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machine-learning-project

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This is based on a data challenge from the Michigan Data Science Team (MDST). The Michigan Data Science Team (MDST) and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit to help solve one of the most pressing problems facing Detroit - blight. Blight violations are issued by…

  • Updated Jul 10, 2021
  • Jupyter Notebook

Internship project of Machine Learnig on Enegry Efficiency to estimate the heating load (HL) and cooling load (CL) using two different approaches: a traditional linear regression approach and a state-of-the-art nonlinear non-parametric method called random forests.

  • Updated Jun 16, 2023
  • Jupyter Notebook

This project aims to predict customer conversion for an insurance company. Using historical data, we will develop a machine learning model that can identify which customers are most likely to purchase a policy. By understanding the patterns and behaviors of previous customers, the model can make predictions and provide valuable insight to company.

  • Updated Mar 15, 2023
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data fetched by wafers (thin slices of semiconductors) is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not. Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors.

  • Updated Dec 29, 2022
  • Jupyter Notebook

data fetched by wafers (thin slices of semiconductors) is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not. Wafers are predominantly used to manufacture solar cells and are located at remote locations in bulk and they themselves consist of few hundreds of sensors.

  • Updated Feb 17, 2023
  • Jupyter Notebook

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