Welcome to my Machine Learning portfolio. This repository bridges the gap between raw financial data and intelligent decision-making, applying supervised machine learning algorithms to solve high-stakes problems in Credit Risk Assessment, Predictive Modeling, and FinTech Analytics.
Evaluating the creditworthiness of individuals is a classic, critical problem in financial risk management. This project builds a high-performance Credit Score Prediction system designed to minimize default risks by uncovering complex, non-linear relationships within historical financial indicators.
The system decouples data extraction and preprocessing from core algorithmic execution, ensuring a reliable data science lifecycle:
graph TD
A[Raw Financial Data Ingestion] --> B[Data Preprocessing & Scaling]
B --> C[Feature Engineering & Selection]
C --> D{Model Selection Layer}
D -->|Advanced Ensemble| E[XGBoost Classifier]
D -->|Baseline Comparative| F[Logistic Regression]
D -->|Instance-Based Classifier| G[K-Nearest Neighbors]
E --> H[Model Evaluation & Metrics]
F --> H
G --> H
H --> I[Academic-Grade LaTeX Reporting]
To maintain production-grade rigor, the flagship ensemble model is heavily benchmarked against classical statistical and instance-based classifiers:
| Algorithm | Model Type | Complexity | Key Use Case in FinTech | Strengths |
|---|---|---|---|---|
| XGBoost | Gradient Boosted Trees | High | Primary Risk Scoring Engine | Captures non-linear feature interactions, handles missing data natively, limits overfitting |
| Logistic Regression | Linear Statistical Model | Low | Baseline Comparative Framework | High interpretability, fast inference, establishes linear boundary sanity checks |
| K-Nearest Neighbors | Instance-Based Learning | Medium | Pattern Recognition | Effectively groups localized customer profiles based on financial proximity metrics |
├── credit_score_model.py # Primary XGBoost pipeline for default risk evaluation
├── Logistic_Regression.py # Baseline classification model for binary risk outcomes
├── KNN.py # Instance-based classification engine
├── latex code for xgboost... # Production LaTeX code for academic-grade documentation
├── model_flow.png # Architectural visualization of data engineering pipeline
├── actual_vs_predicted.png # Performance curve charting real vs. inferred default risks
├── machine learning course.pdf # Comprehensive theoretical notes on ML fundamentals
└── machine learning...use cases.pdf # Specialized application mapping for financial models
Get the production model running locally in under two minutes:
# 1. Clone the repository
git clone https://github.com/Vipeen21/machine-learning-projects.git
cd machine-learning-projects
# 2. Install validated dependencies
pip install xgboost scikit-learn pandas matplotlib seaborn
# 3. Execute the core credit scoring engine
python credit_score_model.py- Hyperparameter Optimization Engine: Integrate Optuna for automated Bayesian optimization of XGBoost parameters.
- Explainable AI (XAI): Integrate SHAP (SHapley Additive exPlanations) values to make credit default predictions fully auditable.
- Production API Layer: Wrap the model inside a lightweight FastAPI endpoint containerized via Docker.
If you find this quantitative repository insightful for your financial modelling, AI research, or academic pursuits, consider dropping a star! ⭐
- Author: Vipeen Kumar
- LinkedIn: Profile Link
- Portfolio Website: vipeen21.github.io
#MachineLearning #QuantitativeFinance #CreditScoring #FinTech #DataScience #XGBoost