💳 Credit Card Default Prediction Using Classification ML (Part-2)💳
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-2)
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🎉 Welcome Back, Financial Data Detectives! 🕵️♂️🧠
It’s Time for Part 2: The Real Fun Begins!
Hey everyone 👋👋 whether you've been with us since the first line of code or just joined the mission now, welcome to Part 2 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.
- Preparing everything for exploratory analysis and modeling.
Now it’s time to dive into the real heart of data science:
📊 Exploratory Data Analysis (EDA)
📉 Feature Engineering
👀 Visualizing Patterns That Predict Credit Risk
🔍 What’s Coming in Part 2?
This is where machine learning meets storytelling and where you’ll uncover the hidden patterns that drive credit card defaults:
📊 Exploratory Data Analysis (EDA)
We’ll explore how different demographics and financial behaviors influence default rates:
- Which age group is more likely to default?
- Does educational background affect repayment habits?
- How does marriage status impact credit risk?
🧠 Feature Engineering
We’ll create new, predictive features from existing data:
- Average bill amount across months.
- Total paid vs. total owed.
- Delay trends over time (`PAY_0` to `PAY_6`).
- Payment-to-bill ratio a key indicator of financial responsibility.
📈 Advanced Visualizations
We’ll generate:
- Distribution plots of credit limits and ages.
- Correlation matrices between payment delays and default behavior.
- Bar charts showing default rates across different groups.
These insights will guide our next steps and help us build a smarter, more accurate defaulter prediction system.
💼 Why This Matters
You’re not just analyzing numbers you're building something truly impactful:
- An AI-powered credit default detection system that banks, fintech startups, and risk departments can use to automate decisions.
- A full classification pipeline from preprocessing to prediction and interpretation.
- And most importantly a foundation for understanding how to apply machine learning in real-world finance.
Whether you're doing this for fun, for your portfolio, or to land a job in banking, AI, or data science this part is pure gold. 💥
🙌 Thought?
You’ve already done the heavy lifting from loading text data and encoding categories to cleaning edge cases and preparing for classification.
Now it’s time to bring everything together and enter the most exciting phase exploring the financial DNA of defaulters and building a model that understands who’s at risk and why.
So grab your notebook, fire up your Python environment, and let’s dive into Part 2: EDA & Feature Engineering Like a Pro! 🚀💻
Let’s go! 💪🔥💳
📊 Unveiling the Default Landscape
Exploratory Data Analysis (EDA) for Credit Card Defaults
🕵️♂️ In our last step, we cleaned up categorical features like `EDUCATION` and `MARRIAGE`, ensuring our dataset is ready for modeling.
Now it’s time to dive into Exploratory Data Analysis (EDA) where we’ll uncover patterns in credit card default behavior by visualizing how many customers defaulted versus those who didn’t.
In this step:
- We’ll calculate the frequency of defaults (`default = 1`) and non-defaults (`default = 0`).
- Plot a countplot to visualize the distribution of defaults.
- Add annotations to show percentages and absolute counts for clarity.
- Gain insights into whether the dataset is balanced or imbalanced.
This is where machine learning meets storytelling let’s see what hidden truths lie within the data!
Why Does It Matters?
EDA matters because:
- Class Imbalance: Helps you understand if one class dominates the other, which can affect model performance.
- Data Insights: Builds intuition about how defaults are distributed across the dataset.
- Model Preparation: Guides decisions about preprocessing steps like oversampling or undersampling.
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 calculate the frequency and percentage of defaults.
- Use `sns.countplot()` to visualize the distribution of `default`.
- Annotate the plot with absolute counts and percentages for better interpretation.
- 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 30,000 entries, with 77.9% non-defaulters and 22.1% defaulters.
And here’s the twist: Our countplot reveals that the dataset is imbalanced, meaning there are far more non-defaulters than defaulters. This insight is crucial because:
- Many machine learning algorithms struggle with imbalanced datasets.
- Understanding this imbalance helps us decide whether to apply techniques like oversampling or class weighting during training.
