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This right kind of targeting not only drives sales but also saves business resources and maximizes profit. Process mining is used to extract process related information from event data. In the multiplayer online video game League of Legends, map/vision control is one of the key factors to win games strategically. An application using real call data from the City of Cincinnati is presented. The highest returns come with the smallest firms with the highest default risk. A scoring model, such as logistic regression, is used to compute activation scores from the set of attributes and these are ranked in descending order of activation likelihood. Sugar continues to be a “hot topic” for [food and beverage] consumers and a driver of recent buyer and volume declines in the aisle. Grouping volunteers by their donation behavior allowed United Way better to evaluate the interaction between volunteering and donating. The importance of process mining can be showcased by the astounding growth of event data. A Travelling Salesperson Problem (TSP) model is solved to determine the shortest delivery tour. The model classifies subscribers from non-subscribers based on client information – default status, contact type; campaign information – month, previous campaign outcome, number of contacts performed in current campaign and number of days since the customer was last contacted; and, socio-economic factors – number of employees and consumer confidence index. The study is based on a competition conducted by “Yelp” as part of a yearly Data Challenge. The School of Human Services in the College of Education Criminal Justice, and Human Services offers a Bachelor’s Degree in Athletic Training. Verizon is one of its Clients. The focus of this study is to predict the transfer values of incoming players to the English Premier League while identifying the most important components that go into formulating a player’s transfer amount. Neural network breaks down complex problems into simpler understandable form. How is a Lego set priced? The goal is to identify patterns among these members who are likely to churn and eventually predict when a member is going to churn. Manish Kumar, Intelligent Allocation of Safety Stock in Multi-item Inventory System to Increase Order Service Level and Order Fill Rate, June 3, 2009 (Amitabh Raturi, Michael Magazine) Within this segment, the China-to-US trade flow is the largest by volume, inter-nation trade route. The models are then evaluated using ROC curve and precision-recall plots for a different number of recommendations. Within the I.T. The data set used for the analysis has been obtained from UCI Machine Learning repository. Operations management relies on production and scheduling to steer the business. Predictions from each of these models were compared and metrics such as precision, recall and F1 scores were used to judge the accuracy of the model predictions. Obtaining the attention and interest of a shopper can be extremely difficult, nonetheless so when a credit card is being promoted by a retailer whose marketing budget does not stand up to those of larger banks. These models use 19 variables that span a customer’s demographic information, credit and previous campaign information available with the bank, and macro-economic variables. Keeping the cheapest vendor first, the process aims at reducing the cost while increasing the procurement of good phone numbers. The final model will help us to understand what variables are significant to prevent frauds related to mobile deposits while being used daily. IBM Behavior Based Customer Insight for Banking solution gives the information and insight that is needed to provide proactive service to client's customers. If the development in the city continues over the next few years, the NFD will have to grow along with the city. The income prediction has also been an area of interest for many companies as this information would enable them to achieve greater understanding of consumer and market behavior. Ashok Maganti, Internship with Argus Information and Advisory Services, August 2016, (Harsha Narain, Michael Magazine) Once the model is finalized it would be implemented and the scores generated from this model will be used in deciding whether the Credit Card account is fraud or not. Being a niche program, attrition of the students is desired to be minimal. The one key reason why the recommendation systems have become ubiquitous in the modern world is the enormous options people have on the internet. A total of 315 hotspots have been identified with ‘Los Angeles’ county in California having the highest score and thus the most susceptible to the spread of novel COVID–19 virus. Apurva Bhoite, Predicting Success of Students at Medical School, July 2017, (Peng Wang, Liwei Chen) It helps them in structuring store lay out, designing various promotion and coupons and combining all with a customer loyalty card which makes all the above strategy even more useful. The analysis concludes that the author ‘Ryan McCollough’ garners maximum views, around 90% of the total, through his posts. The data is first introduced, a data dictionary is created for the reader to further understand the data, the data is then cleaned, and then some initial exploratory analysis is done. The objective of the