How to work with input which is a combination of metadata+ vectorized text data + image pixel data to build a Regression Model (predict views)? There are 4 datasets (all in csv format), each has a uniqueID column by which each record can be identified. Image and text datasets are dense datasets.(need to be converted to ndarray).
Can someone suggest how to use all these 4 datasets for building a regression model?
This is how the datasets look,
Metadata having some input features and target variable(views)
uniqueID    ad_blocked embed  duration language hour views
     1        True     True    68        3      10   244
     2        False    True    90        1      15   63
     3        True     False   195       3      7    350

Vectorized title data - one entire row represents a title
uniqueID  title_1   title_2    title_3 
     1   -0.977637  -0.543310  0.079403
     2    0.041873   0.644655  -0.406487        
     3    0.503560  -0.085412  0.841144

Vectorized descriptions data - one entire row represents a description
 uniqueID  title_1   title_2    title_3 
     1   -0.052256  -0.016036  0.079403
     2    0.000106  0.356706  -0.025788
     3    0.015774 -0.085412   0.712229

Thumbnail pixel data - one entire row represents an image
uniqueID  image_1    image_2   image_3
     1   -0.484456  -0.543310  0.032915
     2    0.666147  0.644655  -0.005733
     3    0.035018  -0.011111  0.841144
If we avoid a Neural Network,

You should reduce the dimensionality of images e.g. PCA [ Assuming no translational variance among images]
Can try the same for the other two datasets with 50 features
Then try the ML models

If that doesn't work, we may try CNN for the image data.
In that scenario, we will need a mixed architecture of simple Neural Network and CNN.

Check this Paper
Blog on the paper