Using ROC curves and area under curves (AUC), we identify the better fitting model. We seek to quantify the “cost,” in terms of player quality, that is incurred when a team chooses to wait until a later round to draft a player at a particular position. While removing the missing data, half of the information is lost and as a result the logistic regression model built gave poor results in prediction with AUC as 0.6 and misclassification rate as 0.54. Advanced Techniques: Various Neural Network Architectures are tried with word embeddings created from questions’ text, CNNs are deployed for extracting spatial information from the images, RNNs are harnessed for generating sequential data of words, Networking with other people in the beauty industry, Learn more about the latest trends in the beauty industry, Get your product/company recognized at an awards event hosted by the company. There are a lot of recommender systems in the existence, most of them either cannot recommend a movie to its existing users efficiently or to a new user. Here the barriers will be analyzed using experimental design, statistical inference, queueing analysis, and simulation modeling. Joel C. Weaver, Enhancing Classroom Instruction by Finding Optimal Student Groups, March 3, 2014 (David Rogers, Jeffrey Camm) Identifying who will vote and the party preference of these voters is key to winning political races, particularly in close races. This implies that if the smaller company defaults in paying back its loan, the company owning or guaranteeing for it is responsible for the entire loan taken by the smaller company. The project is concerned with identifying a solution that could be used to detect and filter out sites containing fake news for purposes of helping users to avoid being lured by clickbait. Support vector machine model was chosen as our final model as it gave the lowest misclassification rate of 0.08 and highest area under curve (AUC) value of 0.97. Mark McNall, From Sinatra to Sheeran: Analyzing Trends in Popular Music with Text Mining, April 2019, (Dungang Liu, Edward P. Winkofsky) The fundamental components of a neural network are the activation function, the error measurement and the method of backpropagation. Mark Richard Boone, Minimization of Deadhead Travel and Vehicle Allocation in a Transportation Network via Integer Programming, August 19, 2011 (Jeffrey Camm, Michael Magazine) On a portfolio level, customers are taking on more debt after BT, but this behavior is highly dependent on the type of customer. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Teams can do this a number of ways either through free agency, the NBA draft or outright trades. Yuanyuan Niu, A Study on Bond Yield Curve Forecasting, July 26, 2012 (Yan Yu, Uday Rao) These predictions help the store managers to comprehend the store performance against performance indicators and prepare in advance, the measure that should be taken to improve sales for example, introducing promotional offers, understanding competitor market, etc. Two methodologies are outlined in this paper for improved metrics and dashboards. Examination of rankings for potential contribution metrics among this cluster revealed high ratings for factors such as years practicing medicine and years as a faculty member, as well as research involvement and team fit. Another learning is that generally, fans buy their main food items (hot dog, burger, pizza) before the game and then beverages, desserts and snacks during the game. Silky Abbott, Simulation of the Convergys Contact Center in Erlanger, KY, July 24, 2012 (David Kelton, Jaime Newell) This project is using a dataset from Kaggle from a Google Merchandise Store to predict revenue per customer in order to help companies make better use of their marketing budgets. Quality is credited by many ways, which includes physiochemical properties, and sensory tests. This brief summarizes residential trends using the above data from the years 1998 through 2018 for Hamilton County. Vikas Konanki, Credit Scoring of Australian Data Using Logistic Regression, April 2015, (Jeffrey Camm, Yichen Qin) The results surpass prediction performance from previous published work, it was possible to achieve a prediction of 97.49%, higher than that reported in the literature. The simulation study conducted suggests that if forecasting the probability of bankruptcy is of interest, complete data sampling technique provides more accurate results. Since the Data Mining course at University of Cincinnati is heavily R oriented, through this project, I have tried to convert the labs and homework from R to python notebooks for the students interested in doing the coursework in Python as well. The 95% confidence interval for the mean is 359,728.19 to 497,168.63. The last project is to analyze the relationship between Average Sales Price and Sales Units. Recommendations include planning lower volume kitchen positioning around the product mix of the hour, utilizing positioning standards in higher volume stores to their fullest, and using the acquired statistical evidence to necessitate a group collaboration with McDonald’s for