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The concerned work provides an in-depth analysis of the various aspects of text classification and regressions using different machine learning models. The work includes exploratory data analysis in order to determine different insights into the data set. Along with this, data preprocessing has been done to convert the data set into a format that is suitable for the development of the models. Machine learning models used for text classification in the concerned work is Logistic Regression and for regression, the model developed is Linear Regression. The performance of the models has been estimated using different performance metrics and in this way, the complete work has been performed and desired objectives are achieved.
The continuous advancement in the field of science and technology has helped businesses to grow and prosper in the market. With the help of different machine learning algorithms, data sets can be analyzed and various insights can be determined. With the help of these insights, the company can make decisions in a better and more concrete manner. The problem statement that has been considered in the concerned work is to analyses a data set containing text comments and then based on the analysis, develop machine learning models that can be used to make predictions. The rationale for the development of the analysis is that with the help of text classification, the acceptance status of the peer reviews can be adequately done. The process of development of the concerned work is challenging in nature as the text documents present in the data set are not clean and thus in order to get desired results, it is important that the text is cleaned before the development of the model.
The existing works which are similar to the concerned work provide an in-depth analysis of the various aspects of text classification and regression techniques. These works, however, do not provide the idea of the steps involved in it and the reasons behind the development of those steps to get adequate results. The concerned work provides a detailed evaluation of the different steps that have been performed for the development of the models and the reasons that are related to their execution. The data set that has been taken into consideration for the analysis is a peer review generated from the International Conference of Learning Representation between the years 2017 and 2020. The data set contains five columns named review_id, text, confidence_score, review_score and acceptance_status. The total number of records present in the data set is 6119. The column text contains the details of the documents and is a combination of the title of the paper, its abstract, review comments and final acceptance or rejection comments. The review score ranges from 1 to 10 and there are two acceptance statuses that are accepted or rejected. In order to develop the model, data preprocessing has been done. The presence of null values in the data set is determined with the help of heatmap and the IsNull function. The ML algorithms that have been developed using the given data set are Logistic Regression for classification purposes and Linear Regression for numerical prediction. The reasons behind the selection of these models are that they are simple to implement and interpret and they can be very efficiently used for training purposes.
The social norms are the protocols that are related to society and they are required to be followed in order to ensure the quality of work (Kadhim, 2019). The creation of the concerned work has been done by following all the social norms and the data is collected from a safe and secure place. It has been ensured that the acknowledgement is given to the source from where the data has been extracted for the given work.
The preparation of the concerned work has been done by following all the ethical standards of the work. The data set has been collected from the peer reviews with proper permission. The creation of the ML models would have an impact on the community as with the help of this, the insights from the text can be determined and p
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