Search results “Data mining models and techniques”
data mining techniques
This video describes data mining tasks or techniques in brief. Each technique requires a separate explanation as well. #datamining #techniques #weka Data mining tutorial in hindi Weka tutorial in hindi Data mining tutorial
Views: 6123 yaachana bhawsar
Overview of Data Mining and Predictive Modelling
My web page: www.imperial.ac.uk/people/n.sadawi The slides can be found here: https://github.com/nsadawi/DataMiningSlides
Views: 122774 Noureddin Sadawi
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 88012 StudyYaar.com
Data Mining - Clustering
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 104594 edureka!
The Technique Behind Building Data Mining Models.
Part of Eng. Muhammad Hijazi Business Intelligence Course to 4th year Computer Science Students, Cairo University. March 09, 2019
Data Mining Classification and Prediction ( in Hindi)
A tutorial about classification and prediction in Data Mining .
Views: 31697 Red Apple Tutorials
Advanced Excel - Data Mining Techniques using Excel
Key Takeaways for the session : Breaking junk using formula and generate reports VBA to manipulate data in required format Data extraction from external files Who should attend? People from any domain who work on data in any form. Good for Engineers, Leads, Managers, Sales people, HR, MIS experts, Data scientists, IT Support, BPO, KPO etc. Feel free to write me at [email protected]
Final Year Projects | Comparison and evaluation of data mining techniques with algorithmic models
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Views: 312 Clickmyproject
Data Science for Business: Data Mining Process and CRISP DM
This lesson provides an introduction to the data mining process with a focus on CRISP-DM. This video was created by Cognitir (formerly Import Classes). Cognitir is a global company that provides live training courses to business & finance professionals globally to help them acquire in-demand tech skills. For additional free resources and information about training courses, please visit: www.cognitir.com
Views: 14671 Cognitir
Data Mining
Views: 156 ARUN RAJ M
Data Mining Classification - Basic Concepts
Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.
Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting
#EnsembleLearning #EnsembleModels #MachineLearning #DataAnalytics #DataScience Ensemble Learning is using multiple learning algorithms at a time, to obtain predictions with an aim to have better predictions than the individual models. Ensemble learning is a very popular method to improve the accuracy of a machine learning model. It avoid overfitting and gives us a much better model. bootstrap aggregating (Bagging) and boosting are popular ensemble methods. In the next tutorial we will implement some ensemble models in scikit learn. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon
Views: 27745 The Semicolon
Email Spam Classifier using Data Mining Techniques
Email is an effective, faster and cheaper way of communication. It is expected that the total number of worldwide email accounts is increased from 3.3 billion email accounts in 2012 to over 4.3 billion by the end of year 2016. Spam is an unwanted, junk, unsolicited bulk mails, used to spreading virus, Trojans, malicious code, advertisement or to gain profit on negligible cost. Ham is a legitimate, wanted, solicited mails. Email spamming is increasing day by day because of effective, fast and cheap way of exchanging information with each other. According to the investigation, User receives spam mails - ham mails About 120 billion of spam mails are sent per day and the cost of sending is approximately zero. Spam is a major problem that attacks the existence of electronic mails. So, it is very important to distinguish ham emails from spam emails, many methods have been proposed for classification of email as spam or ham emails. Classification Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases as in Steps of Classification: 1. Model construction: Describing a set of predetermined classes -Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. -The set of tuples used for model construction is training set. -The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects -Estimate accuracy of the model -If the accuracy is acceptable, use the model to classify new data For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Week 7: Text Mining Conceptual Overview of Techniques
Carolyn Rose discusses text mining conceptual overview of techniques for week 7 of DALMOOC.
Ensemble learners
This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 45844 Udacity
Introduction to data mining and architecture  in hindi
#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 210984 Last moment tuitions
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
Supervised and unsupervised learning algorithms
Views: 66622 Nathan Kutz
Predicting Peer-to-Peer Loan Default Using Data Mining Techniques - Callum Stevens
Access a shiny web app at: https://callumstevens.shinyapps.io/logisticregression/ View full slideshow presentation at: https://goo.gl/mGMkXI Abstract: Loans made via Peer-to-Peer Lending (P2PL) Platforms are becoming ever more popular among investors and borrowers. This is due to the current economic environment where cash deposits earn very little interest, whilst borrowers can face high interest rates on credit cards and short term loans. Investors seeking yielding assets are looking towards P2PL, however most lack prior lending experience. Lenders face the problem of knowing which loans are most likely to be repaid. Thus this project evaluates popular Data Mining classification algorithms to predict if a loan outcome is likely to be 'Fully Repaid‘ or 'Charged Off‘. Several approaches have been used in this project, with the aim of increasing predictive accuracy of models. Several external datasets have been blended to introduce relevant economic data, derivative columns have been created to gain meaning between different attributes. Filter attribute evaluation methods have been used to discover appropriate attribute subsets based on several criteria. Synthetic Minority Over-sampling Technique (SMOTE) has been used to address the imbalanced nature of credit datasets, by creating synthetic 'Charged Off‘ loans to ensure a more even class distribution. Tuning of parameters has been performed, showing how each algorithm‘s performance can vary as a result of changes. Data pre-processing methods have been discussed in detail, which previous research lacked discussion on. The author has documented each Data Mining phase to allow researchers to repeat tests. Selected models have been deployed as Web Applications, providing researchers with accuracy metrics upon which to evaluate them. Possible approaches to improve accuracy further have been discussed, with the hope of stimulating research into this area.
