MINORPROJECT 2SYNOPSISON Data Analysis using Artificial NeuralNetwork for Business Prediction Submitted By: Utkarsh Yadav Saumya Srivastava 500048084 500048716 Under the guidance of Ms. Roohi SilleAssistant Professor Schoolof Computer Science and Engineering Centre for InformationTechnology,School ofComputer Science and Engineering,UNIVERSITY OFPETROLEUM AND ENERGY STUDIESDehradun-248007August- 2017Centrefor Information TechnologySchool of Computer Science and EngineeringUniversityof Petroleum & Energy Studies, Dehradun Project Proposal Approval Form (2017-18) II Minor Project Title: DataAnalysis using Artificial Neural Network for Business Prediction Abstract: Be it the customer satisfaction or the setup of newstart up, Data Analysis plays a vital role in almost every field.
One suchfield of Data Analysis is of Business Prediction. There are various techniquesto perform this out of which one such possible method is the use of ArtificialNeural Network. A Neural Network modelcan be easily used for time series prediction of business. By using multi-layerperceptron neural network is built through which more accurate predictionvalues can be obtained which would in turn help for better forecasting. Keywords: ANN, Neural Network, Data Analysis, Business,Prediction, Forecast Introduction: The project entitled DataAnalysis using Artificial Neural Network for Business Prediction is to analyze thepast data and predict the future demand by forecasting it beforehand forvarious business firms.
The brain is an enormouslycomplex system in which distributed information is processed in parallel bymutual dynamical interactions of neurons. It is still difficult andchallenging, to understand the mechanisms of the brain. The importance andeffectiveness of brain-style computation has become a fundamental principle inthe development of neural networks. There are three different research areas concerningneural networks. One is the experimental based on physiology and molecularbiology. The second area is engineering applications of neural networksinspired by the brain-style computation where information is distributed asanalog pattern signal, parallel computations are dominant and learning guaranteesflexibility and robust computation. The third area is concerned withmathematical foundations of neuro-computing, which searches for the fundamentalprinciples of parallel distributed information systems with learningcapabilities.
Statistics has a close relation with the second application areaof neuronal networks. This area has opened new practical methods of pattern recognition,time series analysis and image processing 1. The study aims toincorporate the Artificial Neural Network in business prediction to discovervarious variables which influence the business performance. The combined effectof each of the variable would help in obtaining values required for forecastingof demand. The process requires inculcating the skills of statistics in ANN sothat desirable predictions can be made.
ProblemStatement: To design a system that would be able to performanalysis of data for computing future predictions for various business fordemand of goods or services based on past demand information. Literature Review:Artificial Neural Networks (ANN)have received a great deal of attention in many fields of engineering andscience. Inspired by the study of brain architecture, ANN represent a class of nonlinearmodels capable of learning from data. ANN have been applied in many areas wherestatistical methods are traditionally employed. They have been used in patternrecognition, classification, prediction and process control. Business Prediction is the conceptof analyzing past data or patterns in order to predict or forecast future possibility.The process focuses on finding methods to benefit individual business firms byperforming statistical techniques. Historic data to be forecasted is gathered,divided for evaluation and then used to develop model for finding desirableresults.
Objectives: 1. To implement Data Analysis in ANN for Businessprediction of goods and services. 2. Deployment of proper functional software for Forecastusing statistical computation. Methodology: ? Requirementsfor the project is gathered. ? A neural network is set up for data analysis.
? Business Prediction techniques are explored andmost suitable techniques is chosen. ? Input is takenconsidering the technique chosen. ? Repeatedcorrection is done until desirable output is fetched. ? Further testingand debugging is carried out. ? Documentation is done. SystemRequirements: (Software/Hardware) Softwarerequirements: · Visual StudioCode to write source code.· An operatingsystem platform like Windows or Linux.· MS Word, MSPowerPoint for documentation.
Hardwarerequirements: · PersonalComputer/Desktop System.· Internetconnectivity. Schedule:(PERT Chart) References: 1H´ector Allende, Claudio Moraga, Rodrigo Salask, 2002. ArtificialNeural Networks in Time Series Forecasting: A Comparative Analysis, 8: 101-113.
Approved By ProjectGuide HOD, Centre for InformationTechnology