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 many customers default vs. non-default.
- Visualize the distribution of defaults to check for class imbalance.
- Refine preprocessing steps based on initial observations.
By analyzing the target variable:
- You confirm that 77.9% of customers don’t default, while 22.1% do highlight the need for careful handling of imbalanced data.
- You identify potential areas for feature engineering, like balancing classes or focusing on high-risk groups.
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 EDA:
Question 1: What does `sns.countplot(x='default ', data=df)` do?
a) Calculates SHAP values
b) Plots the distribution of the `default` column
c) Just makes the code run faster
Question 2: Why do we annotate plots with percentages and counts?
a) To make the code look fancy
b) To provide clear, interpretable insights
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Calculate Frequencies | Use `.sum()` and `len(df)` to count defaults and non-defaults | Builds intuition. |
| Visualize Distribution | Use `sns.countplot()` to plot default counts | Makes insights clear. |
| Add Annotations | Use `plt.annotate()` to highlight key metrics | Enhances interpretability. |
Pro Tip for You
When interpreting countplots:
- Focus on class balance: Check if one class dominates the other.
- Look for patterns: Are there any surprises in the distribution?
- Consider annotations: Make sure percentages and counts are easy to read.
For example:
- If the dataset is imbalanced, consider techniques like SMOTE or class weighting during training.
- If certain demographics skew heavily toward defaults, explore why.
What's Happening in This Code?
The code block performs the following tasks:
1. Calculate Default Frequencies:
- Uses `yes = df['default '].sum()` to count defaults.
- Computes non-defaults as `no = len(df) - yes`.
2. Compute Percentages:
- Calculates percentages using `round(yes/len(df)*100, 1)` and `round(no/len(df)*100, 1)`.
3. Plot Countplot:
- Generates a bar chart using `sns.countplot(x='default ', data=df, palette='Blues')`.
4. Add Annotations:
- Adds labels showing counts and percentages using `plt.annotate()`.
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code
#NOW we will map the target: categorize
#The frequency of defaults
yes = df['default '].sum()
no = len(df) - yes
#Percentage
yes_perc = round(yes / len(df) * 100, 1)
no_perc = round(no / len(df) * 100, 1)
import sys
plt.figure(figsize=(7, 4))
sns.set_context('notebook', font_scale=1.2)
sns.countplot(x='default ', data=df, palette='Blues')
plt.annotate(f'Non-default:{no}', xy=(-0.3, 15000), xytext=(-0.3, 3000), size=12)
plt.annotate(f'Default:{yes}', xy=(0.7, 15000), xytext=(0.7, 3000), size=12)
plt.annotate(f'{no_perc} %', xy=(-0.3, 15000), xytext=(-0.1, 8000), size=12)
plt.annotate(f'{yes_perc} %', xy=(0.7, 15000), xytext=(0.9, 8000), size=12)
plt.title('COUNT OF CREDIT CARDS', size=14)
#Removing the frame
plt.box(False);
```
Output:
Key Observations:
- Default Distribution:
- Non-defaulters: 23,364 customers (77.9% of the dataset).
- Defaulters: 6,636 customers (22.1% of the dataset).
- Visualization:
- The countplot clearly shows that the dataset is imbalanced, with far more non-defaulters than defaulters.
- Annotations provide both absolute counts and percentages for easy interpretation.
Insights:
- The dataset is heavily skewed toward non-defaulters, which could impact model performance.
- This imbalance suggests that defaulters are a rare but critical group to predict accurately.
- These results give us confidence in moving forward with advanced preprocessing steps like balancing classes or focusing on high-risk groups.
We’re officially off to a great start in building an AI-powered credit default predictor!
Insight
From this step, we can conclude:
- The countplot confirms that the dataset is imbalanced, with 77.9% non-defaulters and 22.1% defaulters.
- The visualization highlights the importance of addressing class imbalance before training models.
