The Gender Names Analysis project aims to analyze a dataset that compiles the number of occurrences of male and female names during specific time periods. It calculates the probability of a name based on the total count. The data used in this project comes directly from government authorities, ensuring its credibility.
The specific dataset used in this analysis is:
- US: Baby Names from Social Security Card Applications - National Data, 1880 to 2019
This dataset provides valuable insights into the popularity and distribution of baby names in the United States over the years. It allows you to explore trends in naming conventions, gender-based naming preferences, and the probability of encountering a specific name. The dataset is sourced from government authorities, ensuring its accuracy and reliability. It covers a significant time span from 1880 to 2019, providing a comprehensive view of historical naming trends.
The key attributes in this dataset include:
- Name: String
- Gender: M/F (category/string)
- Count: Integer
- Probability: Float
To run this analysis, you need the following prerequisites:
- Python 3
- Jupyter Notebook (optional)
- Pandas
- Matplotlib (for data visualization)
- Seaborn (for enhanced data visualization)
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This license allows for the sharing and adaptation of the dataset for any purpose, provided that the appropriate credit is given. When using this dataset in your projects or analyses, please make sure to provide proper attribution as per the CC BY 4.0 license.
For more details about the license, visit Creative Commons Attribution 4.0 International License.