Inspiration story behind this project:

The technology sector has long been a male-dominated industry, where women have faced various barriers, including gender bias in hiring, retention, and promotion. This inequality has become more apparent during periods of economic downturn, particularly during mass layoffs. Recent data and studies suggest that women have been disproportionately affected by tech layoffs, exacerbating the gender gap in the industry. This paper explores the extent of gender bias in tech layoffs, analyzing the impact on women’s employment and financial stability, and offering insights into the contributing factors behind this disparity.

What it does:

I used Google AI Studio with the Gemini Flash and Gemini 1.5 models, integrating Python scripting and Ib scraping techniques to extract data points. The project focuses on visualizing these data points using Python and generative AI to create insightful graphics.

How I built it:

I leveraged tools like Google AI Studio (Gemini models), combined with Python for scripting and Ib scraping. Python was used to extract data from various sources, while generative AI helped enhance data visualizations.

Challenges I faced:

One of the main challenges was that Google Gemini did not fully integrate with Python libraries to generate visualizations. Despite providing adequate data points, the model struggled to create the required graphs through Python scripting.

Accomplishments I am proud of:

I’m particularly proud of successfully extracting data from 8-10 research articles through Ib scraping. Unlike more structured databases, Ib scraping is complex and requires additional effort. This alloId us to gather unique data points and insights for the project.

What I learned:

Technically, I gained expertise in leveraging generative AI for visualizations and handling complex Ib scraping to extract data. From a research perspective, I discovered that women are disproportionately affected by tech layoffs, facing economic, mental, and professional impacts.

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