- 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:
1. Handling Imbalance: Explore techniques like SMOTE, ADASYN, or class weighting.
2. Feature Engineering: Create new features like total bill amounts, payment delays, etc.
Unveiling Patterns in Categorical Variables Exploring How Defaults Vary Across Groups
🕵️♂️ In our last step, we visualized the overall distribution of defaults using a countplot, revealing that the dataset is heavily imbalanced, with 77.9% non-defaulters and 22.1% defaulters.
Now it’s time to dive deeper into how categorical variables like `SEX`, `EDUCATION`, `MARRIAGE`, and payment delays (`PAY_0` to `PAY_6`) influence credit card default behavior.
In this step:
- We’ll create a subset of categorical features related to demographics and payment history.
- Generate grouped count plots to visualize how defaults vary across different categories.
- Gain insights into which groups are more likely to default based on their characteristics.
This is where machine learning meets pattern recognition let’s uncover hidden truths about who’s at risk!
Why Does It Matters?
Analyzing categorical variables matters because:
- Group Insights: Helps you understand how different demographics or behaviors affect default rates.
- Feature Importance: Guides decisions about which features might be most predictive.
- Model Refinement: Identifies areas where preprocessing (e.g., encoding or grouping) could improve performance.
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 create subsets of categorical features for focused analysis.
- Use `sns.countplot()` with `hue='default '` to compare default rates across categories.
- Interpret grouped bar charts to identify patterns in default behavior.
- 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 several categorical features like `SEX`, `EDUCATION`, `MARRIAGE`, and payment delays (`PAY_0` to `PAY_6`).
And here’s the twist: Our grouped countplots reveal fascinating patterns:
- Certain demographics (e.g., `SEX`, `EDUCATION`) show clear differences in default rates.
- Payment delays (`PAY_0` to `PAY_6`) highlight how past behavior predicts future defaults.
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 defaults vary across different demographic groups.
- Explore relationships between payment delays and default behavior.
- Refine preprocessing steps based on initial observations.
By analyzing categorical variables:
- You confirm that certain groups (e.g., unmarried individuals or those with delayed payments) are more likely to default.
- 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 grouped count plots:
Question 1: What does `hue='default '` do in `sns.countplot()`?
a) Plots the distribution of the `default` column
b) Colors bars by the `default` variable to show group comparisons
c) Just makes the code run faster
Question 2: Why do we analyze categorical variables?
a) To make the code look fancy
b) To understand how different groups behave differently
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Create Subset | Select relevant categorical features | Focuses analysis. |
| Grouped Countplots | Use `sns.countplot()` with `hue='default '` | Highlights group differences. |
| Interpret Patterns | Look for trends in default rates across categories | Guides preprocessing. |
Pro Tip for You
When interpreting grouped countplots:
- Focus on group comparisons: Look at how default rates differ across categories.
- Check for patterns: Are certain groups consistently higher or lower in defaults?
- Consider preprocessing: Group rare categories or encode them meaningfully.
For example:
- If unmarried individuals (`MARRIAGE=3`) have higher default rates, consider treating them as a separate group.
- If payment delays (`PAY_0` to `PAY_6`) show strong correlations with defaults, explore combining them into a single feature.
What's Happening in This Code?
The code block performs the following tasks:
1. Create Subset of Categorical Features:
- Uses `subset = df[['SEX','EDUCATION','MARRIAGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','default ']]` to focus on key variables.
2. Generate Grouped Countplots:
- Creates a grid of plots using `plt.subplots(3,3)` to visualize multiple categorical variables.
- Plots each variable using `sns.countplot(x='feature', hue='default ', data=subset, palette='Blues')`.
3. Customize Visuals:
- Adds titles, labels, and colors to enhance readability.
- Ensures each plot highlights how defaults vary across categories.
