Predictive modelling of the energy efficiency dataset provided, using the following specific methods explained below.
8 main tasks: (with specific details in Jupiter notebook provided):
1. Perform explanatory data analysis for the 8 variables using the training dataset and with the help of appropriate graphs.
2. Apply the standard Bayesian linear regression model, using Type-II maximum likelihood to estimate “most probable” values for hyperparameter
16 freelancere byder i gennemsnit $212 på dette job
Hello, I am excellent machine learning developer and I am interested in this task. I can build and showcase a sample to help you make a decision. Regards Aruna
Hi, I have worked on ML projects like anomaly detection in smart home energy usage. Ised Jupyter for the development. I would like to work on your project. Let me know if you want to discuss further. Regards, Monir
Hey, I have lot of development experience with python development,I recently worked on Bayesian network using Gibbs theorem,can redo with maximum likelihood also
hello I have an experience with Bayesian estimation of the data set. I'm an engineer and I'm good at Matlab. Please send me the details and let's discuss more. Thanks.
I have hands on experince in eda and machine learning. Bayesian is fine, although I can keep it as a benchmark and check other algorithms for improvement.