solution development. It is composed of two projects. The analysis revealed that factors such as benefits, club qualification, contract type, club level, agency distribution region, persistency rates, home agency state, AIC (Ametrias Investment Corporation) affiliation are some of the factors which have an impact on an agent’s success and his sales revenues. Different models are built and then compared to assess the accuracy. The model is developed to predict which previously purchased products will be in a user’s next order. Top significant factors that affect retention were identified through logistic regression modeling, decision trees and random forest modeling. This research seeks to understand the role of sugar ingredient and lower sugar propositions as well as other factors in the [food and beverage] consumer purchase decision, including: brand, variety, all natural claim, and added benefit. Modeling a time series and predicting future values is an important skill. The data set chosen is having the expense information for a school where each observation is labelled according to the department /object-bought / functionality / Class / user etc. With predictive modeling and clustering analysis the industry can do away with old methods that rely on executives’ intuition and make rate charging based off of numbers. Anjali Chappidi, Un-Crewed Aircraft Analysis & Maintenance Report Analysis, August 2017, (Michael Fry, Jayvanth Ishwaran) Sai Avinash Gundapaneni, Forecast use of a city bike-share system, April 2015, (Jeffrey Camm, Yichen Qin) For GSRS, the same number of latent factor of 19 is used and the cluster number of both users and items is set to default value of 10. The visualizations in Netjets were developed using disparate tools on a need basis without any set of corporate standards. Various modeling techniques will be investigated to understand the credit risk: generalized linear models (McCullagh and Nelder, 1989), classification and regression tree models (Breiman et al., 1984), and chi-squared automatic interaction detector (Kass, 1980). By analysing users reviews, a company can be aware of how its users feel. A simple feature extraction technique was employed to process the raw data, and then various machine learning algorithms were applied for multi-class classification. Pushpa Jha, Natural Language Processing: Topic Modeling, August 2020, (Peng Wang, Yan Yu). The project also tried to quantify the risk and safety issues in a cash vs a non-cash trip. The following report is an intern’s contribution toward the demonstration of the capabilities of Tableau through creating PowerPoint presentations, Tableau Story Points, Tableau Dashboards, identifying principles for structuring data, cleaning up datasets, and refining already created dashboards. and this is how each of these businesses maintains their edge in the highly competitive online business world. In this project we use various statistical tools to efficiently target members that need additional points to earn a reward certificate and to maximize certificate redemption which results in incremental sales to the company. Krishna Chaitanya Vamaraju, Recommender System on the Movie Lens Data Set, July 2018, (Dungang Liu, Olivier Parent) In an era of big data technologies, recommendation engines play a crucial role in increasing the number of purchases per customer for companies. With the use of simulation, this project will examine the parameters such as feeding frequency and number of meals. Lending club offers personal loans in the range of $1000 - $35000 to applicants from various categories. From the results, we illustrate the strengths of both non-parametric and parametric methods. Second, Bayesian methods are used to improve on the author’s own predictive models. MadTree is seeking to increase its market penetration in the areas it currently sells its craft beer, so I have developed linear regression models to illustrate the relative significance of different predictor variables. The optimization procedure was designed at three different levels: optimizing the selling area of products in a whole store; optimizing the floor area of products within each floor of a store; and optimizing the selling space within each product category in a store. Some preprocessing is done on the data to prepare for analysis and modeling. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses. Sagar Vinaykumar Tupkar, Predicting Credit Card Defaults, July 2016, (Yichen Qin, Peng Wang) Anjali Gautam, Prioritization of Calls, August 2020, (Michael Fry, Siddharth Krishnamurthi). Therefore, in order to reduce costs and improve efficiency, it is important to have a good prediction model for separating those customers who are likely to respond positively to the marketing campaign. A large portion of energy is used for HVAC systems. Objective of study was to develop predictive model to identify the variables responsible for churn of customers and predict potential customers which may churn out of telecom services. These models will help Crossroads church recognize the key variables responsible for people not experiencing