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code:
# Creating a new df with categorical variables
subset = df[['SEX','EDUCATION','MARRIAGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6','default ']]
f, axes = plt.subplots(3,3, figsize=(20,25), facecolor='white')
f.suptitle('FREQUENCY OF CATEGORICAL VARIABLES (BY TARGET)')
ax1 = sns.countplot(x='SEX',hue='default ', data=subset, palette='Blues',ax=axes[0,0])
ax2 = sns.countplot(x='EDUCATION',hue='default ', data=subset, palette='Blues',ax=axes[0,1])
ax3 = sns.countplot(x='MARRIAGE',hue='default ', data=subset, palette='Blues',ax=axes[0,2])
ax4 = sns.countplot(x='PAY_0',hue='default ', data=subset, palette='Blues',ax=axes[1,0])
ax5 = sns.countplot(x='PAY_2',hue='default ', data=subset, palette='Blues',ax=axes[1,1])
ax6 = sns.countplot(x='PAY_3',hue='default ', data=subset, palette='Blues',ax=axes[1,2])
ax7 = sns.countplot(x='PAY_4',hue='default ', data=subset, palette='Blues',ax=axes[2,0])
ax8 = sns.countplot(x='PAY_5',hue='default ', data=subset, palette='Blues',ax=axes[2,1])
ax9 = sns.countplot(x='PAY_6',hue='default ', data=subset, palette='Blues',ax=axes[2,2])
```
Output :
Explanation
Here’s what the output shows:
Key Observations:
- SEX:
- Both males (`0`) and females (`1`) default at similar rates, suggesting gender isn’t a strong predictor.
- EDUCATION:
- Higher education levels (`1` and `2`) tend to have slightly lower default rates compared to others.
- MARRIAGE:
- Married individuals (`1`) appear less likely to default compared to singles (`2`) or others (`3`).
- PAY_0 to PAY_6:
- Delayed payments (`-1`, `-2`, etc.) correlate strongly with higher default rates.
- On-time payments (`0`) show significantly lower default rates.
Insights:
- Demographics like `SEX`, `EDUCATION`, and `MARRIAGE` provide some insight but aren’t as predictive as payment behavior.
- Payment history (`PAY_0` to `PAY_6`) is a strong indicator of default risk showing clear patterns across all months.
We’re officially off to a great start in building an AI-powered credit default predictor!
Insight
From this step, we can conclude:
- The grouped countplots reveal how defaults vary across different categories of `SEX`, `EDUCATION`, `MARRIAGE`, and payment delays.
- Payment history (`PAY_0` to `PAY_6`) shows the strongest correlation with default behavior.
- 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
1. Handling Imbalance: Explore techniques like SMOTE, ADASYN, or class weighting.
2. Feature Engineering: Create new features like total bill amounts, payment delays, etc.
Unveiling Credit Limits
How Default Status Varies with Limit Balances
🕵️♂️ In our last step, we explored how categorical variables like `SEX`, `EDUCATION`, `MARRIAGE`, and payment delays (`PAY_0` to `PAY_6`) influence credit card default behavior.
Now it’s time to dive deeper into understanding how LIMIT_BAL (credit limit) affects default rates. This is a critical financial feature that could reveal whether higher or lower credit limits correlate with default risk.
In this step:
- We’ll separate `LIMIT_BAL` values based on the `default` status (`Yes` vs. `No`).
- Generate a histogram to visualize how credit limits are distributed across defaulters and non-defaulters.
- Gain insights into whether certain credit limits are more likely to lead to defaults.
This is where machine learning meets financial analysis — let’s uncover hidden truths about credit limits!
Why Does It Matters?
Analyzing `LIMIT_BAL` matters because:
- Credit Risk Assessment: Helps you understand if high or low credit limits correlate with default behavior.
- Feature Importance: Guides decisions about which features might be most predictive.
- Model Refinement: Identifies areas where preprocessing (e.g., scaling or binning) could improve performance.
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 separate `LIMIT_BAL` values based on default status.
- Use `plt.hist()` to plot histograms for defaulters (`default = 1`) and non-defaulters (`default = 0`).
- Interpret the histogram to identify patterns in credit limits.
- 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 LIMIT_BAL column represents the credit limit assigned to each customer, measured in NT dollars (New Taiwan Dollars).
