💳 AML Default Prediction 📖 Problem Statement

Predicting loan defaults is critical in the financial industry to mitigate risks and prevent significant losses. This project aims to build a predictive model to identify customers who are likely to default on their loans, enabling proactive measures to reduce financial risks and improve decision-making. 🛠️ Tools Used

Programming Language: Python 🐍
Notebook Environment: Jupyter Notebook 📓
Libraries:
    Data Manipulation: Pandas, NumPy
    Visualization: Matplotlib, Seaborn
    Machine Learning: scikit-learn, XGBoost
    Model Evaluation: Metrics like ROC-AUC, Precision, Recall

🔍 Approach

Data Collection and Cleaning:
    Import loan datasets and preprocess the data.
    Handle missing values, outlier detection, and data normalization.

Feature Engineering:
    Perform exploratory data analysis (EDA) to identify key features.
    Create new features based on domain knowledge to improve model performance.

Model Selection and Training:
    Experiment with multiple machine learning models, such as:
        Logistic Regression
        Random Forest
        Gradient Boosting (e.g., XGBoost)
    Fine-tune hyperparameters using GridSearchCV or RandomizedSearchCV.

Evaluation:
    Use evaluation metrics like ROC-AUC, F1-score, and Precision-Recall curves.
    Compare model performances and select the best-performing model.

Deployment (Optional):
    Package the model for production use with APIs or web apps.

🎯 Outcome/Results

The project successfully builds a robust predictive model for loan default prediction. The final model achieves high performance with a ROC-AUC score of XX.XX% (replace with actual results). This helps financial institutions make data-driven decisions and minimize risks. 🚀 Steps to Run and Installation Guide Clone the Repository bash

git clone https://github.com/shobhitpachauri/AML-Default-Prediction.git cd AML-Default-Prediction

Set Up Environment

Install Dependencies:
    Create a virtual environment (optional but recommended):
    bash

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install required libraries: bash

pip install -r requirements.txt

Run the Jupyter Notebook:

Start Jupyter Notebook:
bash

    jupyter notebook

    Open and execute the notebook cells in sequence to replicate the results.

✨ Future Enhancements

Incorporate additional datasets to improve model diversity.
Implement deep learning models for enhanced prediction accuracy.
Create a user-friendly web interface for real-time predictions.
Deploy the model using Flask or Streamlit for production use.

🙌 Acknowledgments

Thanks to the open-source community for providing tools and libraries.
Special thanks to financial datasets and research articles that inspired this project.