any spiritual growth. Parama Nandi, Response Modeling in Direct Marketing, February 25, 2010 (Martin Levy, Jeffrey Camm) Statistical models help uncover relationship between different factors, identify the important/significant factors and give a quantitative measure of their impact on the outcome. During my internship, I am working on various projects including report generation and ad-hoc analysis. We were to build our models on the train data and validation data. Major League Baseball (MLB) is composed of thirty teams spread out across the United States, including one team in Toronto, Canada, so travel is a large aspect of professional baseball. A model is built with logistic regression and area under PRROC (Precision-Recall curve) is used to show model performance of each method. Films have always been an integral part of the world of entertainment. The results from the word embedding approaches are found to be very promising and can provide a scalable solution for this task. However, when compared against the results from Gradient Boosting, the LASSO models produced greater root mean squared error. Companies typically send promotional materials to about 20% of their potential buyers from their lists. Sagar Umesh, PPNR: PCL (HELOC) Balance Forecast, July 2016, (Yan Yu, Omkar Saha) Customers were grouped into frequent shoppers, yearly shoppers, and one-time buyers. Es ist nur eine kostenlose Registrierung bei auto.inFranken.de notwendig. Junbo Liu, Predicting Movie Ratings with Collaborative Filtering, July 2017, (Peng Wang, Zhe Shan) Credit scoring is one of the data-mining research areas, and is commonly used by banks and credit-card companies. The internship at the City of Cincinnati gave me an opportunity to use Excel, Tableau, Arc maps and Visio for process improvement and data visualization. It shows how companies can leverage public data to develop analytical insights when constrained by availability of reliable data. The objective of this project is to explore the factors that are related to employee attrition through data wrangling and building a model that could be used to predict whether an employee would leave the company or not. This project uses various regression modeling and analytical techniques to build a statistical model to help predict product sales. Natural language processing can help organizations build custom tools to process this information to gather valuable insights that drive businesses. The mean squared error is used to compare the two models. In this project, we present a description of LMMs by building a model in the field of marketing, using a step-up model-building strategy with which we can illustrate the hierarchical structure of LMMs. Rasesh Garg, Movie Recommender System, August 2020 (Michael Fry, Peng Wang). Therefore, better care coordination promises to reduce readmission rate which minimizes cost and improve patients’ lives. The result outcomes show a 99.8% accuracy utilizing a strategic classifier. The Logistic Regression model was chosen as the best model for the Kickstarter project classification with an accuracy of 0.9996 and AUC of 0.9999. Count data are one of the most common data types and many statistical models have been developed for their analysis. Piyush Verma, Building a Music Recommendation System Using Information Retrieval Technique, July 2018, (Peng Wang, Yichen Qin) The problem we are trying to address is to predict whether an attack will result in casualties given the nature and characteristics of the attack. Several well-known regularization methods, such as the Tikhonov regularization method, the Landweber regularization method, and the conjugate gradient method, are discussed and analyzed. This kind of analysis is extremely useful for the companies which can tailor and time its content based on the analyzed data to generate maximum readership on its site thereby generating more revenue. The average procedure charges ranges from around $27,000 up to nearly $100,000. One of the product features provides insights for churn propensity which will form the key focus of my paper. The objective is to determine the best suited regression algorithm to predict the customer retention from a list of predictors that are believed to influence it. The increasing cost of marketing campaigns, coupled with declining response rates, has encouraged marketers to search for more sophisticated techniques. Krishnan Janardhanan, Win Probability Model for Cricket, July 2017, (Peng Wang, Ed Winkofsky) The drug is a “Buy and Bill” drug which means hospitals buy the drugs in advance and stock it and then bill the payers according to the usage. Keerthana Regulagedda, Diabetes Prediction In Pima Indian Women, July 2017, (Yan Yu, Michael Magazine) Specifically, this study is designed to answer the question "whether current personnel and emergency equipment resources assigned to fire stations is able to meet the increasing demand of fire and medical-emergency response service." Online reviews contain rich information about how customers perceive brands in a product category, but the information can be difficult to extract and