The company wants to determine which store format each of the new stores should have. The grocery store chain has 10 new stores opening up at the beginning of the year. You've been asked to provide analytical support to make decisions about store formats and inventory planning. This is beginning to cause problems as stores are suffering from product surpluses in some product categories and shortages in others. Up until now, the company has treated all stores similarly, shipping the same amount of product to each store.
Currently, all stores use the same store format for selling their products. Your company currently has 85 grocery stores and is planning to open 10 new stores at the beginning of the year. Once you complete all three tasks, please submit the project as a PDF. The capstone project has three main tasks, each of which requires you to use skills you developed during the Nanodegree program. 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.Combining-Predictive-Techniques Predictive Analytics for Business Nanodegree Capstone Project Overview 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.
#Udacity combining predictive techniques task3 manual
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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