Forecasting the Number of Customers Visiting Restaurants Using Machine Learning and Statistical Method

2021 ◽  
pp. 189-197
Author(s):  
Takashi Tanizaki ◽  
Shunsuke Kozuma ◽  
Takeshi Shimmura
Keyword(s):  
Machine Learning ◽  
Statistical Method ◽  
Number Of Customers

scholarly journals Statistics and Machine Learning Experiments in English and Romanian Poetry

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 92
Author(s):  
Ovidiu Calin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Method ◽  
Information Entropy ◽  
English Language ◽  
Quantitative Approach ◽  
Statistical Measures ◽  
Informational Energy ◽  
N Gram ◽  
Average Information

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

scholarly journals Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3831-3834
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Customer Service ◽  
Service Providers ◽  
Mobile Network ◽  
High Likelihood ◽  
Telecom Service ◽  
Number Of Customers ◽  
Mobile Network Operators

Churn has a significant impact on mobile network operators and telecommunications service providers. Many studies on churn have been reported, but no one can say that they can create universal human tools for predicting churn or that we can see all the reasons for it. The purpose of this study is to derive the call behavior factors of churning customers and to find ways to reduce the churn of target customers who exhibit these call behaviors. For this, this study uses decision tree and machine learning for the prediction of churn in telecom service. Based on the analysis results, first, the fact that the total number of customers who have more than 316.7 in churn shows that the higher the number of calls, the higher the chance of churn. Second, among customers with total day minutes above 316.7, those with customer service calls above 8.5 show a high likelihood of churn among complaining customers. The overall accuracy is 91.4%. Among the customers who predicted not to be churned, the accuracy that would not be churned was 92.87%, and the accuracy that was churned was 78.4% among the customers predicted to be churned

The Prediction of 4G Traffic for Customer’s Demand of Lao Telecommunications Company Using Statistical Method

2016 ◽  
Vol 848 ◽  
pp. 111-114
Author(s):  
Chanthasone Boulom ◽  
Soukan Khounpaseth ◽  
Kham Khanthavivone
Keyword(s):  
Statistical Method ◽  
Mean Square Error ◽  
Actual Data ◽  
Mean Square ◽  
Actual Number ◽  
Average Accuracy ◽  
The Future ◽  
The Mean ◽  
Number Of Customers ◽  
Telecommunications Company

This paper aims to study the prediction of 4G traffic for customer’s demand of Lao Telecommunications Company (LTC) using statistical method called Cubic method. The data used in our paper are the actual number of customers and the actual bandwidth used by the customers in each month. Those actual data used for predicting the future number of customers and bandwidth used by the customers are collected from January 2014 to April 2015. The accuracy of the prediction is evaluated by using the Mean Square Error (MSE). In our paper, the derived average accuracy is 98%, the results may be used to considering for improving the throughput of Evolved Packet Core (EPC) there by providing the better service to the customer.

New Hybrid Statistical Method and Machine Learning for PM10 Prediction

2019 ◽  
pp. 142-155
Author(s):  
Suhartono ◽  
Hendri Prabowo ◽  
Dedy Dwi Prastyo ◽  
Muhammad Hisyam Lee
Keyword(s):  
Machine Learning ◽  
Statistical Method

scholarly journals Statistics and Machine Learning Experiments in English and Romanian Poetry

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 78
Author(s):  
Ovidiu Calin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Method ◽  
Information Entropy ◽  
English Language ◽  
Quantitative Approach ◽  
Statistical Measures ◽  
Informational Energy ◽  
N Gram ◽  
Average Information

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

scholarly journals Exploring the Machine Learning Algorithm for Prediction the Loan Sanctioning Process

2019 ◽  
Vol 9 (1) ◽  
pp. 2714-2719
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Accurate Result ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Safe Procedure ◽  
Bayes Model ◽  
The Past ◽  
Machine Learning Model ◽  
Number Of Customers

Extending credits to corporates and individuals for the smooth functioning of growing economies like India is inevitable. As increasing number of customers apply for loans in the banks and non- banking financial companies (NBFC), it is really challenging for banks and NBFCs with limited capital to device a standard resolution and safe procedure to lend money to its borrowers for their financial needs. In addition, in recent times NBFC inventories have suffered a significant downfall in terms of the stock price. It has contributed to a contagion that has also spread to other financial stocks, adversely affecting the benchmark in recent times. In this paper, an attempt is made to condense the risk involved in selecting the suitable person who could repay the loan on time thereby keeping the bank’s non-performing assets (NPA) on the hold. This is achieved by feeding the past records of the customer who acquired loans from the bank into a trained machine learning model which could yield an accurate result. The prime focus of the paper is to determine whether or not it will be safe to allocate the loan to a particular person. This paper has the following sections (i) Collection of Data, (ii) Data Cleaning and (iii) Performance Evaluation. Experimental tests found that the Naïve Bayes model has better performance than other models in terms of loan forecasting.

scholarly journals Credit Risk Rating Using State Machines and Machine Learning

2020 ◽  
Vol 11 (6) ◽  
pp. 163-168
Author(s):  
Behnam Sabeti ◽  
◽  
Hossein Abedi Firouzjaee ◽  
Reza Fahmi ◽  
Saeid Safavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Learning Task ◽  
Supervised Machine Learning ◽  
State Machines ◽  
Data Set ◽  
Credit Score ◽  
Novel Approach ◽  
User Data ◽  
Number Of Customers

Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

scholarly journals Performance Evaluation of the Priority Multi-Server System MMAP/PH/M/N Using Machine Learning Methods

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3236
Author(s):  
Vladimir Vishnevsky ◽  
Valentina Klimenok ◽  
Alexander Sokolov ◽  
Andrey Larionov
Keyword(s):  
Machine Learning ◽  
High Efficiency ◽  
Performance Characteristics ◽  
New Method ◽  
Arrival Process ◽  
Priority Systems ◽  
Loss Probabilities ◽  
Number Of Customers ◽  
Multi Server ◽  
Stationary Probabilities

In this paper, we present the results of a study of a priority multi-server queuing system with heterogeneous customers arriving according to a marked Markovian arrival process (MMAP), phase-type service times (PH), and a queue with finite capacity. Priority traffic classes differ in PH distributions of the service time and the probability of joining the queue, which depends on the current length of the queue. If the queue is full, the customer does not enter the system. An analytical model has been developed and studied for a particular case of a queueing system with two priority classes. We present an algorithm for calculating stationary probabilities of the system state, loss probabilities, the average number of customers in the queue, and other performance characteristics for this particular case. For the general case with K priority classes, a new method for assessing the performance characteristics of complex priority systems has been developed, based on a combination of machine learning and simulation methods. We demonstrate the high efficiency of the new method by providing numerical examples.

Design of Noncoherent Communications: From Statistical Method to Machine Learning

2020 ◽  
Vol 27 (1) ◽  
pp. 76-83
Author(s):  
Jianhao Huang ◽  
Muhang Lan ◽  
Han Zhang ◽  
Chuan Huang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Method ◽  
Noncoherent Communications
Sign in / Sign up

Export Citation Format

Share Document