Enviar búsqueda
Cargar
11/04 Regular Meeting: Monority Report in Fraud Detection Classification of Skewed Data
•
Descargar como PPT, PDF
•
3 recomendaciones
•
486 vistas
萍華 楊
Seguir
Denunciar
Compartir
Denunciar
Compartir
1 de 30
Descargar ahora
Recomendados
insurance
V. pacáková, d. brebera
V. pacáková, d. brebera
logyalaa
Luis_Ramon_Report.doc
Luis_Ramon_Report.doc
Luis Ramon Buruato
SI 2013 Econometrics Lectures: Econometric Methods for High-Dimensional Data
High-Dimensional Methods: Examples for Inference on Structural Effects
High-Dimensional Methods: Examples for Inference on Structural Effects
NBER
Presentation at the SAS Global Forum 2012, Orlando, FL. Presenters: Alejandro Correa Bahnsen Andres Felipe Gonzalez Montoya
2012 predictive clusters
2012 predictive clusters
Alejandro Correa Bahnsen, PhD
DataMining_CA2-4
DataMining_CA2-4
Aravind Kumar
https://www.irjet.net/archives/V4/i8/IRJET-V4I857.pdf
Handling Imbalanced Data: SMOTE vs. Random Undersampling
Handling Imbalanced Data: SMOTE vs. Random Undersampling
IRJET Journal
Class imbalance problem1
Class imbalance problem1
Class imbalance problem1
chs71
Check this A+ tutorial guideline at https://www.uopassignments.com/math-533-devry For more classes visit http://www.uopassignments.com
MATH 533 Entire Course NEW
MATH 533 Entire Course NEW
shyamuopuopeleven
Recomendados
insurance
V. pacáková, d. brebera
V. pacáková, d. brebera
logyalaa
Luis_Ramon_Report.doc
Luis_Ramon_Report.doc
Luis Ramon Buruato
SI 2013 Econometrics Lectures: Econometric Methods for High-Dimensional Data
High-Dimensional Methods: Examples for Inference on Structural Effects
High-Dimensional Methods: Examples for Inference on Structural Effects
NBER
Presentation at the SAS Global Forum 2012, Orlando, FL. Presenters: Alejandro Correa Bahnsen Andres Felipe Gonzalez Montoya
2012 predictive clusters
2012 predictive clusters
Alejandro Correa Bahnsen, PhD
DataMining_CA2-4
DataMining_CA2-4
Aravind Kumar
https://www.irjet.net/archives/V4/i8/IRJET-V4I857.pdf
Handling Imbalanced Data: SMOTE vs. Random Undersampling
Handling Imbalanced Data: SMOTE vs. Random Undersampling
IRJET Journal
Class imbalance problem1
Class imbalance problem1
Class imbalance problem1
chs71
Check this A+ tutorial guideline at https://www.uopassignments.com/math-533-devry For more classes visit http://www.uopassignments.com
MATH 533 Entire Course NEW
MATH 533 Entire Course NEW
shyamuopuopeleven
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
ahmad abdelhafeez
For more classes visit www.snaptutorial.com MATH 533 Week 1 Homework MATH 533 Week 1 Quiz MATH 533 Week 2 DQ 1 Case Let's Make a Deal MATH 533 Week 2 Homework (2 Sets) MATH 533 Week 2 Quiz MATH 533 Week 3 DQ 1 Ethics in Statistics Readings and Discussion
MATH 533 Education Specialist / snaptutorial.com
MATH 533 Education Specialist / snaptutorial.com
McdonaldRyan97
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
Chirag Gupta
bayesian model
Statsci
Statsci
Vassilios Kelessidis
http://www.iosrjournals.org/iosr-jce/pages/v12i6.html
Multi-Cluster Based Approach for skewed Data in Data Mining
Multi-Cluster Based Approach for skewed Data in Data Mining
IOSR Journals
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years 1999-2008 Data Set)
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Seval Çapraz
FSRM 582 Project
FSRM 582 Project
Qi(Gilbert) Zhou
SI 2013 Econometrics Lectures: Econometric Methods for High-Dimensional Data
Econometrics of High-Dimensional Sparse Models
Econometrics of High-Dimensional Sparse Models
NBER
Lower Order Finite Volume based RANS simulations have become an industry standard in Computational Fluid Dynamics. However, there's a major interest among academics to advance the state of the art by employing spectral element methods in Large Eddy Simulation. In this presentation, we study major issues which inhibit spectral element methods from becoming a mainstream CFD method
Spectral Element Methods in Large Eddy Simulation
Spectral Element Methods in Large Eddy Simulation
gaurav dhir
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
Prediction model of algal blooms using logistic regression and confusion matrix
Prediction model of algal blooms using logistic regression and confusion matrix
IJECEIAES
Pydata Talk on Classification of imbalanced data. It is an overview of concepts for better classification in imbalanced datasets. Resampling techniques are introduced along with bagging and boosting methods.