Views: 646 Callum Stevens
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 164641 Well Academy
Predictive Modelling Techniques | Data Science With R Tutorial
This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 211183 Simplilearn
Data Mining Techniques
Decision Trees, Naive Bayes, and Neural Networks
Views: 22856 nathan baughman
Model Evaluation: Introduction to the Cross Validation and Hold-out methods
My web page: www.imperial.ac.uk/people/n.sadawi
Views: 26885 Noureddin Sadawi
Oracle data mining tutorial, data mining techniques: classification
What is data mining? The Oracle Data Miner tutorial presents data mining introduction. Learn data mining techniques. More lessons, visit http://www.learn-with-video-tutorials.com/oracle-data-mining-tutorial-video
Analyzing and modeling complex and big data | Professor Maria Fasli | TEDxUniversityofEssex
This talk was given at a local TEDx event, produced independently of the TED Conferences. The amount of information that we are creating is increasing at an incredible speed. But how are we going to manage it? Professor Maria Fasli is based in the School of Computer Science and Electronic Engineering at the University of Essex. She obtained her BSc from the Department of Informatics of T.E.I. Thessaloniki (Greece). She received her PhD from the University of Essex in 2000 having worked under the supervision of Ray Turner in axiomatic systems for intelligent agents. She has previously worked in the area of data mining and machine learning. Her current research interests lie in agents and multi-agent systems and in particular formal theories for reasoning agents, group formation and social order as well as the applications of agent technology to e-commerce. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 137267 TEDx Talks
Introduction to Ensemble Learning, Bagging and Boosting
Click here for in depth study with quiz / workout - https://www.udemy.com/decision-tree-theory-application-and-modeling-using-r/?couponCode=YOU_DT_0
Views: 14301 Gopal Malakar
6 Types of Classification Algorithms
Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine. https://analyticsindiamag.com/7-types-classification-algorithms/ -------------------------------------------------- Get in touch with us: Website: www.analyticsindiamag.com Contact: [email protected] Facebook: https://www.facebook.com/AnalyticsIndiaMagazine/ Twitter: http://www.twitter.com/analyticsindiam Linkedin: https://www.linkedin.com/company-beta/10283931/ Instagram: https://www.instagram.com/analyticsindiamagazine/
Four Types Of Cross Validation| K-Fold | Leave One Out |Bootstrap | Hold Out
In this video you will learn about the different types of cross validation you can use to validate you statistical model. Cross validation is an important step in model building which ensures you have a model that will perform well in the new data , which also overcomes the possibility model being over fit. There are four types of cross validation you will learn 1- Hold out Method 2- K-Fold CV 3- Leave one out CV 4-Bootstrap Methods for more learn here : https://www.cs.cmu.edu/~schneide/tut5/node42.html Cross validation is also important step in machine learning model building and data science model building. Study Packs : https://analyticuniversity.com Facebook : https://www.facebook.com/AnalyticsUniversity Twitter : https://twitter.com/AnalyticsUniver
Views: 39157 Analytics University
Cancer is one of the major causes of death when compared to all other diseases. Cancer has become the most hazardous types of disease among the living creature in the world. Early detection of cancer is essential in reducing life losses. This work aims to establish an accurate classification model for Cancer prediction, in order to make full use of the invaluable information in clinical data. The dataset is divided into training set and test set. In this experiment, we compare six classification techniques in Weka software and comparison results show that Support Vector Machine (SVM) has higher prediction accuracy than those methods. Different methods for cancer detection are explored and their accuracies are compared. With these results, we infer that the SVM are more suitable in handling the classification problem of cancer prediction, and we recommend the use of these approaches in similar classification problems. This work presents a comparison among the different Data mining classifiers on the database of cancer, by using classification accuracy.
