"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

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,

Agricultural insurance

Supervisor

Mr. Chikwiriro