Multiclass classification of imbalanced data
Multiclass classification of imbalanced data
SaurabhWani6
discusses using a definitive screening design to characterize and optimize a glycoprofiling method and compares the definitive screening results to a much larger central composite design results
Use of Definitive Screening Designs to Optimize an Analytical Method
Use of Definitive Screening Designs to Optimize an Analytical Method
Philip Ramsey
Discusses various model selection strategies that can be used with definitive screening designs
Modeling strategies for definitive screening designs using jmp and r
Modeling strategies for definitive screening designs using jmp and r
Philip Ramsey
Data clustering is a common technique for statistical data analysis; it is defined as a class of statistical techniques for classifying a set of observations into completely different groups. Cluster analysis seeks to minimize group variance and maximize between group variance. In this study we formulate a mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear binary model is suggested to select the most important variables in clustering a set of data. The idea of the suggested model depends on clustering data by minimizing the distance between observations within groups. Indicator variables are used to select the most important variables in the cluster analysis.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
IJRES Journal
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
Ian Camacho
selection of relevant feature from a given set of feature is one of the important issues in the field of data mining as well as classification. In general the dataset may contain a number of features however it is not necessary that the whole set features are important for particular analysis of decision making because the features may share the common information‟s and can also be completely irrelevant to the undergoing processing. This generally happen because of improper selection of features during the dataset formation or because of improper information availability about the observed system. However in both cases the data will contain the features that will just increase the processing burden which may ultimately cause the improper outcome when used for analysis. Because of these reasons some kind of methods are required to detect and remove these features hence in this paper we are presenting an efficient approach for not just removing the unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information theory to detect the information gain from each feature and minimum span tree to group the similar features with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the performances of the classifier.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
IJERA Editor
Talk at Mathematical Biosciences Institute: Algebraic Methods in Systems and Evolutionary Biology
Non-parametric analysis of models and data
Non-parametric analysis of models and data
haharrington
JEDM_RR_JF_Final
JEDM_RR_JF_Final
Jonathan Fivelsdal
FOR MORE CLASSES VISIT www.math533rank.com MATH 533 Week 1 Homework MATH 533 Week 1 Quiz MATH 533 Week 2 DQ 1 Case Let's Make a Deal MATH 533 Week 2 Homework (2 Sets) MATH 533 Week 2 Quiz
MATH 533 RANK Achievement Education--math533rank.com
MATH 533 RANK Achievement Education--math533rank.com
kopiko162
Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart
Predictive Model for Customer Segmentation using Database Marketing Techniques
Predictive Model for Customer Segmentation using Database Marketing Techniques
Akanksha Jain
An Introduction to boosting
An Introduction to boosting
butest
What factors have significant impact on your customer churn? Find out so you can reduce your churn rate and drive down your CAC>=.