Views: 4702 David Clinton
A Literature Review on Data Mining Techniques applied in Health Care Decision Making
Literature Review on Data Mining Techniques applied in Health Care Decision Making
Views: 1249 mahesh l
KDD ( knowledge data discovery )  in data mining in hindi
#kdd #datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 71573 Last moment tuitions
Machine Learning - Supervised VS Unsupervised Learning
Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/ Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed! Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine! Connect with Big Data University: https://www.facebook.com/bigdatauniversity https://twitter.com/bigdatau https://www.linkedin.com/groups/4060416/profile ABOUT THIS COURSE •This course is free. •It is self-paced. •It can be taken at any time. •It can be audited as many times as you wish. https://bigdatauniversity.com/courses/machine-learning-with-python/
Views: 83737 Cognitive Class
Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 110792 LearnEveryone
Learn Predictive Modeling Techniques Without Programming and Do Data Mining With IBM SPSS Modeler
http://bit.ly/LearnIBMSPSSModeler Learn Predictive Modeling Techniques Without Programming and How To Do Data Mining With IBM SPSS Modeler. IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models. Overview: This course introduces students to data mining and to the functionality available within IBM SPSS Modeler. The series of stand-alone videos, are designed to introduce students to specific nodes or data mining topics. Each video consists of detailed instructions explaining why we are using a technique, in what situations it is used, how to set it up, and how to interpret the results. This course is broken up into phases. The Introduction to Data Mining Phase is designed to get you up to speed on the idea of data mining. You will also learn about the CRISP-DM methodology which will serve as a guide throughout the course and you will also learn how to navigate within Modeler. The Data Understanding Phase addresses the need to understand what your data resources are and the characteristics of those resources. We will discuss how to read data into Modeler. We will also focus on describing, exploring, and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data. While the Modeling Phase will focus on building a predictive model. The Evaluation Phase focuses how to take your data mining results so that you can achieve your business objectives. And finally the Deployment Phase allows you to do something with your findings. What are the requirements? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. No statistical or data mining background is necessary. What are you going to get from this course? Over 22 lectures and 4 hours of content! Data Mining and Advanced Analytics Defined Modeling Methods in Modeler CRISP-DM Overview General Modeler Orientation Reading Data Assessing Data Quality Integrating Data Constructing Data Modeling Evaluation Deployment What is the target audience? This course is for anyone that would like to learn how to use IBM SPSS Modeler. This course is for anyone that would like to learn how to do Data Mining. Enroll "IBM SPSS Modeler: Getting Started" Course Here: http://bit.ly/LearnIBMSPSSModeler For More Video Uploads In The Future Please Subscribe To This Youtube Channel by Clicking Subscribe Button Below! and Don't Forget To Giving Your "LIKE" For This Video! Please Click LIKE Button Below! Thanks For Watching The Video! See You Next Time! Note: All The Links In The Video Description Are Affiliate Links, So I Can Make Money If Visitor Purchase The Products!
Data Preprocessing
Project Name: Learning by Doing (LBD) based course content development Project Investigator: Prof Sandhya Kode
Views: 36512 Vidya-mitra
Model Evaluation : ROC Curve, Confusion Matrix, Accuracy Ratio | Data Science
In this video you will learn about the different performance matrix used for model evaludation such as Receiver Operating Charateristics, Confusion matrix, Accuracy. This is used very well in evauating classfication models like deicision tree, Logistic regression, SVM ANalytics Study Pack : https://analyticuniversity.com Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 16779 Big Edu
Credit Card Default - Data Mining
A data mining project as part of requirements for Applied Data Mining at Rockhurst University. This presentation explores the mining of data utilizing R programming. Methods used are Decision Tree and Linear Regression models to predict the outcome of whether a customer will default on their next monthly credit card payment.
Views: 2056 Jonathan Walker
Creating Data Mining Structures & Predictive Models using the Excel Add-In  for SQL Server 2008
A demonstration of how to create Data Mining Structures & Predictive Models using the Excel Data mining Addin for SQL Server 2008. A data mining structure is created first and then a Microsoft Decision Tree & Neural Network are created. In the subsequent video I will create a lift chart (also known as an Accuracy Chart) to compare the effectiveness of the two models. The raw data used in the demonstration is available at http://www.analyticsinaction.com/creating-data-mining-structures-predictive-models-using-the-excel-add-in-for-sql-server-2008/ I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 26178 Steve Fox
Advanced Data Mining with Weka (5.2: Building models)
Advanced Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Building models http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/7XXl63 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2282 WekaMOOC
Student Performance Measure By Using Different Classification Methods Of Data Mining
This video explains about the various classification methods of data mining to measure the performance of the students using the grades obtained in four semesters.