summarize from unstructured text data. With the impulsion of World Wide Web and massive growth in computer networks, network security is becoming a key issue to tackle. Because of Globalization, people all around the world are now able to access different kinds of music. Post which, application of machine learning algorithms to predict the final IMDB score for a movie. An average of nearly 7,000 students become dropouts each day. Swaraj Mohapatra, Process mining, missing link between model-based process analysis and data-oriented analysis techniques, April 2015, (Yan Yu, Yichen Qin) University of Cincinnati is also located in Hamilton county. So, for any business churn prediction would prove an important investment in terms customer lifetime value and marketing. This model can be used by the telecom company to target customers with a potential to churn and retain them. After fitting the logistic regression model, we will move to the tress approach for the model building. Since I am interested in robustness against outliers, I chose to measure fit using Mean Square Error. Also, credit card business possesses a risk to the bank whether or not the customer is able to fully repay the amount borrowed using the credit card and there comes the role of collection strategies as in to recover the amount or avoid the customer from being charged-off. The final house prices are a continuous variable and are predicted using linear regression. Ishali Tiwari, Prediction of Wine Quality by Mining Physiochemical Properties, August 2019, (Yan Yu, Ishan Gupta) Did Unemployment Trends across America Contribute to the Rise of Donald Trump?, May 2020, (Dungang Liu, Edward Winkofsky). In this project, we applied multiple linear regression, univariate linear regression, random forest, XGBoost, and Artificial Neural Network, models. Huiqing Li, Statistical Analysis of Knee Bracing Efficacy in Off-road Motorcycling Knee Injuries, March 5, 2010 (Martin Levy, Yan Yu) We are interested in identifying the patterns of the enrollment of students in the STEM programs in UC and understand if factors like gender, race, etc. We utilize grouping schemes and logistic regression to analyze the data, and also deploy Tableau for data processing and data visualization. Elise Mariner, Analysis of American Modern Insurance Group Mobile Home Policies, July 2015, (David Rogers, David Kelton) The approach taken was to first identify similar behaving / performing stores and then to collectively roll out assortments optimized for such similar stores. Some of the benefits of this system if implemented were that Schwab would be: The desired system will not only help the team in monitoring but also the development team to understand the performance of the application and the infrastructure management team to understand the amount of server usage for capacity planning. Our analysis is meant to assist teams during the decision-making process of drafting players by quantifying the tradeoffs inherent in each potential decision. Further we found that most of the revenue increase of complete localization can be accomplished with far fewer than N assortments. The cost function was used to adjust the cost of false positives and was also used to choose the optimal cut-off probability for the models used. MICE (Multivariate Imputation via Chained Equations) was performed to impute the missing values in the data set. Arjen Robben 90. For providing the best viewing experience and retaining the users, it is important for the OTT platforms to seamlessly suggest movies to both existing and new users. The major clients for Dymatize are Amazon, BB.com, Walmart etc. Charles Brendan Cooper, What Matters Most? In saturated markets, customer retention has become more important and will bring a lot of benefits for companies. Google Analytics is the main tool used for the visitors' profile analysis. Traditional disease identification methods like human vision is time consuming and requires lot of human resource. 33 different special events have been hosted over the past five years. Binning is popular because of its ability to identify outliers and handle missing values. Feature variables describe characteristics of the cell nuclei present in the image. Gene networks built from top differentially co-expressed gene pairs showed a consistent enrichment for already-established SLE associated genes. They can help lenders decide whether an applicant is a good candidate for a loan. It is seen that Random Forest performs better than neural network. Chaitanya Peri, Predicting “No Real Spiritual Growth”, April 2015, (Jeffrey Camm, Peng Wang) From this, researchers will be able to examine the effects that physiological and environmental factors have on the test subjects without having to change a test subject's environment physically, thereby saving much time and money. It is difficult to determine the replenishment point if the sales information and lead time are unknown. As such, the identification of influential factors which lead to such substance usage is critical, and has been the subject of much research over the past few decades. If the development