A probabilistic modeling project focusing on uncertainty in real-world sensor readings. A Bayesian linear regression framework is used to simulate sensor data and compute predictive distributions with credible intervals. The model produces interpretable predictions with quantified uncertainty, improving trust and decision-making in sensor-based systems.