dc.contributor.author | Abdullha, Abir | |
dc.contributor.author | Habib, Yeasin | |
dc.contributor.author | Masum, Md. Raisul Islam | |
dc.contributor.author | Rabby, Akm Shahariar Azad | |
dc.date.accessioned | 2021-09-01T09:34:33Z | |
dc.date.available | 2021-09-01T09:34:33Z | |
dc.date.issued | 2020-06-16 | |
dc.identifier.uri | http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6098 | |
dc.description.abstract | ,Most countries are now in a dangerous place for forestation and some are in developed forestation. So forestation and trees percentage prediction are to predict the condition of the countries about their condition of forestation and tress percentage. The paper is about a machine learning model to predict the countries condition. We used logistic regression, SVM AND Naive Bayes to predict the condition also for matrix. we also find the accuracy of logistic regression, SVM, Nave Bayes, Ada boosting classifier, Decision tree, ANN, Linear Discriminant Analysis, Gradient Boosting Classifier, MLP Classifier to find our best accuracy and compare with them with our data. we give details of selected algorithms. We collected some previous data and present data and comparing them to predict the condition of the country. we use some conditions and logic for machine learning. By logistic regression, SVM and Nave Bayes will show us the prediction and condition of those chosen countries. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Proceedings of the 2019 8th International Conference on System Modeling and Advancement in Research Trends, IEEE | en_US |
dc.subject | Logistics algorithm | en_US |
dc.subject | SVM algorithm | en_US |
dc.subject | Nave bayes algorithm | en_US |
dc.subject | Details about algorithms | en_US |
dc.subject | Compare algorithm accuracy | en_US |
dc.subject | Condition of countries in forestation | en_US |
dc.title | Countries Condition of Forestation and Trees Percentage Using Machine Learning | en_US |
dc.type | Article | en_US |