Application of Data Analysis and Soft Computation to Model the Need of Crop Insurance for the Indian Farmers
Keywords:Crop insurance, Exploratory data analysis, Correlational analysis, Threshold value computation, Machine learning model
A very high level of uncertainty is associated with agriculture in the form of natural, social and human-related actions. Farmers incur heavy losses whenever their farmlands are affected. Crop insurance is the answer to such losses that existed as an institutional response to the nature-induced risk. Although crop insurance is advantageous, there are some drawbacks in implementation because of which the farmers are not willing to opt for it. Hence, there is a demand to model the need of crop insurance for Indian Farmers. For this purpose, the paper is divided into two parts. The first part involves applying exploratory data analysis (EDA) to correlate the factors with the farmers' responses. A correlational analysis is also conducted to study the relationship between different factors. The second part involves the application of three machine learning (ML) algorithms, namely, Logistic Regression (LR), Random Forest (RF) and Gradient Boost classifier (GB) to meet the aims and objectives of the paper. The performance of the three ML models is scrutinised based on their accuracy and predictive capability. The ML model with the best performance is chosen to determine the threshold value below which there is a high likelihood of a farmer opting for crop insurance. The strength of the proposed approach is its practical applicability.
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