This project performs a comprehensive analysis of electric power consumption and population growth in the United Kingdom. Using Python's pandas and matplotlib libraries, it first reads and visualizes electric power consumption data over time, fitting an exponential growth model. The fitted model is then used to forecast future trends, and confidence intervals are plotted. Additionally, clustering techniques, specifically Affinity Propagation are used to identify patterns in the population growth data. The resulting clusters are visualized on a scatter plot, with cluster centers marked for interpretation. This integrated approach provides insights into the relationships between electric power consumption, population growth, and potential clusters within the dataset.