Insights into Customer Churn
Insights into Customer Churn
Vendasta Technologies
Más contenido relacionado
La actualidad más candente
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
ahmad abdelhafeez
For more classes visit www.snaptutorial.com MATH 533 Week 1 Homework MATH 533 Week 1 Quiz MATH 533 Week 2 DQ 1 Case Let's Make a Deal MATH 533 Week 2 Homework (2 Sets) MATH 533 Week 2 Quiz MATH 533 Week 3 DQ 1 Ethics in Statistics Readings and Discussion
MATH 533 Education Specialist / snaptutorial.com
MATH 533 Education Specialist / snaptutorial.com
McdonaldRyan97
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
Chirag Gupta
bayesian model
Statsci
Statsci
Vassilios Kelessidis
http://www.iosrjournals.org/iosr-jce/pages/v12i6.html
Multi-Cluster Based Approach for skewed Data in Data Mining
Multi-Cluster Based Approach for skewed Data in Data Mining
IOSR Journals
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years 1999-2008 Data Set)
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Seval Çapraz
FSRM 582 Project
FSRM 582 Project
Qi(Gilbert) Zhou
SI 2013 Econometrics Lectures: Econometric Methods for High-Dimensional Data
Econometrics of High-Dimensional Sparse Models
Econometrics of High-Dimensional Sparse Models
NBER
Lower Order Finite Volume based RANS simulations have become an industry standard in Computational Fluid Dynamics. However, there's a major interest among academics to advance the state of the art by employing spectral element methods in Large Eddy Simulation. In this presentation, we study major issues which inhibit spectral element methods from becoming a mainstream CFD method
Spectral Element Methods in Large Eddy Simulation
Spectral Element Methods in Large Eddy Simulation
gaurav dhir
Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
Prediction model of algal blooms using logistic regression and confusion matrix
Prediction model of algal blooms using logistic regression and confusion matrix
IJECEIAES
Pydata Talk on Classification of imbalanced data. It is an overview of concepts for better classification in imbalanced datasets. Resampling techniques are introduced along with bagging and boosting methods.
Multiclass classification of imbalanced data
Multiclass classification of imbalanced data
SaurabhWani6
discusses using a definitive screening design to characterize and optimize a glycoprofiling method and compares the definitive screening results to a much larger central composite design results
Use of Definitive Screening Designs to Optimize an Analytical Method
Use of Definitive Screening Designs to Optimize an Analytical Method
Philip Ramsey
Discusses various model selection strategies that can be used with definitive screening designs
Modeling strategies for definitive screening designs using jmp and r
Modeling strategies for definitive screening designs using jmp and r
Philip Ramsey
Data clustering is a common technique for statistical data analysis; it is defined as a class of statistical techniques for classifying a set of observations into completely different groups. Cluster analysis seeks to minimize group variance and maximize between group variance. In this study we formulate a mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear binary model is suggested to select the most important variables in clustering a set of data. The idea of the suggested model depends on clustering data by minimizing the distance between observations within groups. Indicator variables are used to select the most important variables in the cluster analysis.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
IJRES Journal
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
Ian Camacho
selection of relevant feature from a given set of feature is one of the important issues in the field of data mining as well as classification. In general the dataset may contain a number of features however it is not necessary that the whole set features are important for particular analysis of decision making because the features may share the common information‟s and can also be completely irrelevant to the undergoing processing. This generally happen because of improper selection of features during the dataset formation or because of improper information availability about the observed system. However in both cases the data will contain the features that will just increase the processing burden which may ultimately cause the improper outcome when used for analysis. Because of these reasons some kind of methods are required to detect and remove these features hence in this paper we are presenting an efficient approach for not just removing the unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information theory to detect the information gain from each feature and minimum span tree to group the similar features with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the performances of the classifier.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
IJERA Editor
Talk at Mathematical Biosciences Institute: Algebraic Methods in Systems and Evolutionary Biology
Non-parametric analysis of models and data
Non-parametric analysis of models and data
haharrington
JEDM_RR_JF_Final
JEDM_RR_JF_Final
Jonathan Fivelsdal
FOR MORE CLASSES VISIT www.math533rank.com MATH 533 Week 1 Homework MATH 533 Week 1 Quiz MATH 533 Week 2 DQ 1 Case Let's Make a Deal MATH 533 Week 2 Homework (2 Sets) MATH 533 Week 2 Quiz
MATH 533 RANK Achievement Education--math533rank.com
MATH 533 RANK Achievement Education--math533rank.com
kopiko162
La actualidad más candente
(19)
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
MATH 533 Education Specialist / snaptutorial.com
MATH 533 Education Specialist / snaptutorial.com
Probability density estimation using Product of Conditional Experts
Probability density estimation using Product of Conditional Experts
Statsci
Statsci
Multi-Cluster Based Approach for skewed Data in Data Mining
Multi-Cluster Based Approach for skewed Data in Data Mining
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
Statistical Data Analysis on a Data Set (Diabetes 130-US hospitals for years ...
