https://github.com/drkbluescience/up-school-bitexen-datathon-2024_v2
This repository contains analyses and visualizations of gender-related statistics, awarded second place in the UP School & Bitexen Women in Datathon 2024
https://github.com/drkbluescience/up-school-bitexen-datathon-2024_v2
classification dataanalysis tabular-data visualization
Last synced: 6 months ago
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This repository contains analyses and visualizations of gender-related statistics, awarded second place in the UP School & Bitexen Women in Datathon 2024
- Host: GitHub
- URL: https://github.com/drkbluescience/up-school-bitexen-datathon-2024_v2
- Owner: drkbluescience
- Created: 2024-11-02T10:40:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-16T14:51:01.000Z (over 1 year ago)
- Last Synced: 2025-01-14T11:33:07.376Z (over 1 year ago)
- Topics: classification, dataanalysis, tabular-data, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.66 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **UP School & Bitexen Women in Datathon 2024 (Version 2)**
# Awards
This project was awarded second place in the **UP School & Bitexen Women in Datathon 2024**.
For more details, visit the [LinkedIn post](https://www.linkedin.com/posts/up-school_women-in-datathon-kazananlar%C4%B1-belli-oldu-ugcPost-7187052456030195712-KGJx?utm_source=share&utm_medium=member_desktop).
# Abstract
This notebook encompasses various analyses and visualizations of gender-related statistics from multiple datasets, including labour force participation rates, maternal mortality rates, and women's representation in top-income groups. Insights derived from these visualizations aim to highlight disparities and trends in gender equality across different countries and regions. For the dataset used in this analysis, visit the Kaggle page: [UP School & Bitexen Women in Datathon Dataset](https://www.kaggle.com/datasets/upschoolio/up-school-women-in-datathon-dataset). Additionally, classification modelling was conducted on the Placement dataset to explore factors influencing placement outcomes.
To understand women's economic participation and entrepreneurship comprehensively, the project also examines factors such as the gender wage gap, time dedicated to unpaid care work, and the Women Entrepreneurship Index. By analyzing data on the ratio of female-to-male labour force participation and indicators of women’s entrepreneurial activities, notable patterns emerge: developed countries generally exhibit higher levels of female labour force participation and entrepreneurship. In contrast, developing nations tend to show lower values in these areas. A positive correlation was observed between female labour force participation rates and the Women Entrepreneurship Index, suggesting a potential interplay between these factors. Visualizations further illustrate the intersection between unpaid care responsibilities, income disparities, and female economic engagement, shedding light on structural barriers faced by women globally.
The findings presented in this project aim to inform policymakers and stakeholders on the current landscape of gender equality in economic participation, with actionable insights to support female entrepreneurship and workforce engagement worldwide.
*Note: This is the second version of the project, which received second place in the UP School & Bitexen Women in Datathon 2024. The first version can be viewed [here](https://www.kaggle.com/code/enisezengin/2nd-place-v1-upschool-bitexen-datathon).*