current analysis is to use the machine learning methods and NLP techniques to analyze text data. Maitrik Sanghavi, Member Churn Prediction & CK Health/Goals Dashboards, July 2018, (Michael Fry, Rucha Fulay) This, along with the shift towards outcome-based care models, motivates hospitals to become more efficient and provide high-quality, cost-effective care. The second project uses SAS programming to manipulate the performance data of a call center that has operations in multiple sites and business areas, and to help analyze its improvement in terms of AHT (average handling time, a metric to measure the time a representative spends handling an inbound call). The results from both methods can be considered equivalent and an equivalence test is applied to determine the number of iterations of Monte Carlo simulation. The evaluation methods will be based on common binary evaluation parameters. I plan to try Regression, Decision Trees, Gradient Boosting and fit the best model to the data. For example, It can send alerts and targeted offerings and provide insights that help banks to develop direct marketing campaigns. It is discovered that the age and financial status are the largest and most important differentiators for the two population groups. The customer experience can be increased manifold if Executive reports are made user-friendly and in such a manner that the executives are encouraged to see patterns and trends in data, and to even question the data. Arathi Nair, Demand Forecasting for Low-Volume, High-Variability Industrial Safety Products under Seasonality and Trend, December 4, 2013 (Uday Rao, David Kelton) The team contributes to the growth of customers by providing clean, consolidated and consumable data insights. Modeling a time series and predicting future values is an important skill. This analysis focuses on a psychographic segmentation of the consumers within Bank XYZ’s geographic footprint and identifies the unique groups that exist based on their attitudes, needs, behaviors and beliefs. The dataset used is custom sourced, which combines their internal point-of-sale data with 50+ sourced public datasets that include financial indicators, demographic indicators, and risk factors. A handicapping technique is useful in comparing performances of different ages and genders in long-distance running. In the era of artificial intelligence, social media can be leveraged to answer a lot of questions for insurance companies. Within more than 3 million SKUs involved and more than 2 million of these still unclassified, the current pace and accuracy of classifying these products was not sustainable. Consequently, I will solve the following questions in this project:  1. find out the most important variables in predicting house price. 2015 (Jeffrey Camm, Michael Fry) However, for continuity in growth, its essential to retain their workforce and make the employees feel valued. It is prudent to use deep Neural Networks for complex problems such as image processing. The study comprise of designing the experiment, gathering datasets, analyzing and developing new business insights. This research project uses a data driven approach to tackle the issue of heart disease in individuals. Primary obstacle in this exercise is – multiple levels of seasonality present. The SEDEA model, implemented in R, uses cost and quality measures for each hospital to calculate the hospital efficiency scores and ranks the hospitals accordingly. Using clustering analysis, the different problems were categorized into three sets by using factors as proxies for difficulty and complexity. If a customer is not likely to renew their tickets, the athletic department can take preemptive measures to retain the customer. Based on the results, the Lasso model with λ equal to 403.429 are preferred according to the performance of the results. The project was divided into four phases – creating the base data, forecasting charge offs using Markov Chain modeling, forecasting charge offs using loss curves and improving the overall efficiency of both the process and the model. The respondents were randomly asked either to rate or rank restaurants based on their past experience. So, Quora is keen to group multiple questions based on their meaning to reduce redundancy and improve overall convenience for users. Areas of relevance include cellular manufacturing. The project explores automated prediction of duplicate questions given two questions from the Quora platform through predictive models. EG America is looking to maximize profits for their beer category through pricing analytics. We will describe how to include them in the simulation model. Then we structured the data into Keras’s Neural Network feedable format, further we generated the embedding layer using the Gensim library in python. We also consider k-means clustering models. Linear regression was also attempted but failed for the data. The objective of this study is to use the energy data to build a model that can predict the Energy Star Score of a building and interpret the results to find the factors which influence the score. In this study, an in-depth customer attrition analysis was conducted which was followed by building a data model that predicted whether a customer would churn or not. Without the appropriate equipment available for at home workouts, the user saw