Apex

🔍 Project Details Use this:

💡 Inspiration Inspired by the alarming rise in juvenile crime and child neglect, we built an AI tool to help identify children at risk of legal or welfare issues early—supporting timely intervention and care.

🛠️ What it does The model analyzes datasets related to child behavior, environment, and demographics to predict potential juvenile justice involvement or need for welfare support.

🧠 How we built it We used Python with pandas, scikit-learn, and XGBoost. The dataset was preprocessed and multiple models were tested. XGBoost delivered the best results, and metrics like precision, recall, and accuracy were tracked.

🚀 Challenges we ran into Data inconsistency and lack of real-world datasets. We overcame this by engineering relevant features and balancing classes.

✅ Accomplishments A working model with strong predictive power, a clear UI, and the potential to support NGOs and policy-makers.

📚 What we learned How to apply AI to real-world social issues, collaborate fast under pressure, and create high-impact tools with limited resources.

🔮 What's next Integrating this into a web app using Streamlit and expanding to include more welfare categories.

Built With

• jupyter
• matplotlib
• numpy
• pandas
• python
• scikit-learn
• seaborn
• xgboost