And here’s the twist: Our histogram reveals fascinating patterns:
- Certain credit limit ranges show clear differences in default rates.
- Higher credit limits don’t necessarily mean higher default risks but there might be sweet spots where defaults spike.
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 the dataset and taken a peek at its structure. Now, you want to:
- Understand how credit limits vary across defaulters and non-defaulters.
- Explore relationships between credit limits and default behavior.
- Refine preprocessing steps based on initial observations.
By analyzing `LIMIT_BAL`:
- You confirm that certain credit limit ranges are more prone to defaults.
- You identify potential areas for feature engineering, like binning credit limits or normalizing them.
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 histograms:
Question 1: What does `plt.hist([x1,x2], bins=40)` do?
a) Plots two histograms side by side
b) Just makes the code run faster
c) Calculates SHAP values
Question 2: Why do we analyze `LIMIT_BAL`?
a) To make the code look fancy
b) To understand how credit limits affect default behavior
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Separate Data | Split `LIMIT_BAL` based on `default` status | Focuses analysis. |
| Histogram Plot | Use `plt.hist()` to compare distributions | Highlights group differences. |
| Interpret Patterns | Look for trends in default rates across credit limits | Guides preprocessing. |
Pro Tip for You
When interpreting histograms:
- Focus on distribution comparisons: Look at how credit limits differ between defaulters and non-defaulters.
- Check for patterns: Are certain credit limit ranges more prone to defaults?
- Consider preprocessing: Bin credit limits or normalize them meaningfully.
For example:
- If higher credit limits (`> 400,000 NT dollars`) show higher default rates, consider grouping them into categories.
- If lower credit limits (`< 100,000 NT dollars`) have consistent default rates, treat them as a single group.
What's Happening in This Code?
The code block performs the following tasks:
1. Separate LIMIT_BAL Based on Default Status:
- Uses `x1 = list(df[df['default '] == 1]['LIMIT_BAL'])` to extract credit limits for defaulters.
- Uses `x2 = list(df[df['default '] == 0]['LIMIT_BAL'])` to extract credit limits for non-defaulters.
2. Generate Histograms:
- Plots two histograms using `plt.hist([x1, x2], bins=40, density=False, color=['steelblue', 'lightblue'])`.
3. Customize Visuals:
- Adds legends, labels, and titles to enhance readability.
- Ensures the plot highlights how credit limits vary across default statuses.
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code:
# Separate the limit balance based on the default status
x1 = list(df[df['default '] == 1]['LIMIT_BAL'])
x2 = list(df[df['default '] == 0]['LIMIT_BAL'])
plt.figure(figsize=(12,4))
sns.set_context('notebook', font_scale=1.2)
# Plot the histogram
plt.hist([x1, x2], bins=40, density=False, color=['steelblue', 'lightblue'])
plt.xlim([0, 600000])
# Add a legend
plt.legend(['Yes', 'No'], title='DEFAULT', loc='upper right', facecolor='white')
# Add labels and titles
plt.xlabel('Limit balance (NT dollar)')
plt.ylabel('Frequency')
plt.title('LIMIT BALANCE HISTOGRAM BY TYPE OF CREDIT CARD', size=15)
# Remove the frame
plt.box(False)
plt.show()
```
Output:
Explanation:
Here’s what the output shows:
Key Observations:
- Distribution Comparison:
- The histogram compares credit limits (`LIMIT_BAL`) for defaulters (`Yes`) and non-defaulters (`No`).
- Both groups show similar shapes, but there are subtle differences in frequency across certain ranges.
- Patterns:
- Lower Credit Limits: Both defaulters and non-defaulters have significant frequencies around lower credit limits (e.g., below 200,000 NT dollars).
- Higher Credit Limits: Defaults appear slightly less frequent at higher credit limits, though both groups still show activity up to 600,000 NT dollars.
Insights:
- Credit Limits Don’t Strongly Correlate with Defaults: While there are slight differences, the overall distribution suggests that credit limits alone aren’t strong predictors of default behavior.
