💳 Credit Card Default Prediction Using Classification ML (Part-1)💳
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-1)
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🎉 Welcome to a New Adventure in Financial Data Science
🧠 Background of the Project:
Why This is So Exciting!
Hey everyone 👋👋 whether you've been with us since our first blog or just joined the mission now, welcome to our brand new project:
🧠 Credit Card Default Prediction Using Classification Machine Learning
In today’s fast-paced financial world, understanding who will default on their credit card payments is one of the most critical tasks for banks, fintech companies, and risk management departments.
That’s where you come in.
With this project, you’ll build an AI-powered system that predicts whether a customer is likely to default on their credit card payment, using real-world data like:
- Demographic info (age, gender, education, marital status)
- Credit history (payment delays across past months)
- Bill amounts over time
- Payment behavior and more
This isn’t just about code, it’s about building something truly impactful:
An AI-powered credit default predictor that helps institutions make smarter decisions, reduce risk, and improve customer experiences.
🔥 Why This Project Matters?
The project matters because:
- It teaches you how to work with real-world financial datasets used by banks and fintech startups.
- You’ll explore classification modeling, feature engineering, and advanced evaluation techniques.
- You’ll learn how to apply powerful ML models like:
- Logistic Regression
- Random Forest
- XGBoost & LightGBM
- CatBoost & Gradient Boosting Trees
- And you’ll explain your model using SHAP values, making it interpretable and stakeholder-friendly.
By working on this project, you’re not just learning machine learning, you’re preparing for high-demand roles in banking, finance, and data science.
💳 What Is Credit Card Default Prediction?
Credit card default prediction involves forecasting whether a customer will fail to make minimum payments based on historical and demographic data.
Some of the key features include:
- `LIMIT_BAL`: The credit limit assigned to the user.
- `SEX`, `EDUCATION`, `MARRIAGE`: Important demographic factors affecting financial behavior.
- `PAY_0`, `PAY_2`, ..., `PAY_6`: These show past payment statuses, where negative values indicate early payments, positive values represent late payments, and higher numbers mean more delays.
- `BILL_AMT1–6` and `PAY_AMT1–6`: Show how much was billed and how much was paid each month — crucial for understanding spending patterns and repayment behavior.
And best of all you're going to teach machines to predict default probability using these features giving you hands-on experience with classification modeling, feature engineering, and model interpretability.
🏆 Why You’ll Love This Project
You’re not just analyzing numbers you’re building a real-world financial risk assessment tool that could help:
- Banks automate loan approval and credit scoring.
- Fintech startups build smart apps that warn users about potential defaults.
- Data Scientists land jobs at top-tier financial institutions.
Whether you're doing this for fun, for your portfolio, or to break into the AI/ML/DS sector, this project gives you the tools to succeed.
🙌 Thoughts
This project isn’t just about predicting defaults, it's about building systems that help financial institutions make smarter, safer, and faster decisions.
From EDA and preprocessing to training, evaluating, and deploying every step brings you closer to machine learning mastery.
So grab your notebook, fire up your Python environment, and let’s dive into Part 1: Introduction to Credit Card Default Prediction & Dataset Overview! 🚀💻
Let’s get started 💪🔥📊
📖 Unveiling the Dataset:
Exploring Credit Card Default Prediction Data
🕵️♂️ In this new project, we’re diving into the world of credit card default prediction, where you’ll build an AI-powered system that forecasts whether a customer is likely to default on their credit card payments.
In this step:
- We’ll load the dataset containing 30,000 entries with 25 columns.
- Take a peek at the first few rows to understand what kind of data we’re working with.
- Identify key features like `LIMIT_BAL`, `PAY_0`, `BILL_AMT1`, and more.
- Get ready to explore relationships between these features and the target variable (`default`).
This is where machine learning meets financial risk assessment. Let's see how well our AI can predict defaults!
Why Does It Matters?
Loading and exploring the dataset matters because:
- Feature Understanding: Helps you identify which variables might be important for predicting defaults.
- Data Quality Check: Ensures there are no missing values or anomalies early in the pipeline.
- Business Context: Builds intuition about how financial institutions use such data to make decisions.
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 load a CSV file using `pd.read_csv()`.
- Use `.head()` to preview the first few rows of the dataset.
- Understand the structure of the dataset, including column names and data types.
- Get ready to perform exploratory data analysis (EDA) and preprocessing.
This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on real-world financial data.
Fun Fact
Did you know?
The credit card default prediction dataset contains 25 columns each representing critical information about customers’ financial behavior.
