Introduction to Machine Learning using various Optimization Techniques - Brossura

Bakshi, Ms Gursimran Kaur

9781078241021: Introduction to Machine Learning using various Optimization Techniques

Brossura

ISBN 10: 1078241023 ISBN 13: 9781078241021

Casa editrice: Independently published, 2019

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Buying or selling a property is a financial as well as an emotional undertaking. In this advanced era, these processes can be addressed differently than earlier, with more accuracy and optimization into them. In traditional times, real estate domain has been slow to embrace the recently emerged techniques. So, it’s a high time to start. Technologies such as machine learning can bring tangible benefits to all the parties involved. These may involve sellers, renters, buyers and tenants as well as brokers and agents. Therefore, initially feature dimension reduction using KPIs have been applied to reduce features space without losing the accuracy of classification. Here, unitary method has been used for selecting basic features from primary (self-created) dataset and secondary (taken from Kaggle website) datasets. Once the feature reduction is performed, the classification is applied based on J48 Decision tree and Support Vector Machine (SVM) classifier. From there on, the achieved information is changed into an arrangement issue that states whether the property has been acquired or not. To prepare the order information, J48 and SVM has been executed. In spite of the fact that, these models perform altogether to order land acquiring, at the same time, experience the ill effects of the parameter tuning issue. This issue has been settled by considering the outstanding meta-heuristic improvement strategy i.e., NSGA-III. It iteratively upgrades the meta-J48 model to enhance the classification rate by thinking about change using mutation and crossover operations. The acquired arrangements are non-dominant in nature, consequently, proposed model can give better accuracy as well as different parameters simultaneously. Broad experiments have been performed. It has been discovered that the proposed method beats as far as Accuracy, True Positive Rate, True Negative Rate, Precision and F_Measure. Consequently, the proposed strategy is relevant for ongoing land clients.

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