FSRM 582 Project
FSRM 582 Project
Econometrics of High-Dimensional Sparse Models
Econometrics of High-Dimensional Sparse Models
Spectral Element Methods in Large Eddy Simulation
Spectral Element Methods in Large Eddy Simulation
Prediction model of algal blooms using logistic regression and confusion matrix
Prediction model of algal blooms using logistic regression and confusion matrix
Multiclass classification of imbalanced data
Multiclass classification of imbalanced data
Use of Definitive Screening Designs to Optimize an Analytical Method
Use of Definitive Screening Designs to Optimize an Analytical Method
Modeling strategies for definitive screening designs using jmp and r
Modeling strategies for definitive screening designs using jmp and r
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
Simulation Study of Hurdle Model Performance on Zero Inflated Count Data
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
Non-parametric analysis of models and data
Non-parametric analysis of models and data
JEDM_RR_JF_Final
JEDM_RR_JF_Final
MATH 533 RANK Achievement Education--math533rank.com
MATH 533 RANK Achievement Education--math533rank.com
Destacado
Develop a predictive model using historical data set DONOR_RAW, which can predict whether the prospect will donate/ not donate. Data set: DONOR_RAW data set • 50 Variables • 19,372 observations Tools Used: • SAS Enterprise Miner 4.3 • SAS 9.3_M1 Techniques Used: • Logistic Regression • Decision Trees - CHAID Also introduced Interaction Terms to have a better understanding of the data. Final Model Selection Analysis based on: • LIFT Chart
Predictive Model for Customer Segmentation using Database Marketing Techniques
Predictive Model for Customer Segmentation using Database Marketing Techniques
Akanksha Jain
An Introduction to boosting
An Introduction to boosting
butest
What factors have significant impact on your customer churn? Find out so you can reduce your churn rate and drive down your CAC>=.
Insights into Customer Churn
Insights into Customer Churn
Vendasta Technologies
Presentation - Predicting Online Purchases Using Conversion Prediction Modeli...
Presentation - Predicting Online Purchases Using Conversion Prediction Modeli...
Christopher Sneed, MSDS, PMP, CSPO
Learn more about our latest innovation: Behavioral Track & Trigger featuring Browse Abandonment Campaigns. This slideshare includes best practices, tips, tricks, step by step instructions for setting up your campaigns, and top 10 subject lines to get you started.
Behavioral Track & Trigger featuring Browse Abandonment Campaigns
Behavioral Track & Trigger featuring Browse Abandonment Campaigns
WhatConts
A combination of decision tree learning and clustering
A combination of decision tree learning and clustering
Chinnapat Kaewchinporn
For every marketer of mobile application, acquiring new customers certainly requires more effort in terms of time and money. On the other hand, firm can always focus on maintaining existing customer base and gain maximum out of them. If this is the case, then predictive analysis will be the correct approach for this situation. The primary goal of this webinar is to predict segment of Mobile application users, * Who will uninstall the app * Remain inactive (which will be also termed as a churner) for quite long time and are expected to churn. Churn analysis is the approach by which we will predict the likelihood of this event to occur. Our webinar covers: * How to extract data from Google Analytics using R * How to build churn model in R * Identifying the customer/subscriber segment that are classified based on past data pattern, who are likely to churn (Study customer behavior Patterns) Watch Full Webinar - http://www.tatvic.com/webinar/churn-analysis-for-mobile-application/
How to Perform Churn Analysis for your Mobile Application?