And here’s the twist:
Our dataset includes both numerical features (like `LIMIT_BAL`, `AGE`, `BILL_AMT1`) and categorical features (like `SEX`, `EDUCATION`, `MARRIAGE`). This diversity makes it perfect for exploring different preprocessing techniques and feature engineering strategies.
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 which features are most predictive of default behavior.
- Explore relationships between payment history, bill amounts, and demographics.
- Refine preprocessing steps based on initial observations.
By analyzing the dataset:
- You confirm that features like `PAY_0`, `BILL_AMT1`, and `LIMIT_BAL` are crucial for predicting defaults.
- 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 dataset exploration:
Question 1: What does `df.head()` do?
a) Loads the entire dataset
b) Displays the first few rows of the dataset
c) Just makes the code run faster
Question 2: Why do we check the dataset structure early?
a) To impress stakeholders
b) To understand which features might be important for predictions
c) Just for fun
Drop your answers in the comment, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Load Dataset | Use `pd.read_csv()` to import data | Set up the pipeline. |
| Preview Data | Use `.head()` to inspect the first few rows | Builds intuition. |
| Understand Structure | Check column names and data types | Guides preprocessing. |
Pro Tip for You
When loading and exploring datasets:
- Always check for missing values using `df.isnull().sum()`.
- Look for outliers in numerical columns using boxplots or histograms.
- Consider encoding categorical variables like `SEX`, `EDUCATION`, and `MARRIAGE`.
For example:
- If `PAY_0` shows many `-1` values, it might indicate early payments worth investigating further.
- If `BILL_AMT1` has extreme values, consider scaling or normalization.
What's Happening in This Code?
The code block performs the following tasks:
1. Import Libraries:
- Imports essential libraries like `pandas`, `numpy`, `matplotlib`, and `seaborn`.
2. Suppress Warnings:
- Uses `warnings.filterwarnings('ignore')` to avoid clutter from non-critical warnings.
3. Load Dataset:
- Reads the dataset using `pd.read_csv('/kaggle/input/credit-card-defaulter-prediction/Credit Card Defaulter Prediction.csv')`.
4. Preview Data:
- Displays the first few rows using `df.head()`.
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import os
warnings.filterwarnings('ignore')
from scipy.stats import randint
from pandas import set_option
plt.style.use('ggplot')
from sklearn.feature_selection import RFE #For cross validation
# Load dataset
df = pd.read_csv('/kaggle/input/credit-card-defaulter-prediction/Credit Card Defaulter Prediction.csv')
df.head()
```
Output:
Explanation
Key Observations:
- Dataset Structure:
- Contains 30,000 rows and 25 columns.
- Features include:
- Numerical: `LIMIT_BAL`, `AGE`, `BILL_AMT1`, etc.
- Categorical: `SEX`, `EDUCATION`, `MARRIAGE`, etc.
- Target Variable: `default` (binary classification).
- Sample Rows:
- Each row represents a customer with details like credit limit (`LIMIT_BAL`), payment status (`PAY_0`, `PAY_2`, etc.), and bill/payment amounts (`BILL_AMT1`, `PAY_AMT1`, etc.).
- The `default` column indicates whether the customer defaulted (`Y` for yes, `N` for no).
Insights:
- The dataset provides a rich mix of demographic, behavioral, and financial features — perfect for building a robust credit default predictor.
- Initial inspection reveals no obvious issues, but deeper EDA will uncover patterns and relationships.
We’re officially off to a great start in building an AI-powered credit default predictor!
Insight
From this step, we can conclude:
- The dataset contains 30,000 entries with 25 columns, providing a solid foundation for training models.
- Key features like `LIMIT_BAL`, `PAY_0`, `BILL_AMT1`, and `default` give us confidence in moving forward with preprocessing and modeling.
- These results give us a realistic view of model impact — beyond just accuracy scores.
We’re officially entering the next phase — let’s dive deeper into exploratory data analysis (EDA) and feature engineering!
Potential Next Steps and Suggestions
1. Exploratory Data Analysis (EDA): Generate histograms, scatter plots, and correlation matrices.
2. Feature Engineering: Create new features like total bill amount or average payment delay.
Encoding Categorical Features
Transforming Text into Numbers for Machine Learning
🕵️♂️ In our last step, we loaded the credit card default prediction dataset and took a peek at its structure.
Now it’s time to dive deeper into preprocessing by encoding categorical features like `SEX`, `EDUCATION`, `MARRIAGE`, and `default`. This is where machine learning meets data preparation. Let's turn text labels into numbers that algorithms can understand!
In this step:
- We’ll convert categorical variables (`SEX`, `EDUCATION`, `MARRIAGE`) into numerical representations.
