Classification Models on Credit Card Default Prediction! 💳🧠

Wow, what an incredible journey we’ve had in Part 3 of our Credit Card Default Prediction project!

From the moment we split the dataset into training and test sets, you've been on a fast-track journey through:

🧱 Feature Scaling : Applying StandardScaler() to normalize input features.
🤖 Model Training : Training 9 different classifiers , including Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, and more!
📊 Accuracy Comparison : Finding out which models perform best:

CatBoost came out on top with 82.12% accuracy
Gradient Boosting and LightGBM followed closely behind
Naive Bayes and KNN struggled reinforcing that not all models are equal for this type of financial data

And finally we dove into model evaluation using the confusion matrix , revealing how well our top-performing model (CatBoost) distinguishes between:

✅ True Positives (correctly predicted defaulters)
❌ False Negatives (missed defaulters the most dangerous type of error in credit risk)
✅ True Negatives (correctly predicted non-defaulters)
❌ False Positives (incorrectly flagged as defaulters)

This is where theory meets real-world impact and where your hard work pays off with actionable insights.

🏆 Final Thoughts on Part 3

This part wasn’t just about code it was about building something truly impactful:

A full classification pipeline from preprocessing to prediction.
An AI-powered system that listens to customer behavior and predicts who’s at risk of defaulting .
And most importantly a foundation for understanding how to apply machine learning in finance, banking, and risk assessment .

Even though we achieved 82.12% accuracy , we saw that the model still missed some actual defaulters highlighting the need for advanced evaluation techniques like precision-recall tuning , threshold optimization , and SHAP-based interpretation .

🙌 Thank You, Data Detectives!

To every student, viewer, and learner who followed along, thank you so much for being part of this adventure! 👏👏 Whether you’re here because you love financial data science , want to land a job in banking, fintech, or machine learning , or are just curious about how machines predict human behavior, your effort today will shape your success tomorrow.

Every line of code you wrote, every plot you interpreted, and every decision you made brought you closer to machine learning mastery .

Keep pushing forward because the world needs more people like you: curious, passionate, and unafraid to build AI that makes a difference. 🔥

🚨 Get Ready for Part 4

Where We Go Even Deeper Into Model Behavior!

In Part 4 , we’re diving into the final stages of model refinement and deployment:

🔍 Advanced Evaluation Metrics

We’ll generate a full classification report showing:

Precision : Of all predicted defaulters, how many were actually defaulters?
Recall : Of all actual defaulters, how many did the model catch?
F1-score : The perfect balance between precision and recall.
Support : How many samples each class has helped us interpret reliability.

📈 ROC Curve & AUC Score

We’ll calculate the AUC score and visualize the ROC curve giving us insight into how well the model ranks predictions.

🎯 Threshold Tuning

We’ll experiment with changing thresholds to prioritize recall over precision, especially important when catching defaulter cases is critical.

🧠 SHAP Interpretation

We’ll explore what drives predictions using SHAP values , turning our black-box model into a transparent, stakeholder-friendly tool.

🚀 Model Deployment Prep

We’ll export the trained CatBoost model and StandardScaler so they can be used in web apps, dashboards, or APIs.

🎉 Why This Project Will Help You Land Jobs

By completing this project, you’ll have:

A real-world classification pipeline from preprocessing to prediction.
Hands-on experience with credit risk modeling , one of the most in-demand skills in banking and fintech.
An impressive portfolio piece that shows you can handle sensitive financial data responsibly.
A job-ready skillset for roles in:

Risk Assessment
Fintech Product Development
Data Science & Machine Learning Engineering
Credit Scoring & Financial Modeling

Whether you're doing this for fun, for interviews, or for career growth you're creating something meaningful.

🎁 The End

But It’s Just the Beginning!

Thank you once again for being part of this exciting third step. I hope this gave you clarity, confidence, and excitement about what’s possible in classification modeling and financial AI .

Now go get some rest, grab your favorite drink, and get ready for the next chapter because Part 4 is going to be EPIC !💪🔥📊

See you in Part 4 and trust me, it’s going to be packed with advanced evaluation, model tuning, and deployment-ready steps !