Data mining techniques case study

The main objective of this research is to predict heart diseases by the use of classification algorithms namely Nave Bayes and Support Vector Machine in order to compare them on the basis of the performance factors ie. However the techniques applied in this case study were especially designed to address the analysis of time series events.

The prevention of students dropping out is considered very important in many educational institutions.

Data mining techniques case study. Data mining started with statistics. Statistical functions such as standard deviation regression analysis and variance are all valuable tools that allow people to study the reliability and relationships between data. Much of what data mining does is rooted in statistics making it one of the cornerstones of data mining technology.

Data Mining Techniques in Healthcare. A Case Study Chirag 1 Komal Sharma 2 Assistant Professor CSE 1 RPSDC Mahendergarh Cognizant Technology Solutions 2 Gurgaon Haryana - India ABSTRACT Data mining is the process of discovering information through large set of database and transform it into a understandable. Data mining techniques filter through large amounts of raw data and extract useful information that gives enterprise businesses a competitive edge in the market.

Various cases on customer purchasing habits have been presented and also used in real problems. Data mining techniques are highly effective in analysing consumer. How to Apply Data Mining T echniques 163 164 Silver Sakata Su Herman Dolins OShea Steven B.

Dolins received his BS in physics and MS in computer science from T ulane. A Case Study of Customer Relationship Management using Data Mining Techniques Santhosh Kumar INTRODUCTIONValue Creation for the customer is the key determinant of a successful business. Comparative Analysis of Data Mining Techniques for Fraud Detection A Case Study of Branchless Banking M.

Syed Saif-ur-Rahman Assistant Professor Faculty of Computing. To succeed in dealing with this issue researchers apply data mining techniques such as classification clustering association rules and so on. The main objective of this research is to predict heart diseases by the use of classification algorithms namely Nave Bayes and Support Vector Machine in order to compare them on the basis of the performance factors ie.

Probabilities and classification accuracy. In this paperwork there are three different data mining techniques such as Naïve Bayes K-NN Decision were addressed to analyze the dataset in which Tanagra tool was also used for the classification and evaluation of the data using 10-fold cross validation and the results were compared. A proposed Model for Predicting Employees Performance Using Data Mining Techniques.

Egyptian Case Study January 2019 International Journal of Computer Science and Information Security 17131-40. Some cases in finance where data mining is used are given below. 1 Loan Payment Prediction.

Data mining methods like attribute selection and attribute ranking will analyze the customer payment history and select important factors such as payment to income ratio credit history the term of the loan etc. Applying Data Mining Techniques to Predict Student Dropout. The prevention of students dropping out is considered very important in many educational institutions.

In this paper we describe the results of an educational data analytics case study focused on detection of dropout of System Engineering SE undergraduate students after 7 years of enrollment in a. Applying data mining techniques to medical time series. An empirical case study in electroencephalography and stabilometry.

Various data mining techniques such as classification and clustering are applied to reveal hidden knowledge from educational data. 6 Clustering is used by pattern analysis decision-making and machine learning which includes data mining document retrieval image segmentation and pattern classification. 5 Various pieces of information stored for each event can be used for clustering.

Data mining is the stage of the KDD process where the data are studied and useful information is extracted using a set of techniques and tools. Traditional time series analysis techniques examine whole time series. However the techniques applied in this case study were especially designed to address the analysis of time series events.

Case-Study Insights The aim of this case study was to establish how best to utilise data mining to improve assembly and quality control processes. To allow them to be implemented into existing systems with a minimal impact. Validated results have been produced which can be easily be interpreted and become actionable pieces of information.

Work is global this case study focuses on the data-driven agronomy work being implemented in Colombia where CIAT is headquartered. The work was first piloted in Colombia and over the last ten years. CIAT has iterated on its original data mining techniques and developed strong relationships with various public and private.

In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. Data mining and discovery of valuable information from large databases is an attractive field of study which has received a lot of attention within the past two decades.

In fact data mining aims to create models for decision-making. Different data mining techniques including clustering classification decision trees regression. A proposed Model for Predicting Employees Performance Using Data Mining Techniques.

Egyptian Case Study free download Human Resources Management HRM has become one of the essential interests of managers and decision makers in almost all types of businesses to adopt plans for correctly discovering highly qualified employees.