- Encode the **target variable** (`default`) as binary values (`1` for defaulters, `0` for non-defaulters).
- Prepare the dataset for training classification models like Logistic Regression, Random Forest, or XGBoost.
This is where theory meets practice and where you transform raw data into something machines can learn from!
Why Does It Matters?
Encoding categorical features matters because:
- Machine Learning Algorithms: Most algorithms require numerical inputs and they can’t process text directly.
- Feature Interpretability: Numerical encoding makes it easier to analyze relationships between features.
- Model Performance: Proper encoding ensures your model learns meaningful patterns rather than arbitrary mappings.
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 encode categorical variables using `.replace()` and `.astype(int)`.
- Understand why certain encodings (like ordinal or one-hot encoding) might be better suited for specific tasks.
- 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`, and `MARRIAGE`.
And here’s the twist: By encoding these features numerically, you’re teaching machines to understand human behavior turning qualitative data into actionable insights.
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:
- Convert categorical features like `SEX`, `EDUCATION`, and `MARRIAGE` into numerical representations.
- Encode the target variable (`default`) so your model knows which class corresponds to defaulters.
By performing these transformations:
- You confirm that features like `SEX`, `EDUCATION`, and `MARRIAGE` are now interpretable by machine learning algorithms.
- You prepare the dataset for training powerful classifiers like Random Forest or XGBoost.
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 feature encoding:
Question 1: What does `.replace()` do?
a) Converts categorical variables into numbers
b) Just makes the code run faster
c) Calculates SHAP values
Question 2: Why do we encode categorical features?
a) To make the code look fancy
b) To enable machine learning algorithms to process text data
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Replace Values| Use `.replace()` to map categories to integers | Make data numeric. |
| Convert Data Type| Use `.astype(int)` to ensure proper data types | Ensures consistency. |
| Target Encoding | Encode `default` as binary (0/1) | Prepares target for classification. |
Pro Tip for You
When encoding categorical features:
- Always check if ordinal encoding (assigning numbers based on order) makes sense — e.g., `EDUCATION` levels.
- Consider one-hot encoding for nominal categories with no inherent order.
- Ensure the target variable is encoded consistently (e.g., `Y` → `1`, `N` → `0`).
For example:
- If `EDUCATION` has levels like `'University'`, `'Graduate school'`, `'High School'`, etc., assigning them ordered integers helps capture hierarchical relationships.
- For `SEX`, simple binary encoding (`M` → `1`, `F` → `0`) works well since there’s no hierarchy.
What's Happening in This Code?
The code block performs the following tasks:
1. Encode `SEX` Feature:
- Replaces `'M'` with `1` and `'F'` with `0` using `df.SEX.replace(['M','F'],[1,0]).astype(int)`.
2. Encode `EDUCATION` Feature:
- Maps education levels to integers: `'University'` → `1`, `'Graduate school'` → `2`, etc.
3. Encode `MARRIAGE` Feature:
- Assigns numbers to marital statuses: `'Married'` → `1`, `'Single'` → `2`, etc.
4. Encode Target Variable (`default`):
- Replaces `'Y'` with `1` (default) and `'N'` with `0` (non-defaulter).
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code:
# Encoding categorical features
df.SEX = df.SEX.replace(['M','F'],[1,0]).astype(int)
df.EDUCATION = df.EDUCATION.replace(['University', 'Graduate school', 'High School', 'Unknown',
'Others', '0'],[1,2,3,4,5,6]).astype(int)
df.MARRIAGE = df.MARRIAGE.replace(['Married', 'Single', 'Other', '0'],[1,2,3,4]).astype(int)
# Encode target variable
df['default '] = df['default '].replace(['Y','N'],[1,0]).astype(int)
```
Output:
Explanation
Here’s what the output shows:
Key Observations:
- Encoded Features:
- `SEX`: Mapped to `1` (Male) and `0` (Female).
- `EDUCATION`: Assigned ordinal values based on educational levels.
- `MARRIAGE`: Encoded as `1` (Married), `2` (Single), etc.
- `default`: Converted to binary (`1` for defaulters, `0` for non-defaulters).
Insights:
- The dataset now contains numerical representations of categorical features, making it ready for machine learning algorithms.
- These transformations preserve the original meaning while ensuring compatibility with classifiers.
We’re officially off to a great start in building an AI-powered credit default predictor!
Insight
From this step, we can conclude:
- The encoded dataset transforms categorical variables into numerical formats, enabling machine learning algorithms to process them effectively.
- Features like `SEX`, `EDUCATION`, and `MARRIAGE` are now represented as integers, capturing meaningful relationships.
