Today, our primary focus is on implementing anomaly detection for our economic indicator dataset. Anomaly detection is a powerful statistical approach aimed at uncovering irregular patterns that deviate from expected behavior, and these outliers can often yield valuable insights. In essence, you can think of this process as akin to finding needles in a haystack. In the context of our economic data, these ‘needles’ may represent unusual spikes or dips in indicators such as unemployment rates or hotel occupancy. Detecting these anomalies holds significant importance as they could potentially signify significant economic events, shifts, or even errors in the data collection process. To accomplish this task, we are utilizing the Isolation Forest method, an advanced algorithm well-suited for identifying anomalies within intricate datasets. This technique proves especially effective when dealing with large, multidimensional data, aligning perfectly with our specific objectives