How to Perform Churn Analysis for your Mobile Application?
Tatvic Analytics
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...
Silicon Studio Corporation
Identify prospects from a credit data set SMALL using data mining techniques Data set: SMALL data set • 145 Variables • 8,000 observations Tools Used: • SAS Enterprise Miner Workstation 7.1 • SAS 9.3_M1 Steps involved: • Data Quality Check • Data Partition - TRAIN/ VALIDATE/ TEST • Mining using Decision Trees - CHAID/ Pruned CHAID/ CART/ C4.5 • Data Mining using Regression - Forward/ Backward/ Stepwise • Data Mining using Regression with Interaction terms included • Data Mining using Neural Network • Model Comparison and Scoring Final Model Selection Analysis based on: • LIFT Chart • ROC Curve
Prospect Identification from a Credit Database using Regression, Decision Tre...
Prospect Identification from a Credit Database using Regression, Decision Tre...
Akanksha Jain
Decision Tree - C4.5&CART
Decision Tree - C4.5&CART
Xueping Peng
Telco Churn Roi V3
Telco Churn Roi V3
hkaul
Flight Delay Prediction Model (2)
Flight Delay Prediction Model (2)
Shubham Gupta
This presentation is from a webinar on May 27,2016. Watch the video online at: http://go.cgbi.com/HE-Webinar.html This presentation will introduce you to public resources which can help you to identify students at-risk and increase new student retention rates significantly. Learn how to integrate public data with internal student data using tools you most likely already own to gain a greater insight into your student body, get a leg up on other schools competing over the same students and validate the performance of your recruiting efforts. We'll walk through a real case study to introduce tips for leveraging data to predict not only the success of your student retention efforts, but the success of your students. The webinar includes a demonstration of public data visualized in Tableau.
Making a Difference in Your Student Retention - Identifying Analytic Trends f...
Making a Difference in Your Student Retention - Identifying Analytic Trends f...
CCG
Learn tips on how to provide the best experience and build strong relationships with your customers. See what crucial data should be constantly gathered and analyzed in order to decrease churn and improve customer retention.
The Leading Reasons for Customer Churn in SaaS
The Leading Reasons for Customer Churn in SaaS
Grigore Raileanu
Presentation at SAS Analytics conference 2014 Predictive analytics has been applied to solve a wide range of real-world problems. Nevertheless, current state-of-the-art predictive analytics models are not well aligned with business needs since they don't include the real financial costs and benefits during the training and evaluation phases. Churn modeling does not yield the best results when it's measured by investment per subscriber on a loyalty campaign and the financial impact of failing to detect a churner versus wrongly predicting a non-churner. This presentation will show how using a cost-sensitive modeling approach leads to better results in terms of profitability and predictive power – and is applicable to many other business challenges.
Maximizing a churn campaign’s profitability with cost sensitive predictive an...
Maximizing a churn campaign’s profitability with cost sensitive predictive an...
Alejandro Correa Bahnsen, PhD
A slide on how to build a decision tree using c4.5 Algorithm
Decision tree Using c4.5 Algorithm
Decision tree Using c4.5 Algorithm
Mohd. Noor Abdul Hamid
Founders: Are you being honest about your churn rates? Really, really honest? Probably not. At best, you track your customer churn and neglect the other, more important churn rates. Do you know your revenue churn? Your monthly subscription churn? What about your net churn? If you don't, you need to. In startups, ignorance isn't bliss; it's death. Use our No-BS Guide to learn about the four most important churn rates, what they mean, and how to calculate them. Check out our free, automatic churn calculator at the end!
The No-BS Guide to Understanding (and Calculating) Churn
The No-BS Guide to Understanding (and Calculating) Churn
Close.io
These slides are from a talk I at the papis conference in Boston in 2016. The main subject is uplift modelling. Starting from a churn model approach for an e-gaming company, we introduce when to apply uplift methods, how to mathematically model them, and finally, how to evaluate them. I tried to bridge the gap between causal inference theory and uplift theory, especially concerning how to properly cross validate the results. The notation used is the one from uplift modelling.