- The target variable (`default`) is encoded as binary, preparing it for classification tasks.
We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems.
Potential Next Steps and Suggestions
1. Exploratory Data Analysis (EDA): Generate histograms, scatter plots, and correlation matrices.
Learning! 🚀
Cleaning & Preparing the Data:
Handling Edge Cases in Categorical Features
🕵️♂️ In our last step, we encoded categorical features like `SEX`, `EDUCATION`, and `MARRIAGE` into numerical values making them ready for machine learning.
Now it’s time to take one more leap forward by:
- Separating features (`x`) and target (`y`)
- Cleaning up inconsistent or rare categories in `EDUCATION` and `MARRIAGE`
- Ensuring all feature values make sense before training begins
- Preparing everything for modeling with confidence
This is where theory meets real-world data science. Let's refine our dataset so that our AI makes smart, consistent decisions!
Why Does It Matters?
Cleaning and preparing data matters because:
- Inconsistent Categories: Some entries in `EDUCATION` and `MARRIAGE` might be mislabeled (e.g., `'Unknown'`, `'Others'`, or even `'0'`).
- Model Reliability: Garbage in = garbage out. Clean data ensures your model learns meaningful patterns.
- Business Impact: Real-world models used in banks or fintech apps need clean, interpretable inputs especially when approving loans or predicting risk.
By refining these features now, you're ensuring your AI system can be trusted in production environments.
What to Expect in This Step
In this step, you'll:
- Learn how to use `np.where()` to replace outlier or rare categories in `EDUCATION`.
- Handle unknown or invalid marriage statuses using similar logic.
- Separate features and labels properly.
- Understand why some transformations are applied before model training.
This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees in financial behavior.
Fun Fact:
Did you know?
Sometimes datasets contain unusual category mappings like `'0'` appearing under `EDUCATION` or `MARRIAGE`.
And here’s the twist: By applying `np.where(df.EDUCATION == 5, 4, df.EDUCATION)`,
we’re telling the model:
> "Treat Unknown, Others, and 0 as the same educational level."
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 defaults for loan approval automation.
You’ve already loaded the dataset and encoded categorical variables. Now, you want to:
- Remove noise from the `EDUCATION` field where `'Unknown'`, `'Others'`, and even `'0'` are grouped together.
- Ensure `MARRIAGE` doesn't have invalid values like `'0'` skewing predictions.
By cleaning up edge cases:
- You confirm that the model will train on realistic, well-defined groups.
- Stakeholders gain confidence in deploying this AI system for real-world use.
These insights help create technology that listens, learns, and acts based on what it sees, turning raw financial data into actionable content!
Mini Quiz Time!
Let’s test your understanding of data preparation:
Question 1: What does `np.where(df.EDUCATION == 5, 4, df.EDUCATION)` do?
a) Just renames the column
b) Replaces value 5 with 4 in EDUCATION
c) Makes the code run faster
Question 2: Why do we clean up rare or unknown categories in `EDUCATION` and `MARRIAGE`?
a) To impress stakeholders
b) To reduce noise and improve model reliability
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
| Task | Description | Importance |
|------|-------------|------------|
| Separate Features & Target| Use `drop('default')` and extract `default` column | Prepares for modeling. |
| Clean Education Categories | Replace 5, 6, and 0 with 4 | Reduces noise in data. |
| Fix Marriage Status | Replace 0 with 3 | Ensures valid categories. |
Pro Tip for You
When handling categorical data:
- Always check unique values using `.unique()` especially after replacements.
- Consider grouping rare or unknown categories to avoid overfitting to outliers.
- Save your preprocessing steps; they'll be needed again during deployment.
For example:
- If `EDUCATION=4` stands for "Unknown", treat it as a separate group rather than trying to assign it to a specific education level.
What's Happening in This Code?
The code block performs the following tasks:
1. Separate Features & Target:
- Uses `features = df.drop(columns=['default '], axis=1)` to isolate input variables.
- Assigns `y = df['default ']` as the binary classification target.
2. Clean Up Education Levels:
- Replaces `5`, `6`, and `0` in `EDUCATION` with `4` treating them as a single unknown category.
3. Fix Marriage Status:
- Replaces `0` in `MARRIAGE` with `3` likely treating it as "Other".
4. Check Unique Values:
- Confirms that `df.MARRIAGE.unique()` now contains `[1, 2, 3, 4]`.
By running these diagnostics, we gain insights into how well-prepared the dataset is for classification modeling.
