Language
C#
Tool Type
Desktop app
License
AM331-A3
Version
1.0.0
Inter-American Development Bank
The Atypical Data Classifier is an application designed to detect anomalous data in household surveys and determine eligibility for social programs in Colombia. This tool allows you to view the values of the variables and their scores for households classified as irregular, facilitating detailed analysis. It is a multiplatform web app that can be used on different devices and operating systems, being a valuable resource for data analysis in the social context.
The Atypical Data Classifier solves the problem of identifying and managing anomalous data in household surveys, crucial for the effective allocation of resources in social programs. It facilitates the detection of irregularities and improves decision making for error correction and training of field personnel.
Automated classification of atypical data patterns. Focus on municipal and household data irregularities. Enhanced data analysis for accurate decision-making. Useful in policy development and urban planning.
Developed with HTML, C#, JavaScript, and CSS, it targets web-based data analysis applications. Its structure allows the detection and classification of outliers through an online interface.
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This image shows a web form titled "Ficha #565919 - Hogar#1" with sections B and C for entering data about the dwelling and household, suggesting a survey or census tool.
The image shows a web table with departments and codes, alongside "Processed Households" and "Irregulars" indicators. Example: Code 5 has 106 processed and 16 irregulars. Buttons to view details.
This image displays a web interface for tracking "Irregular Households" by file number, household number, processing date, and provides a "View" option for details.
Presents the IDB tool that uses machine learning to detect anomalous data in household surveys.
Details of the IDB project to improve poverty classification tools in Colombia (Sisbén IV).