Utilizing ECL and Machine Learning Techniques To Analyze Factors for Why Kids Go Missing

The HelpingSavingKids project aims to analyze the correlation between missing children cases and social factors such as unemployment, education, poverty, and population in various locations. Additionally, it explores how additional information sources can enhance the National Center for Missing & Exploited Children (NCMEC) feed to aid in locating missing children.

1. Correlation Analysis: Determine if there's a significant relationship between the locations where children are reported missing and the social conditions of those areas. This involves statistical analysis of public data sets to identify patterns and insights.

2. Enhancing NCMEC Feed: Propose how integrating data on local emergency services and community resources (like fire and police stations, hospitals, churches, food banks) can provide valuable context and support to the existing NCMEC feed, potentially aiding in recovery efforts.

• Utilize HPCC Systems for data processing and ECL for data analysis.
• Aggregate and analyze data from various public sources.
• Develop a model to predict missing children cases based on identified correlations.
• Suggest improvements for the NCMEC feed based on analysis.

This project encourages contributions in data analysis, model development, and strategies to enhance the NCMEC feed. Contributions should aim to provide insightful, actionable information to help find missing children more effectively.

For more information or to contribute, please contact mnaik43@gatech.edu, royceaden@gmail.com, nikkikunthu@gmail.com.

This project is a collaborative effort to leverage data for social good, focusing on the critical issue of finding missing children.