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West Nile Virus Prediction Based on Data Mining

Informatization Office, Fudan University, Shanghai 200433, P. R. China

Mailing Address: Room 502, Building 4, Lane 408, Guoding Road, Yangpu District, Shanghai, P. R. China.

https://doi.org/10.1142/S0219265921500250Cited by:0 (Source: Crossref)

Abstract

This paper performed some exploratory data visualization on this data set. The nature and representation of input data was found out and the preliminary feature selection was conducted in this step. And this paper performed data preprocessing and feature engineering on this data set, which had critical importance of the accuracy of prediction results. The paper built multiple regression models on the missing values prediction in the testing set. The paper implemented various data mining algorithms to build predictive models, including Gaussian Naive Bayes classifier, K-Nearest Neighbors (K-NN) algorithm, Multi-layer Perceptron (MLP), Logistic regression, random forest and XGBoost. After the experiments, XGBoost classifier could give the best result among all the models.

Keywords:

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