decreases in strength in bench press (-4.35%), bent over row (-5.71%), deadlift (-11.21%), overhead press (-2.78%), and squat(-10%) which appears to be due to a reduction in overall sets per week. This analysis uses case times and physician behaviors in the main operating room with the intention that if current case times and turnover can be moved and would fit in the surgery center then gaining efficiencies would be very attainable by the very nature of a surgery center versus a larger hospital. Madhava Chandra, Analysis on Loan Default Prediction, July 2018, (Yichen Qin, Peng Wang) The idea of this paper is to analyze wine ratings with respect to its physicochemical properties. Churn prediction is critical for subscriber retention and profit growth in a subscription business. Many never question how concrete got here or how it can be trusted. Length of stay for re-directed and urgent-care patients, along with emergency-department and urgent-care center utilization, are compared across models. This study aims to build “Cincinnati crime category prediction models” in order to find an insight of the crime data through appropriate data visualization. However, such small businesses often lack expertise and technical know-how to perform requisite analysis. The ability to forecast credit loss accurately is of vital importance to every financial institution for both decision support and regulatory compliance. Second, on the same unit, repeated measures are performed over time or different spatial points on the same subject. Four models viz. For example, in the well-known K-means method, all the variables involving in the distance measure either come from variables, or records, but not both. It brings together customer data, analytics, business and marketing strategies for more than 15 million loyal Kroger Customers. Edmund A. Berry, National Estimates of the Inpatient Burden of Pediatric Bipolar Disorder in an Inpatient Setting. This study gave a list of important variables that affects the model and should be considered for predicting defaults. The goal of this report is to introduce process mining not only as a technique but also as a method. Every quarter a meeting is held and an opportunity is provided for each department to represent where they stand and what are the challenges they are facing. The end deliverable was a dashboard of the use cases that were developed by CST. Therefore, prediction accuracy is the key to success of a recommendation system. An Analysis of the 2003 and 2006 Kids Inpatient Databases (KID) Data, September 25, 2009 (Martin Levy, Pamela Heaton) Udayan Maurya, Telecom Customer Churn Analysis, June 2016, (Peng Wang, Yan Yu) If a bank is able to identify customers who have potential to spend more next year than what they have spent this year they can market better products to them and increase customer satisfaction along with their profits. I have also developed dynamic graphs and dashboards using Tableau’s geographic mapping feature as a means to visually compare different zip codes in various sales metrics including year-over-year increases in sales volume, sales volume per capita, and sales velocity. What type should be bought? Loan applicants can borrow money in the range of $1,000 to $40,000, and the investors can choose the loan products they want to invest in and make profit. Vinod Iyengar, Call Volume Forecasting and Call Center Staffing for a Financial Services Firm, March 13, 2009 (Uday Rao, Martin Levy) Currently, the next-best action planning is driven by analyzing the free text across multiple departments such as Oncology & Respiratory. This project is divided into various subparts each demanding exploratory data analysis, statistical computing, data munging, analytical dataset creation, and data visualization on multiple datasets to come up with recommendations and communicating results to the senior management. The role of analytics has increased a lot in the sports industry in recent times. This analysis showed that sauces appear four times as often in the cluster most responsive to promotions. In this project, an extended list of factors influencing a customer to re-loan or re-finance were identified from a comprehensive data mart, and a feature engineering process resulted in a consolidated analytical dataset to be used by the extended analytical team. Saurabh Wani, Movie Score Prediction, August 2020, (Yichen Qin, Liwei Chen). The results show that the robust optimization model performs the best in terms of stability. Sankirna Joshi, Multilingual Toxic Comment Classification, August 2020, (Yan Yu, Peng Wang). The advantage to our approach is it only requires transactional data that all retailers have. Forecasting trends for data that exhibits time series behavior at multiple levels can be very complex. From the results, we illustrate the strengths of both non-parametric and parametric methods. This study has been made to forecast the sales for OMI Industries. An innovative, integrated and customer-oriented retail business model, Omni-channel retailing is flourishing with the advent of the online and digital channels. If the right message is sent to the right group, it can help increase customer engagement and help generate higher profits at a lower cost. The System is set up in such a way that the whole process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. The