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Machine Learning in Finance

Description

This repository explores the application of machine learning techniques in the domain of finance. From credit score predictions to financial product recommendations, dive into various notebooks that demonstrate the power of ML in the financial sector.

Table of Contents

1. Credit Score Prediction
2. DBSCAN Fundamental Analyses
3. Fraud Detection
4. PCA for Trading Strategies
5. Financial Product Recommendation
6. Installation and Setup
7. Contributing
8. License

Credit Score Prediction

Explore the "Credit score.ipynb" notebook to understand how machine learning can be used to predict or analyze credit scores.

DBSCAN Fundamental Analyses

The "Main.ipynb" notebook in this section discusses the application of the DBSCAN clustering algorithm for fundamental analyses in finance.

Fraud Detection

Dive into the "fraud_detect.ipynb" notebook to learn about detecting fraudulent activities in banking or financial transactions using machine learning techniques.

PCA for Trading Strategies

The "PCA_for_stock_portfolio.ipynb" notebook showcases the application of Principal Component Analysis (PCA) for stock portfolio management or trading strategies.

Financial Product Recommendation

Discover how machine learning can be used to build recommendation systems for financial products in the "recommendation engine.ipynb" notebook.

Installation and Setup

git clone https://github.com/amaboh/Machine_learning_in_finance.git
cd Machine_learning_in_finance
# Follow specific setup instructions for each notebook

License

This project is licensed under the MIT License. See the LICENSE.md file for details.

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