Unemployment Analysis With Python
This repository contains the code and data for an Unemployment Analysis Project in India. The project aims to explore and analyze the unemployment rate data for different states and regions in India. It provides valuable insights into the employment scenario across the country.
Dataset The dataset used in this analysis is provided CSV file named Unemployment_Rate_upto_11_2020.csv. The dataset contains the following columns:
State: The name of the Indian state. Date: The date of the unemployment rate data. Frequency: The frequency of the data collection. Unemployment_Rate: The estimated unemployment rate (%) for the state. Employed: The estimated number of employed individuals. Labor_Participation_Rate: The estimated labor participation rate (%) for the state. Region: The region to which the state belongs. Longitude: The longitude of the state's geographical location. Latitude: The latitude of the state's geographical location. Analysis The project includes various data exploration and analysis steps. It covers the following aspects:
Data Cleaning: The initial step involves cleaning and preprocessing the dataset to ensure data quality.
Exploratory Data Analysis (EDA): The EDA phase focuses on understanding the data distribution, identifying patterns, and visualizing trends related to unemployment rates across different states and regions.
Geospatial Analysis: Geospatial visualizations are used to map the unemployment rate data on the Indian map, showcasing the variation in unemployment across states.
Temporal Analysis: The project includes a temporal analysis to study how unemployment rates have changed over time.
Conclusion: The findings and insights derived from the analysis are summarized to draw meaningful conclusions about unemployment patterns in India.
How to Use To replicate the analysis, follow these steps:
Clone the repository to your local machine.
Ensure you have the necessary Python libraries and packages installed, such as pandas, numpy, matplotlib, seaborn, and plotly.
Open the Jupyter Notebook unemployment_analysis.ipynb to view the step-by-step analysis.
The Unemployment_Rate_upto_11_2020.csv file, which is the dataset used for analysis.
Execute the code cells in the Jupyter Notebook to reproduce the data analysis and visualizations.
Contribution Contributions to this project are welcome! If you find any issues or have ideas for improvement, feel free to create a pull request or raise an issue.
License This project is licensed under the MIT License.