Beyond Churn Prediction : An Introduction to uplift modeling
Beyond Churn Prediction : An Introduction to uplift modeling
Pierre Gutierrez
1 Get satisfaction data in Kissmetrics under 5 minutes Growth hacking t = time spent on customer relationship (perceived t - real t) = margin Automation + Scalabilty = Growth Marketing #1 Get satisfaction data in Kissmetrics under 5 minutes o/ NPS: NET PROMOTER SCORE How likely is it that you would recommend our service to a friend or colleague? IT STARTS WITH A SIMPLE EMAIL Trial Expired NPS Survey 1 day after SEND AN EMAIL AT THE END OF YOUR TRIAL PERIOD Each image has a unique trackable link On click, send to your website and record the value in your analytics Done ! Email template available at https://www.sendwithus.com/resources/templates RECORD THE DATA TO KISSMETRICS DATA ANALYSIS Higher NPS scores lead to better retention #2 Capture more data Guillaume Cabane Mention “ Net Promotor Score isn’t just a metric - it’s an excuse to dig deeper SEND USERS TO A FORM Send survey respondents to a form where they can give a reason for their score. Engagement is better while inside your app, so when possible display the survey in-app first, and default to the email in backup USE A DEDICATED NPS APP DATA ANALYSIS Churn by NPS reason #3 Action! Increase sales, reduce churn and create happiness Increase sales NPS FOR YOUR TRIAL USERS The key is to get that data back into Kissmetrics AND other tools, which is possible with all NPS solutions offering webhooks … or a Segment.com integration MAKE USE OF THAT DATA Reminder E-mails Display survey in-app for 5 days Send survey dataGet customer data Follow-up emails MAKE USE OF THAT DATA Trial expired In App Email Surveys Answered NPS Survey Review 100 mentions DISCOUNT AUTOMATION 72% Opened 9% Replied 15% Clicked TRIAL EXTENSION AUTOMATION Results Cumulated number of upgrades for NPS respondents. Month 1 Month 2 Month 3 x2 x3 x3.5 Reduce churn NPS FOR YOUR PAYING USERS Guillaume Cabane Mention “ A NPS rating is true at one point in your user’s lifecycle. Making conclusions on events happening months after is meaningless; it’s better to ask opinion every so often. MAKE USE OF THAT DATA Handled by handUpgraded NPS Survey 1 month after REACHING OUT GETTING DATA CUSTOMER SUCCESS PRODUCT FEEDBACK MULTI-TASKING NPS THINGS FIT TOGETHER Answer Rate Qualitative Handling Automation We are learning more On our customers, increasing sales, decreasing churn, and creating happiness.
How to Reduce Churn by 50% and Increase Customer Happiness with NPS Processes
How to Reduce Churn by 50% and Increase Customer Happiness with NPS Processes
Kissmetrics on SlideShare
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์ ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
BAINIDA
Destacado
(20)
Predictive Model for Customer Segmentation using Database Marketing Techniques
Predictive Model for Customer Segmentation using Database Marketing Techniques
An Introduction to boosting
An Introduction to boosting
Insights into Customer Churn
Insights into Customer Churn
Presentation - Predicting Online Purchases Using Conversion Prediction Modeli...
Presentation - Predicting Online Purchases Using Conversion Prediction Modeli...
Behavioral Track & Trigger featuring Browse Abandonment Campaigns
Behavioral Track & Trigger featuring Browse Abandonment Campaigns
A combination of decision tree learning and clustering
A combination of decision tree learning and clustering
How to Perform Churn Analysis for your Mobile Application?
How to Perform Churn Analysis for your Mobile Application?
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...
Prospect Identification from a Credit Database using Regression, Decision Tre...
Prospect Identification from a Credit Database using Regression, Decision Tre...
Decision Tree - C4.5&CART
Decision Tree - C4.5&CART
Telco Churn Roi V3
Telco Churn Roi V3
Flight Delay Prediction Model (2)
Flight Delay Prediction Model (2)
Making a Difference in Your Student Retention - Identifying Analytic Trends f...
