Analyzing-Unemployment-Rates-During-COVID-19

Project Overview

This project involves analyzing unemployment rates during the COVID-19 pandemic. The goal is to understand the trends and forecast future unemployment rates using time series analysis. The project includes data cleaning, visualization, and ARIMA modeling.

Key Highlights

• Data Cleaning and Preprocessing: Handled missing values and formatted date columns.
• Data Visualization: Created visualizations to depict unemployment rate trends over time.
• Time Series Analysis: Applied the ARIMA model to forecast unemployment rates.

Tools and Libraries

• Python: The primary programming language used.
• Libraries:
  • pandas: For data manipulation and cleaning.
  • matplotlib: For creating visualizations.
  • seaborn: For advanced data visualization.
  • statsmodels: For time series modeling and forecasting.

Dataset

The dataset used in this project includes columns such as:

• Region: The geographical region.
• Date: The date of the record.
• Frequency: The frequency of the data (e.g., monthly).
• Estimated Unemployment Rate (%): The estimated unemployment rate.
• Estimated Employed: The number of employed individuals.
• Estimated Labour Participation Rate (%): The labor participation rate.
• Area: The area of the region.

Setup and Installation

1. Clone the Repository

   git clone https://github.com/yourusername/unemployment-rate-analysis.git
   cd unemployment-rate-analysis

2. Install Dependencies

   Make sure you have Python installed. Install the required libraries using pip:

   pip install pandas matplotlib seaborn statsmodels

3. Download the Dataset

   Ensure that you have the dataset CSV file available. Update the file path in the script to point to your dataset.

Running the Analysis

1. Load and Prepare Data

   Make sure to update the url variable in the script with the path to your dataset.

2. Run the Analysis

   Execute the Python script to perform the analysis:

   python unemployment_analysis.py

3. View Results

   The script will generate plots and forecasts that visualize the unemployment trends and predictions.

Code

You can view the complete code for this project in unemployment_analysis.py. The script includes sections for data cleaning, visualization, and time series forecasting.

Contributing

If you'd like to contribute to this project, please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or comments, please reach out to:

• Name: Naman Jain
• Email: namanbhansali59@gmail.com
• LinkedIn: https://www.linkedin.com/in/naman-jain-3247831b7/