Project Objective:

• Analyze US states labor force unemployment and participation trends over time.
• Understand the differences in participation rates between states.
• Use Time-Series data to identify long term trends and regional Differences.
• Clear and Comparative visualization assist in presenting Insights.

Problem Statement:

• Labor force Participation and unemployment trends differ significantly throughout the states across U.S states.
• Effective Analysis is necessary to interpret raw economic data that is complex to understand.
• Policymakers and Analysts require clear insights to understand workforce behaviour.
• This Project Turns Raw Data into insightful visuals.

Project Workflow:

• State Level Economic Data was acquired using official FRED API.
• Utilized Pandas to clean and process time-series data.
• Standardized Column Names and handled missing values.
• EDA(Exploratory Data Analysis) was performed to uncover hidden patterns.
• Matplotlib and Plotly were used to visualize state-wise comparison.

Key Insights:

• During the 2008-09 Financial crisis, All the U.S states had their unemployment rate peak with some exceeding around 12-15%.
• California, Michigan and Nevada were the most affected by labor market stress.
• While Unemployment was high, labor force participation rates displayed a gradual upward or stabilizing trend.
• By 2010, a number of states began to show early signs of labor market recovery.

Tools & Skills:

• Python
• Data Cleaning and Analysis: Numpy & Pandas
• Visualization: Matplotlib & Plotly
• Time-Series Analysis
• EDA(Exploratory Data Analysis)