![]() ![]() The problem in this project was a bank received an influx of loan applications and we needed to build and apply a classification model to provide a recommendation on which loan applicants can be approved by the bank. We have also learnt how to score, compare and interpret results of non binary modules. Non-Binary Classification Models: In this section we have learned and built to compare boosted models and forest models.We have learned how to use Stepwise to automate predictor variables selection and how to score and compare the models and interpret the results. Binary Classification Models: In this lesson, we have learned to build logistic regression and decision tree models.Classification Problems: In this lesson, we have learned how classification modeling differs from numeric data.However, this lesson was a bit short with only 3 sub lessons. ![]() This lesson was as interesting as all the past lessons were. The proposed model is compared with other classification algorithms and the results show that our proposed model outperforms other models in terms of accuracy and area under the curve.You can see the example answers of the project below: Lesson 4: Classification Models This technique results in using fewer training set yet producing superior results. The network contains a separate classification module for churn prediction. Attention mechanism makes the network focus on the features that highly contributes to the target prediction. The Autoencoder network represents the data in latent space representation. To overcome these problems, in this paper, we are proposing a feature extraction model based on Autoencoder with attention mechanism. They require manual feature extraction, or the model cannot balance skewed datasets. But most of these models have several shortcomings. Classification models are often used for churn rate prediction. Effective churn rate prediction is a critical task. In Telecom, customer churn is to find whether the customer is going to leave the service of the current operator or not. Especially in Telecom sector it can help find the customer churn rate. User Behaviour Analysis gives valuable insights for customer management. In addition, an autoencoder is shown to be effective for anomaly detection. Using these more recent datasets, deep neural networks are shown to be highly effective in performing supervised learning to detect and classify modern-day cyber attacks with a high degree of accuracy, high detection rate, and low false positive rate. Deep neural network models are trained using two more recent intrusion detection datasets that overcome limitations of other intrusion detection datasets which have been commonly used in the past. Second, an autoencoder is used to detect and classify attack traffic via unsupervised learning in the absence of labeled malicious traffic. ![]() First, a feedforward fully connected Deep Neural Network (DNN) is used to train a Network Intrusion Detection System (NIDS) via supervised learning. ![]() The contribution of this work is two-fold. The focus for this Thesis is on classifying network traffic flows as benign or malicious. Organizations must rely on new techniques to assist and augment human analysts when dealing with the monitoring, prevention, detection, and response to cybersecurity events and potential attacks on their networks. With more security tools and sensors being deployed within the modern day enterprise network, the amount of security event and alert data being generated continues to increase, making it more difficult to find the needle in the haystack. As the scale of cyber attacks and volume of network data increases exponentially, organizations must develop new ways of keeping their networks and data secure from the dynamic nature of evolving threat actors. ![]()
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