Machine Learning in Finance

Description

This repository contains coursework and projects exploring applications of machine learning in quantitative finance.
It includes Jupyter notebooks, project tasks, and reports that cover topics such as predictive modeling, feature engineering, model evaluation, and financial forecasting.

Table of Contents

• Installation
• Usage
• Features
• Configuration
• License

Installation

1. Clone the repository:

   git clone https://github.com/Smartpero/Machine_Learning_In_Finance.git
   cd Machine_Learning_In_Finance

2. (Optional but recommended) Create a virtual environment: python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install dependencies. A typical set might include: pip install jupyter numpy pandas scikit-learn matplotlib seaborn

4. Install rrequirements: pip install -r requirements.txt

Usage

• Launch Jupyter and open the notebooks (e.g. Notebook 1.ipynb, Notebook 2.ipynb, etc.) to explore machine learning models applied to financial datasets.

• Follow the project tasks in the respective PDFs for guided exercises.

• Review project reports for conclusions and findings.

• Use the notebooks for hands-on experiments: try new feature sets, tune model hyperparameters, compare models, visualize results.

Features

• Multiple Jupyter notebooks demonstrating different ML techniques in finance (regression, classification, time-series forecasting, etc.)

• Project task descriptions that guide applied work and learning

• Project reports summarizing results, insights, and model evaluation

• Visualizations of model performance, error metrics, and financial data features

• Emphasis on comparing different algorithms and validating models

Configuration

• Setting : Description
• Python environment : Ensure correct Python version (e.g. 3.8+) and virtual environment activation
• Dependencies : Required libraries (e.g. scikit-learn, numpy, pandas, matplotlib, seaborn)
• Notebook paths : Notebooks expect certain directory structure; keep relative paths consistent for data/assets if used
• Dataset / data sources : If using external or custom datasets, ensure proper file placement or update paths inside notebooks
• Model/Hyperparameter settings : Notebooks may have tunable settings for model type, hyperparameters, feature sets — adjust as needed for experiments

License

This project is for educational purposes and is licensed under the MIT License (or whichever you choose).