predefined group index will make sure that once a variable is shrunk to zero, all the other variables in the same group will also be shrunk to zero. The data in UCI machine learning repository is taken. Consumer lending industry is growing rapidly with a wide spread of loan types and lending personal loans over internet is gaining huge importance. Thereafter, ARIMA modeling is used to derive the model. In this project models were developed to determine both a voter’s likelihood of voting in a general election and that voter’s likelihood of voting for the Democratic party for registered voters in Hamilton and Warren counties in Ohio. In addition to the domestic league, Greuther Furth participated in this seasons edition of the DFB-Pokal. When a person calls 911 there is an expectation that the call will be answered and dispatched to the nearby emergency response department within seconds. Lili Wang, Determining Sample Size in Design of Experiments, December 1, 2010 (Martin Levy, Yan Yu) For the random model, the Process Analyzer and Output Analyzer were used to analyze results. These variables were analyzed to determine which ones have the greatest impact on overall steam consumption. The features used in generating the model were: ram, battery power, pixel width, pixel height, the weight of the mobile, internal memory, mobile depth, clock speed and touchscreen. It occurs when a consumer applies for and uses credit under his or her own name, or uses a synthetic identity, to make transactions. The neural network is basically a framework for many different machine learning algorithms to work together and process the complex data inputs. I trained the network for 20 epochs with a learning rate of 0.0001. In this competition, objective is to tackle this natural language processing problem by applying advanced techniques to classify whether question pairs are duplicates or not. In order to predict future performance, it is important to examine the past observations for the dependent variable and all of the past observations for the explanatory variables. Jiaoyao Liu, Exploration of Map/Vision Control of Different Roles in League of Legends, May 2020, (Dungang Liu, Liwei Chen). We aim at processing data and getting it to a form that is presentable. In 2017, the Ohio Department of Medicaid announced a 1% reduction in Medicaid reimbursement for all hospitals that are deemed to have excessive readmissions. Current therapies for PCP suffer from significant treatment failures and a multitude of serious side effects. The best model for handwritten digit classification is SVM with an accuracy score of 99.35% on the test dataset and taking one-third time to train the hyperparameters in comparison to CNN. To date, there has been limited understanding of consumer preferences for specific sugar ingredients – i.e., natural vs. added – and lower sugar propositions. There is incontrovertible evidence that a wage gap exists today and has existed for many years, which I support throughout my capstone analysis. Targeting the right customers is not an easy job. This will explore a bag-of-words model with user browsing data on the website of a local TV news station in Las Vegas owned by EW Scripps. This capstone project contains two major applications of what I learnt during my master’s in business Analytics– Data Visualization (the tool used here is Power BI) and Data Mining using Clustering Analysis (using K-Means). Mahitha Sree Tammineedi, Analysis and Design of Balance Transfer Campaigns, July 2019, (Charles R. Sox, Jacob George) Zarak Shah, Bank Loan Predictions, July 2019, (Yichen Qin, Edward Winkofsky) We present the application of data-mining approaches to house-price analysis in the real-estate industry. The scope of the project was to identify the characteristics of loan defaulters for credit card and house purchase. The turnaround time for comparing access between two users is now reduced from approximately an hour to few seconds. Based on this analysis, I recommend different market segments to target and avoid within the state of Texas. Since Retail Installment loan products are the major revenue generating source, the team wants to particularly focus on customers who have opted for retail installment loan once during their tenure at Axcess. In order to perform reporting and analytics on this data, the data must be extracted from the source system, modeled and loaded into a data warehouse. I tested Tomahawk simulation with R using MLB game data from 2010 to 2014. In its current form, the tournament consists of 64 teams from conferences across the nation divided into 4 sections. This project applies various machine learning algorithms to predict sentiment of reviewer from his textual review on Amazon food products. The nature of the sport makes it very suitable for statistical analysis. In the second part, the model goes through the validation process and the accuracy of the model is confirmed. The results from this project could serve as a guideline for retailers in assortment planning strategy. We analyzed data using statistics-based approaches to get insights on different project types (projects containing At your service tasks (AYS) and projects containing non AYS Tasks). 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.

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