Global Regulatory Analytics Model

The Global Regulatory Analytics Model is a data analytics and machine learning project designed to analyze regulatory events and their impact across different regions and sectors. It processes global datasets to uncover patterns, classify regulatory changes, and provide predictive insights that can support decision-making in finance, compliance, and governance. The project highlights advanced data analysis, machine learning, and visualization skills.

Features

• Data Preprocessing – Cleaning and structuring global regulatory datasets
• Classification Models – Categorizing regulatory announcements by type or impact
• Predictive Analytics – Forecasting regulatory trends and potential effects
• Exploratory Data Analysis (EDA) – Understanding distributions and correlations
• Visualization Dashboards – Presenting results through graphs and charts

Tech Stack

• Language: Python
• Libraries: pandas, scikit-learn, numpy, matplotlib, seaborn
• Machine Learning: Logistic Regression, Random Forest, SVM (depending on implementation)
• Environment: Jupyter Notebook / Google Colab
• Version Control: Git & GitHub

Installation & Setup

1. Clone the repository

   git clone https://github.com/yourusername/Global-Regulatory-Analytics-Model.git
   cd global-regulatory-analytics-model
   

2. Set up a virtual environment (optional)

   python -m venv venv
   source venv/bin/activate   # Mac/Linux
   venv\Scripts\activate      # Windows
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Open the notebook

   jupyter notebook
   

Then open the provided .ipynb file.

Usage

• Load the regulatory dataset(s) into the notebook
• Run preprocessing scripts to prepare the data
• Train classification or predictive models
• Evaluate models using accuracy and precision metrics
• Visualize findings to interpret regulatory impacts

Support

For help, please contact the project maintainers.

Contributing

This project is not open for contributions.

Authors and Acknowledgment

Developed by the KalApache Team.

• Abitan, Julianne Therese
• Aquino, Jan Dolby
• Cahanap, Jerilyn Louise
• Caparas, Joaquin Gabriel
• Escaño, Nichole Jhoy
• Mandac, Minette Victoria
• San Miguel, Chloe’ Lee

Project Status

This project has been submitted and graded.

Google Collab Link

KalApache-Google Collab Link

Datasets

KalApache-Datasets Link

Github Link

KalApache-Github Repository Link