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Machine learning and data mining are research areas of computer science whose quick development is due to the advances in data analysis research, growth in the database industry and the resulting market needs for methods that are capable of extracting valuable knowledge from large data stores. A vast amount of research work has been done in the mul...
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... there are multiple input variables, the procedure is referred as multiple linear regression. Liner regression is applicable in some of the most popular applications of Linear regression algorithm are in financial portfolio prediction, salary forecasting, real estate predictions and in traffic in arriving at ETAs. Figure 3 shows an example of linear regression model. One of the most commonly used regression techniques in the industry which is extensively applied across fraud detection, credit card scoring and clinical trials, wherever the response is binary has a major advantage. ...
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... there are multiple input variables, the procedure is referred as multiple linear regression. Liner regression is applicable in some of the most popular applications of Linear regression algorithm are in financial portfolio prediction, salary forecasting, real estate predictions and in traffic in arriving at ETAs. Figure 3 shows an example of linear regression model. One of the most commonly used regression techniques in the industry which is extensively applied across fraud detection, credit card scoring and clinical trials, wherever the response is binary has a major advantage. ...
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Machine learning and data mining are research areas of computer science whose quick development is due to the advances in data analysis research, growth in the database industry and the resulting market needs for methods that are capable of extracting valuable knowledge from large data stores. A vast amount of research work has been done in the mul...
Citations
... Text mining has been also utilized in code mining of other fields such as software engineer. Those techniques follows a data-driven approach to recover names by searching in a large corpus of data (Tran H., 2019) and to understand the code semantic . ...
... Overfitting is the tendency of data mining techniques to generate models which tailor to training data without generalization to previously unseen data (Provost and Fawcett, 2013) Different supervised machine learning algorithms are employed to preform prediction: Decision Tree, Neural Network and knn which were then evaluated and the one which yields highest accuracy level will be chosen to formulate the final model (Reiman., 2018) -Decision Tree: is one of the most widely used and practical supervised machine learning method. It builds models in the tree-like graph, which breaks down data into subsets while at the same time, an associated decision is incrementally built (Tran., 2019). Each node represents an attribute, each brand defines a rule, and each leaf represents an outcome which altogether represents classification rules. ...
... -Naïve Bayes: is a set of supervised learning algorithms based on the assumption of conditional independence over training data set (Tran., 2019). Naïve Bayes works well with a large dataset in a complex context. ...
Social media is seen as a platform where people freely express their opinions about any matter, thus, generating a massive amount of user-generated content. Twitter undoubtedly has held its firm position among all social networking sites with an exponential number of users every year. Many studies were carried out by investigating the power of Twitter data in the health care industry, politics, sports, and music industry. Over the last five years, the music industry has experienced a shift in the way people listen to music since the introduction of online streaming music. Music lovers are prone to interact with their favorite songs and artists through social media, which provides enormous troves of insight not on just individual song and artists but also on how music consumers perceive any song. Therefore, many kinds of research have been carried out to investigate the impact of Twitter on forecasting songs revenue. However, there are only two studies that aimed to explore the predictive power of Twitter to song performance. This paper shed some light on this little-recognized topic by evaluating Twitter data in forecasting song popularity, which is demonstrated via the Billboard Top 100 chart.
The results indicated that while Twitter data can be utilized as a predictor of song popularity, incorporating Twitter and Billboard information (number of weeks the songs presented in the chart) enhance chart prediction than sole Twitter data. Findings of this study are beneficial to the music industry to discover song performance by real-live update trends on social media in order to propose an appropriate strategy for hit and non-hit songs.
