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Name: Thomas Webb
Type: User
Name: Thomas Webb
Type: User
Objective: Build a neural network-based classifier that will predict the likelihood of 6-month customer retention based on history of customer data.
Objective: In order to select the best-performing model, build and compare a variety of regression models to predict the compressive strength of concrete based on a raw data set regarding materials commonly used in the construction industry.
Objective: Identify trends of customer behavior and provide business insights supported by trends in the customer data.
This project is an Exploratory Data Analysis of the GroupLens Research project's collection of data related to movies and movie reviews. The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. This data is widely used for collaborative filtering and other filtering solutions.
This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.
Objective: Build a CNN classifier that will be able to accurately predict the species of plant seedling based on an image of that seedling taken from the top.
Objective: Using a bankβs customer data, build and train several machine learning models to predict the probability of a customer to subscribe to a term deposit. Use K-fold cross validation to assess the performance of each model.
Objective: Train a model that will analyze text from a collection of tweets about US Airlines from February 2015, and by identifying negative sentiments, draw business insights regarding the reasons for customer dissatisfaction surrounding each airline.
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