• Career Accelerator Program
    Used data from a real career accelerator program (name not disclosed due to confidentiality) to determine whether a fellow will get a job and to predict the length of the job search using supervised machine learning. F1 score of 96.6 percent achieved.

  • Charity ML Donors Project
    Helped CharityML, a non-profit, classify whether individuals earn more than or less than 50K so that they can target their campaign strategies for donation using supervised machine learning. Accuracy of 81 percent achieved.

  • Image Classification Project
    Trained a deep neural network to do image classification on flowers using PyTorch and performed transfer learning using AlexNet. Built a Python module to automate training and prediction based on user input.

  • Identifying Customer Segments
    Used data from Bertelsmann Arvato Analytics to identify customer segments using k-means clustering and Principal Component Analysis techniques to direct marketing campaigns to increase the expected revenue of the firm.