  Knowledge of thermal properties is essential to design and evaluate thermal
systems and processes using nanofluids. This paper presents different
analytical models to predict thermal conductivity and viscosity. The efforts
have been made to develop machine learning models to predict these properties.
An extensive literature survey was carried out to collect thermal properties
data of different nanofluids to train and test these machine learning models.
The most influential properties like thermal conductivity, diameter, volume
concentration of nanoparticles, base fluid thermal conductivity and nanofluid
temperature are used as input variables to the thermal conductivity models and
molecular weight, diameter and volume fraction nanoparticles, base fluid
viscosity and nanofluid temperature are taken as an input variable to the
viscosity model. Data is divided into two-part, one part is used to train the
models and remaining part is used to test it. Result shows linear regression
and ANN model do predict thermal conductivity more closely and ANN predict
viscosity more accurately compared to analytical models.