Views: 2911 Neha Choudhary
Data Mining and Predictive Analytics in IBM SPSS Modeler
The US faces a shortage of 1.5 million managers who know how to use the analysis of data to make effective decisions. McKinsey and Company. https://theachieveher.com/p/business-data-analytics-ibm-spss-modeler This predictive analytics course if for you if you are a … •Manager who wants to know how to use the analysis of data to make effective decisions. •Analyst who wants to understand data science even if you never intend to apply it yourself. •Anyone interested in learning about data mining. (IBM offers a free 30-day trial so let's get started!) •Scientist who wants to integrate text from your Twitter, Facebook or customer surveys into your predictive analysis. •Person who assesses proposals to improve some part of your organization’s business. •Beginner to IBM SPSS modeler 16 or 17 and looking for the best set of tutorials and demonstrations available online! •Data scientist who is just getting started with predictive analytics and wants to understand how to solve business problems with analytics in SPSS Modeler. So are you ready to easily and quickly answer data mining questions like: •Who are my best customers and what do they buy? •Why do they leave? •Which piece of equipment will fail next? •Or… which donors are likely to donate again? This course consists of extremely real world hands on demonstrations to immerse you in the most common prediction models, decision trees, clustering, and text analytics. So get ready to: 1.Segment your customers using popular models like K-Means. 2.Predict an amount of giving using several types of decision trees including C and R Trees. 3.Merge quantitative and qualitative data using text analytics. 4.Use a host of visual analytics like web and distribution nodes. 5.Use unsupervised clustering techniques to divide a population of customers into understandable groups. 6.Learn how to isolate the best input predictors using feature selection. 7.Export your insights to Excel and much more! Join me at https://theachieveher.com/p/business-data-analytics-ibm-spss-modeler
Novel Data Mining Methods for Virtual Screening - PhD Defense
The Defense of PhD degree in Computer Science in King Abdullah University of Science and Technology (KAUST). Abstract: Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a confident conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by the big expansion in data and its dimensionality are inevitable. In this thesis, I address the main challenges that come when data mining techniques are used for virtual screening. In order to achieve an efficient virtual screening using data mining, I start by addressing the problem of feature selection and provide analysis of best ways to describe a chemical compound for an enhanced screening performance. High-throughput screening (HTS) assays data used for virtual screening are characterized by a great class imbalance. To handle this problem of class imbalance, I suggest using a novel algorithm called DRAMOTE to narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated. Existing works are mostly proposed for small-scale virtual screening based on making use of few thousands of interactions. Thus, I propose enabling large-scale (or big) virtual screening through learning millions of interaction while exploiting any relevant dependency for a better accuracy. A novel solution called DRABAL that incorporates structure learning of a Bayesian Network as a step to model dependency between the HTS assays, is showed to achieve significant improvements over existing state-of-the-art approaches.
Views: 464 Othman Soufan
What is DATA MODELING? What does DATA MODELING mean? DATA MODELING meaning & explanation
✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is DATA MODELING? What does DATA MODELING mean? DATA MODELING meaning - DATA MODELING definition - DATA MODELING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data modeling in software engineering is the process of creating a data model for an information system by applying formal data modeling techniques. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the information system. There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. The data requirements are initially recorded as a conceptual data model which is essentially a set of technology independent specifications about the data and is used to discuss initial requirements with the business stakeholders. The conceptual model is then translated into a logical data model, which documents structures of the data that can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them. Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling: - to assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and clients to understand and use an agreed semi-formal model the concepts of the organization and how they relate to one another; - to manage data as a resource; - for the integration of information systems; - for designing databases/data warehouses (aka data repositories). Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten et al. (2004) determined two types of data modeling: - Strategic data modeling: This is part of the creation of an information systems strategy, which defines an overall vision and architecture for information systems is defined. Information engineering is a methodology that embraces this approach. - Data modeling during systems analysis: In systems analysis logical data models are created as part of the development of new databases. Data modeling is also used as a technique for detailing business requirements for specific databases. It is sometimes called database modeling because a data model is eventually implemented in a database.
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Data Warehousing & Mining Techniques - Lec3 - Modeling
Prof. Dr. Wolf-Tilo Balke Technische Universität Braunschweig Institut für Informationssysteme http://www.ifis.cs.tu-bs.de/teaching/... Wolf-Tilo Balke holds the Chair for Information Systems at TU Braunschweig since April 2008. Before that he was a the Associate Research Director at L3S Research Center in Hannover, Germany and a Research Fellow at University of California at Berkeley, CA, USA.
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