Student Stress Factors: Bayesian Analysis Project

Project Overview

This project applies Bayesian statistical methods to analyze various factors contributing to student stress. It includes a detailed exploration of data involving student demographics, academic pressures, and personal lifestyle, using Bayesian models to infer the impact of these factors on stress levels.

Features

• Bayesian Statistical Modeling: Utilizes advanced Bayesian methods to estimate the effects of multiple stress-related factors.
• Data Visualization: Features graphical representations of data and model outcomes to facilitate understanding of key insights.
• Interactivity: Includes interactive elements to manipulate model parameters and visualize different scenarios.

Requirements

To run this notebook, you will need:

• Python 3.7 or higher
• Libraries: pymc3, arviz, matplotlib, seaborn, pandas, numpy
• Jupyter Notebook or JupyterLab

Installation

Clone the repository to your local machine:

git clone https://github.com/Apoorva-Udupa/Student_stress_factors_BayesianModelling.git
cd student-stress-factors-bayesian

All contributions are welcome!