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Thomas Webb's Projects

bank-churn-prediction icon bank-churn-prediction

Objective: Build a neural network-based classifier that will predict the likelihood of 6-month customer retention based on history of customer data.

concrete-compressive-strength-prediction icon concrete-compressive-strength-prediction

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.

grouplens-movie-research-eda icon grouplens-movie-research-eda

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.

personal-loan-conversion-analysis icon personal-loan-conversion-analysis

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.

plant-seedling-image-classification icon plant-seedling-image-classification

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.

term-deposit-predicitve-analysis icon term-deposit-predicitve-analysis

Objective: Using a bank’s 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.

us-airline-twitter-sentiment-analysis icon us-airline-twitter-sentiment-analysis

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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