This project, Technology News Insights Engine, was developed as part of the AI Studio Challenge with Accenture and Breakthrough Tech. The goal was to explore and analyze technology news articles, providing insights through detailed visualizations and analysis.

Objective 1: Analyze tech articles using knowledge graphs

Objective 2: Create cypher queries to gain more insight on the data

Objective 3: Learn more about how we can apply ML techniques to industry practices

The project utilized:

Google Colab for running Python-based notebooks, enabling accessible and efficient computation.

• Libraries such as pandas, matplotlib, seaborn, and networkx for data manipulation and visualization.
• Data Exploration: Focused on understanding the dataset and identifying patterns.
• Graph Creation: Visualized trends, correlations, and key insights using tools like bar graphs, line graphs, and network diagrams.
• Results and Key Findings: Sentiment varies across industry. We closely analyzed Banking, Capital Markets, and Communication + Media. Click to view our presentation!
• Potential Next Steps: Incorporate additional datasets to expand the scope of analysis. Enhance visualizations with interactive dashboards.

Clone the repository:

git clone https://github.com/yourusername/yourproject.git

Open the notebooks in Google Colab:

Navigate to https://colab.research.google.com/. Upload the desired notebook (.ipynb file) from the project. Install dependencies directly in Colab.

Usage Open the notebook in Google Colab and execute the cells sequentially.

Adjust parameters in the notebook to customize the analysis.

We welcome contributions to improve this project! Follow these steps:

• Fork the repository.
• Make your changes and ensure they align with the project goals.
• Submit a pull request with a detailed description of your changes.

This project is licensed under the MIT License. Feel free to use, modify, and distribute this project as per the terms of the license.

Team Members: Oyu Enkhbold, Sheryl Lai, Mansa Patel, Uchenna Justin, Ariel Trusty Tools and Libraries: pandas, matplotlib, seaborn, Google Colab. Dataset Source: Hackernoon, Hugging Face