Time series anomalies must be recognized in various areas due to increasing dataset complexity. This research looks towards anomaly detection training utilizing interactive language models. Our goal is to use a simple tool to assist data analysis teams in understanding these approaches. Moving averages, Z-scores, percentiles, Isolation Forests, Local Outlier Factors, autoencoders, and Autoregressive Integrated Moving Average (ARIMA) are among the techniques we use. We thoroughly evaluate each strategy to determine its advantages and disadvantages. For accurate anomaly detection, Python solutions focus parameter customization and visualization. The interactive language model shows promise as a teaching tool, but further research is needed to demonstrate its usefulness in anomaly detection and data processing.