This repository demonstrates a proof of concept (PoC) for resolving competency gaps using AI-driven recommendations. By analyzing Learning Needs Analysis (LNA) scores, the system prioritizes competencies and provides tailored learning solutions to address them effectively.

The script provided in this repository performs the following tasks:

1. Merges LNA master data with LNA scores.
2. Uses GPT-3.5-turbo-instruct to generate learning recommendations and relevant learning topics for each competency.
3. Outputs the results to an Excel file for further analysis and implementation.

• Python 3.x
• Pandas library
• OpenAI library
• Google Colab for file downloads (optional)
• LNA master and LNA score data in CSV format

1. Clone the repository:

   git clone https://github.com/yourusername/gap-competency-resolution.git
   cd gap-competency-resolution

2. Install the required libraries:

   pip install pandas openai

3. Prepare your LNA data:

   • Ensure you have lna_master.csv and lna_score.csv files in the repository directory.

4. Set up your OpenAI API key:

   • Replace the placeholder API key in the script with your actual OpenAI API key:

     api_key = "bring your own bitch"

1. Run the script:
2. Download the output file:
   • After the script runs, download the generated learning_recommendations.xlsx file which contains the recommended learning solutions and topics.

This project is licensed under the MIT License. See the LICENSE file for details.

Contributions are welcome! Please fork the repository and create a pull request with your changes.

• OpenAI for providing the GPT-3.5-turbo-instruct model.
• Pandas library for data manipulation.
• Google Colab for file handling.