in the city continues over the next few years, the NFD will have to grow along with the city. Tee katuvalojen vikailmoitukset tästä. The classical clustering method is one-way. Concrete is so ubiquitous today that it is often taken for granted. Evaluation is done on area under the ROC curve between the predicted probability and the observed target. The current model requires a safety-time level defined for each item at each distribution center. The Root-Mean-Squared-Error between the logarithm of the predicted value and the logarithm of the observed value of housing price will be used to evaluate the models. This will be a damaging thing to the company, as the customer might not use the card later, and for the country, because a huge negative sentiment will put the consumption based economies at risk. LMS can be in-house or driven through third party software products. Forecasting has since long been one of the biggest factors for deciding the success or failure of an organization. Machine learning classification models such as Logistic Regression and Gradient Boosting Machine were used for this purpose. Jo-Ann has a brick-and-mortar model with a little online presence (~4%) which is facing the same challenges on a day-to-day basis. This project focuses on consumer buying behavior in retail grocery stores across the United States. Currently, the next-best action planning is driven by analyzing the free text across multiple departments such as Oncology & Respiratory. Given a fixed payroll, and focusing purely on the offensive side of the ball, how should a baseball team assign its funds to give itself the highest average number of runs possible? Automating data input processes and updating dashboards are critical to on time services. University Hospital's Emergency Department (ED) treats nearly 100,000 patients annually. Tracking this data and reporting it in the format desired is therefore a huge and important task. First, a deterministic model was designed; afterwards probabilistic components are introduced. Our methodology includes a consulting approach to first understand the problem from client stakeholders, and then apply data cleaning, wrangling, exploration, and visualizations to uncover trends and insights. Dashboards prove to be extremely useful in analysis as they show the trends for different metrics over the month, and help us dig down deeper through the multiple layers of information. The UC Foundation team would like to predict probability of large future donations and target the donors effectively. This project provides a detailed analysis of the factors that influence applicants' decisions, based on the applicant's characteristics, from records from 2002 to 2012. In this project, we attempt to train generative models and use these models to create music. Recommender systems personalize customer experience by understanding their usage of the system and recommending items they would find useful. Accurate demand forecasting is essential for operational planning. In this report we look at one instance where call notes of customers is used as an indication of possible attrition. The unsuccessful students are defined as students enrolled in a program for more than 6 years and didn't graduate. However, the money put into these rookie contracts doesn’t always yield returns on the playing field. Stochastic Optimization is a useful tool to answer these questions. The final call in such cases is subjective and a "good" decision depends on the choice of decision maker, but this methodology aims to give the decision maker tools to facilitate and inform the decision making process. The results from this project could serve as a guideline for retailers in assortment planning strategy. We also predict the expected revenue for each zip code across United States. Fans of past year's teams started questioning whether the 2006 team would have been able to pull off such a drive. The predictions from each of the models were compared for concordance and area under the curve (AUC) metrics. The main purpose of the first project is to automate the process that used to be manually accomplished within different databases, RStudio was used and substantially reduced the time required to produce flat files for further usage. Chris Fant, American Athletic Conference Football Division Alignment: Minimizing the Travel Distance and Maintaining Division Balance, July 24, 2014 (David Rogers, Michael Fry) The time has now come to expand on this research with the use of simulation. It also has minimum misclassification rate. I finally got some games with my Brasilver Team, in that new Silver tournament. First model is combination of individual “Fourier Time Series” models for each hour from 9 AM to 5 PM during a regular day. Microsoft Excel, SQL Server Management Studio, and SAS are the main tools used in this study. Analytical tools are used to perform a thorough data exploration that leads us to key insights that would be beneficial in the modeling process. Yash Sharma, Image Recognition, July 2017, (Bradley Boehmke, Liwei Chen)

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