Making a Difference in Your Student Retention - Identifying Analytic Trends f...
The Leading Reasons for Customer Churn in SaaS
The Leading Reasons for Customer Churn in SaaS
Maximizing a churn campaign’s profitability with cost sensitive predictive an...
Maximizing a churn campaign’s profitability with cost sensitive predictive an...
Decision tree Using c4.5 Algorithm
Decision tree Using c4.5 Algorithm
The No-BS Guide to Understanding (and Calculating) Churn
The No-BS Guide to Understanding (and Calculating) Churn
Beyond Churn Prediction : An Introduction to uplift modeling
Beyond Churn Prediction : An Introduction to uplift modeling
How to Reduce Churn by 50% and Increase Customer Happiness with NPS Processes
How to Reduce Churn by 50% and Increase Customer Happiness with NPS Processes
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Similar a 11/04 Regular Meeting: Monority Report in Fraud Detection Classification of Skewed Data
https://www.irjet.net/archives/V9/i5/IRJET-V9I523.pdf
Automobile Insurance Claim Fraud Detection using Random Forest and ADASYN
Automobile Insurance Claim Fraud Detection using Random Forest and ADASYN
IRJET Journal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data. the literature on the field offers a wide spectrum of algorithms and applications. however, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application. This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning. for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms. the research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.
Analysis of Common Supervised Learning Algorithms Through Application
Analysis of Common Supervised Learning Algorithms Through Application
aciijournal
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data.the literature on the field offers a wide spectrum of algorithms and applications.however, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application. This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning.for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms.the research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATION
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATION
aciijournal
Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
Predicting automobile insurance fraud using classical and machine learning mo...
Predicting automobile insurance fraud using classical and machine learning mo...
IJECEIAES
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
ahmad abdelhafeez
Supervised learning is a branch of machine learning wherein the machine is equipped with labelled data which it uses to create sophisticated models that can predict the labels of related unlabelled data. the literature on the field offers a wide spectrum of algorithms and applications. However, there is limited research available to compare the algorithms making it difficult for beginners to choose the most efficient algorithm and tune it for their application. This research aims to analyse the performance of common supervised learning algorithms when applied to sample datasets along with the effect of hyper-parameter tuning. for the research, each algorithm is applied to the datasets and the validation curves (for the hyper-parameters) and learning curves are analysed to understand the sensitivity and performance of the algorithms. The research can guide new researchers aiming to apply supervised learning algorithm to better understand, compare and select the appropriate algorithm for their application. Additionally, they can also tune the hyper-parameters for improved efficiency and create ensemble of algorithms for enhancing accuracy.
Analysis of Common Supervised Learning Algorithms Through Application
Analysis of Common Supervised Learning Algorithms Through Application
aciijournal
Accounting for Variance in Machine Learning Benchmarks Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
Accounting for variance in machine learning benchmarks
Accounting for variance in machine learning benchmarks
Devansh16
Building_a_Readmission_Model_Using_WEKA
Building_a_Readmission_Model_Using_WEKA
Sunil Kakade
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
C0413016018
C0413016018
ijceronline
This study proposes a scalable asset selection and allocation approach using machine learning that integrates clustering methods into portfolio optimization models. The methodology applies the Uniform Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of 20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio allocation. Overall, this study highlights the potential of machine learning in portfolio optimization, providing a useful framework for investment practitioners.
