This project performs a detailed analytical study of unemployment rate data, with a focus on trend analysis, seasonal pattern detection, and evaluating the impact of COVID-19 on employment levels. The workflow was implemented entirely in Python, leveraging industry-standard data analysis libraries. The dataset was processed using Pandas for data cleaning operations such as handling missing values, filtering, grouping, time-based indexing, and aggregation. NumPy was used for numerical computations and array-based operations. Exploratory Data Analysis (EDA) involved statistical summaries, correlation checks, and time-series trend inspection. For visualization, Matplotlib and Seaborn were used to generate line plots, distribution plots, comparative trend graphs, and COVID-period impact visualizations to clearly interpret fluctuations in unemployment rates over time. The project emphasizes structured data preprocessing, analytical transformation, and visual storytelling to extract actionable insights that could support economic forecasting and policy-level decision-making.