Code:
# Separate features and target
features = df.drop(columns=['default '], axis=1)
y = df['default ']
# Fix inconsistent education levels
df.EDUCATION = np.where(df.EDUCATION == 5, 4, df.EDUCATION)
df.EDUCATION = np.where(df.EDUCATION == 6, 4, df.EDUCATION)
df.EDUCATION = np.where(df.EDUCATION == 0, 4, df.EDUCATION)
# Fix invalid marital status
df.MARRIAGE = np.where(df.MARRIAGE == 0, 3, df.MARRIAGE)
# Check unique values
df.MARRIAGE.unique()
```
Output:
array([1, 2, 3, 4])
Key Observations:
- After cleaning, the `MARRIAGE` column now has only four valid categories:
- `1`: Married
- `2`: Single
- `3`: Other
- `4`: Unknown (originally `0`)
- Similarly, `EDUCATION` was cleaned by replacing rare or invalid categories (`5`, `6`, and `0`) with `4` creating a unified “Unknown” group.
Insights:
- These transformations ensure that all categorical features have consistent and meaningful representations.
- We’ve eliminated invalid or rare categories reducing noise and improving model generalization.
- The dataset is now cleaner, more reliable, and ready for advanced preprocessing and modeling.
We’re officially off to a great start in building a production-ready credit default prediction system!
Insight
From this step, we can conclude:
- We successfully separated features (`x`) and target (`y`) preparing for model training.
- Applied smart data cleaning to handle rare/unexpected categories in `EDUCATION` and `MARRIAGE`.
- Used `np.where()` to maintain consistency across categorical values improving model reliability.
- These results give us confidence in moving forward with feature scaling, model training, and advanced evaluation.
We’re officially entering the next phase and getting closer to predicting credit defaults like a pro.
Potential Next Steps and Suggestions
1. Feature Scaling: Apply StandardScaler or MinMaxScaler to normalize bill amounts and payment history.
2. Train-Test Splitting: Divide the dataset into training and testing sets.
🎉 Final Wrap-Up: What a Powerful Start
You Built the Foundation of a Credit Default Prediction System! 💳🧠
Wow, what an incredible journey we’ve had in Part 1 of our Credit Card Default Prediction project!
From the moment we loaded the dataset with 30,000 entries and 25 features, you've been on a fast-track journey through:
- 📊 Data Exploration: Understanding what each column represents from `LIMIT_BAL` to `PAY_0`, `BILL_AMT1`, and beyond.
- 🔁 Categorical Encoding: Transforming text-based categories like `SEX`, `EDUCATION`, and `MARRIAGE` into numerical values that machines can learn from.
- 🧹 Data Cleaning: Handling edge cases by replacing invalid or unknown values in `EDUCATION` and `MARRIAGE`.
- 🎯 Target Preparation: Converting the `default` column into binary labels (`Y=1`, `N=0`) for classification modeling.
You didn’t just play around with data you built something truly impactful:
- A clean, structured, and ready-to-model dataset that banks, fintech companies, and credit risk analysts would be proud of.
- A strong foundation for training powerful classifiers that predict who’s likely to default and who isn’t.
This is where theory meets practice and where your hard work pays off with real results.
🌟 Final Thoughts on Part 1
This part wasn’t just about loading data, it was about preparing for intelligent decision-making.
- You’ve taken raw financial information and turned it into machine-readable format.
- You’ve ensured that rare or inconsistent values won’t confuse your AI model later.
- And most importantly you’ve set yourself up for success in building a real-world credit card defaulter predictor.
Even though we haven’t trained any models yet you’re already halfway there. With every line of code, you're proving that you know how to build reliable, production-ready machine learning systems.
🙌 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 AI, 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 transformation you made, and every decision you took 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. 🔥
🚨 Stay Tuned for Part 2
Where the Real Fun Begins!
In Part 2, we’re diving into the heart of data science:
📊 Exploratory Data Analysis (EDA)
We’ll visualize distributions, correlations, and payment trends discovering which factors most strongly influence defaults.
📈 Feature Engineering
We’ll create new features like:
- Average bill amount across months.
- Total paid vs. total owed.
- Delay patterns in past payments.
📉 Advanced Visualizations
We’ll explore how different demographics perform financially using bar charts, heatmaps, and distribution plots.
These insights will guide our next steps and help us train smarter models that understand who is at risk of defaulting, and why.
🏆 Why This Project Will Boost Your Career
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 creating something meaningful.
🎉 The End But It’s Just the Beginning!
Thank you once again for being part of this exciting first 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 2 is going to be EPIC! 💪🔥📊
See you in Part 2 and trust me, it’s going to be packed with visual storytelling, deep insights, and AI-driven financial predictions!