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTION
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTION
IJCI JOURNAL
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validation - Professor Daniel Martin Katz + Professor Michael J Bommarito
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Daniel Katz
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETS
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETS
Editor IJCATR
Nearly 80% of banks surveyed by Enigma are evaluating or already using machine learning in their sanctions screening programs. This deck enables you to move beyond the buzzwords and provides key information about machine learning for your sanctions screening strategy. Here's what the deck contains: - Why sanctions screening can benefit from machine learning - Which machine learning models are a good fit for sanctions screening - What best practices you need to successfully integrate machine learning
Machine learning for sanctions screening
Machine learning for sanctions screening
Enigma
https://www.irjet.net/archives/V9/i1/IRJET-V9I1163.pdf
Automobile Insurance Claim Fraud Detection
Automobile Insurance Claim Fraud Detection
IRJET Journal
With the advent of technology, there has been an excessive use of cellular phones. Cellular phones have made life convenient in our society. However, individuals and groups have subverted the telecommunication devices to deceive unwary victims. Robocalls are quite prevalent these days and they can either be legal or used by scammers to trick one out of their money. The proposed methodology in the paper is to experiment two ensemble models on the dataset acquired from the Federal Trade Commission(DNC Dataset). It is imperative to analyze the call records and based on the patterns the calls can classify as a robocall or not a robocall. Two algorithms Random Forest and XgBoost are combined in two ways and compared in the paper in terms of accuracy, sensitivity and the time taken.
Empirical analysis of ensemble methods for the classification of robocalls in...
Empirical analysis of ensemble methods for the classification of robocalls in...
IJECEIAES
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
Classification of Breast Cancer Diseases using Data Mining Techniques
Classification of Breast Cancer Diseases using Data Mining Techniques
inventionjournals
Cyb 5675 class project final
Cyb 5675 class project final
Craig Cannon
Presentation on Financial Fraud Detection using Healthcare
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
ShrutiGarg649495
Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques. http://research.sabanciuniv.edu.
Re-mining item associations: Methodology and a case study in apparel retailing
Re-mining item associations: Methodology and a case study in apparel retailing
Gurdal Ertek
Paper-Allstate-Claim-Severity
Paper-Allstate-Claim-Severity
Gon-soo Moon
Similar a 11/04 Regular Meeting: Monority Report in Fraud Detection Classification of Skewed Data
(20)
Automobile Insurance Claim Fraud Detection using Random Forest and ADASYN
Automobile Insurance Claim Fraud Detection using Random Forest and ADASYN
Analysis of Common Supervised Learning Algorithms Through Application
Analysis of Common Supervised Learning Algorithms Through Application
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATION
ANALYSIS OF COMMON SUPERVISED LEARNING ALGORITHMS THROUGH APPLICATION
Predicting automobile insurance fraud using classical and machine learning mo...
Predicting automobile insurance fraud using classical and machine learning mo...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Analysis of Common Supervised Learning Algorithms Through Application
Analysis of Common Supervised Learning Algorithms Through Application
Accounting for variance in machine learning benchmarks
Accounting for variance in machine learning benchmarks
Building_a_Readmission_Model_Using_WEKA
Building_a_Readmission_Model_Using_WEKA
C0413016018
C0413016018
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTION
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTION
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
Legal Analytics Course - Class 6 - Overfitting, Underfitting, & Cross-Validat...
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETS
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETS
Machine learning for sanctions screening
Machine learning for sanctions screening
Automobile Insurance Claim Fraud Detection
Automobile Insurance Claim Fraud Detection
Empirical analysis of ensemble methods for the classification of robocalls in...
Empirical analysis of ensemble methods for the classification of robocalls in...
Classification of Breast Cancer Diseases using Data Mining Techniques
Classification of Breast Cancer Diseases using Data Mining Techniques
Cyb 5675 class project final
Cyb 5675 class project final
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
Tanvi_Sharma_Shruti_Garg_pre.pdf.pdf
Re-mining item associations: Methodology and a case study in apparel retailing
Re-mining item associations: Methodology and a case study in apparel retailing
Paper-Allstate-Claim-Severity
Paper-Allstate-Claim-Severity
11/04 Regular Meeting: Monority Report in Fraud Detection Classification of Skewed Data
1.
Minority Report in
Fraud Detection: Classification of Skewed Data Clifton Phua, Damminda Alahakoon, and Vincent Lee SIGKDD 2004 Reporter: Ping-Hua Yang
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
M odeling
18.
19.
Modeling
20.
21.
22.
23.
24.
25.
Results
26.
27.
28.
Discussion
29.
30.
Descargar ahora