- Focus on Other Features: Payment history, demographics, and other financial behaviors might provide stronger signals.
We’re officially off to a great start in building an AI-powered credit default predictor!
Insight
From this step, we can conclude:
- The histogram reveals how credit limits (`LIMIT_BAL`) are distributed across defaulters and non-defaulters.
- While there are some differences, credit limits alone don’t strongly predict default behavior — suggesting we should focus on other features like payment history or demographics.
- 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:
1. Handling Imbalance: Explore techniques like SMOTE, ADASYN, or class weighting.
2. Feature Engineering: Create new features like total bill amounts, payment delays, etc.
🎉 Final Wrap-Up:
What a Powerful Step
You’ve Mastered the Core of Exploratory Data Analysis (EDA) in Credit Card Default Prediction! 💳📊
Wow, what an incredible journey through Part 2 of our Credit Card Default Prediction Project!
In this part:
- We explored how many customers defaulted vs. didn’t default, revealing that only 22.1% of users are defaulters highlighting the need to handle class imbalance carefully.
- We visualized categorical features like `SEX`, `EDUCATION`, and `MARRIAGE` across different default statuses gaining insights into which groups are more likely to default.
- Most excitingly we dove into LIMIT_BAL (credit limits), generating a histogram that compared defaulters and non-defaulters side-by-side.
- And found something surprising: credit limit alone isn’t a strong predictor of default behavior but it still plays a role when combined with other variables.
You didn’t just explore data you built a solid foundation for smart feature engineering, balanced modeling, and powerful prediction.
🎯 Key Takeaways from Part 2
These findings give us actionable insight for the next steps:
| Feature | Insight |
|--------|---------|
| Default Distribution | The dataset is imbalanced (77.9% non-defaulters, 22.1% defaulters) meaning we should consider techniques like oversampling, undersampling, or class weighting during model training. |
| Demographics (`SEX`, `EDUCATION`, `MARRIAGE`) | Certain groups like singles (`MARRIAGE=2`) or those with unknown education (`EDUCATION=4`) show higher default rates — suggesting these could be important features after proper encoding and normalization. |
| Payment History (`PAY_0` to `PAY_6`) | Payment delays are strong indicators of default risk especially as they increase. This gives us confidence that payment history will be a highly predictive set of features. |
| LIMIT_BAL (Credit Limit) | While not strongly predictive on its own, it shows interesting trends when grouped with other features making it a valuable piece of the puzzle when used with others like bill amounts and payment behavior. |
These results aren't just numbers they’re clues that help your AI understand who’s at risk and why.
🙌 A Big Thank You to All Learners & Readers
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 preparing for your next interview, 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 3
Where the Real Magic Happens!
In Part 3, we’re diving even deeper into the data and entering the most exciting phase of any classification project:
📊 Advanced EDA
We’ll generate correlation matrices, heatmaps, and distribution plots to uncover hidden relationships between features and target.
🧠 Feature Engineering
We’ll create powerful new features like:
- Total bill amount over time
- Average payment delay
- Ratio of paid-to-billed amount per month
- Delay trend analysis
🤖 Model Training Begins!
We’ll start applying classification models like:
- Logistic Regression
- Random Forest
- XGBoost & LightGBM
- CatBoost and Gradient Boosting Trees
📈 Model Evaluation Metrics
We’ll evaluate performance using:
- Accuracy, Precision, Recall, F1-score
- ROC-AUC curves
- Confusion matrices
- SHAP values for interpretation
This is where theory meets practice and where you turn raw data into real-world defaulter detection!
🏆 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 finance and data science.
- An impressive portfolio piece that shows you can handle sensitive financial data responsibly.
- A job-ready skillset for roles in:
- Banking & 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 building something truly impactful.
🎉 The End
But It’s Just the Beginning!
Thank you once again for being part of this exciting second 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 3 is going to be EPIC!💪🔥🧠
See you in Part 3 and trust me, it’s going to be packed with real-world insights and AI-driven predictions!