"Utilizing Machine Learning Techniques to Evaluate Risk Factors in Agricultural Insurance"
- Author
- Chingadayi Collen
- Title
- "Utilizing Machine Learning Techniques to Evaluate Risk Factors in Agricultural Insurance"
- Abstract
-
ABSTRACT
The Project explores the application of the Support Vector Machine (SVM) algorithm in assessing risk factors in agricultural insurance. SVM, a machine learning model known for its robustness and effectiveness in classification tasks, is utilized to analyze and predict the likelihood of various risks associated with agricultural ventures. By processing historical data and identifying patterns, the SVM algorithm can assist insurers in determining the level of risk posed by different factors, such as climate variability, crop types, and market financial status, insurance amount. This assessment is crucial for the development of accurate insurance policies that can protect farmers against potential losses, ensuring the sustainability of agricultural production. The project's findings could significantly contribute to the precision and reliability of agricultural insurance models, ultimately supporting the agricultural sector's resilience to uncertainties.
- Date
- JUNE 2024
- Publisher
- BUSE
- Keywords
- Support Vector Machine, algorithm in assessing risk factors in agricultural insurance, agricultural ventures
- Supervisor
- Mr. Chikwiriro
- Item sets
- Department of Computer Science
- Media
-
Chingadayi Collen
Part of "Utilizing Machine Learning Techniques to Evaluate Risk Factors in Agricultural Insurance"