Countries Condition of Forestation and Trees Percentage using Machine learning

Abir Abdullha, Yeasin Habib, Md. Raisul Islam Masum, AKM SHAHARIAR AZAD RABBY
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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.
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使用机器学习的国家造林状况和树木百分比
大多数国家现在都处于植树造林的危险境地,有些国家则处于发达的植树造林状态。因此,造林和树木百分比预测就是对各国的造林和树木百分比状况进行预测。本文研究了一种预测国家状况的机器学习模型。我们还使用逻辑回归、支持向量机和朴素贝叶斯对矩阵进行了条件预测。我们还发现了逻辑回归、支持向量机、朴素贝叶斯、Ada增强分类器、决策树、人工神经网络、线性判别分析、梯度增强分类器、MLP分类器的精度,以找到我们的最佳精度,并与我们的数据进行比较。我们给出了所选算法的细节。我们收集了一些以前的数据和现在的数据,并将它们进行比较,以预测该国的状况。我们使用一些条件和逻辑来进行机器学习。通过逻辑回归,SVM和朴素贝叶斯将向我们展示所选国家的预测和情况。
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