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The aim of the project is to develop a Machine Learning model to perform predictive analytics on the banking dataset. The banking data set consists of details about customers like and whether the customer will buy a product provided by the bank or not. The data set is obtained from University of California Irvine Machine Learning Repository. This data set is used to create a binary classification...
Intrusion is one of the most serious problems with network Security, as new types of intrusions are getting much more challenging to detect. Large amount of network traffic has been generated due to the use of internet; most of the generated traffic is in the format which cannot be used directly to arrive at meaningful information. The cleansing and labeling of data each time needs a considerable...
Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human-intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised...
As data keeps growing, Big Data starts to be everywhere, and there is almost an urgent need to make sense of this data. This is why Machine Learning has become crucial as it aids in improving business, decision making and it has the potential to provide solutions for a wide range of problems in computer science and other fields. Machine Learning (a.k.a. Data Mining or Predictive Analytics) algorithms...
In this paper we explore the possibility of automatic model selection in the supervised learning framework with the use of prediction intervals. First we compare two families of non-parametric approaches of constructing prediction intervals for arbitrary regression models. The first family of approaches is based on the idea of explaining the total prediction error as a sum of the model's error and...
Software fault prediction models play an important role in software quality assurance. They identify software subsystems (modules,components, classes, or files) which are likely to contain faults. These subsystems, in turn, receive additional resources for verification and validation activities. Fault prediction models are binary classifiers typically developed using one of the supervised learning...
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