Inspiration
Navigating public administration can be daunting due to complex processes and overwhelming choices. Our goal was to simplify this experience using AI, improving accessibility and efficiency.
What it does
Our AI-powered recommendation system predicts the most relevant administrative processes for users based on their input. It streamlines public services by reducing search times and improving accuracy.
How we built it
We used an LSTM model trained on public administration datasets to identify patterns and predict relevant processes. Python was our primary language, and we leveraged libraries like TensorFlow and Pandas for modeling and data manipulation.
Challenges we ran into
One challenge was cleaning and structuring diverse administrative data for training. Another was ensuring the model’s predictions were accurate and context-aware, given the complexity of some processes.
Accomplishments that we're proud of
We successfully built a functional AI model that can predict administrative processes with a high enough accuracy. This achievement demonstrates the potential of AI in solving real-world public service challenges.
What we learned
We deepened our understanding of LSTM models and their applications, improved our skills in data preprocessing, and learned how to adapt AI solutions to specific public sector needs.
What's next for RDL recommendation system
We aim to enhance the model’s accuracy by integrating more comprehensive datasets and expanding its scope to include multilingual support. We also plan to collaborate with public administrations for real-world implementation.
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