Building predictive model with low correlated data I have been working on a project with low features and only few entry fields ( 4 to be exact ). All the data in the dataset is barely correlated to each other. Is there some organized way or approaches to tackle such data and thus build a predictive model out of it. To give you a little glimpse of the dataset, here are a few columns from the 

+----+----------+-----------+---------+
| ID | OAG CODE | CONSIGNEE | SHIPPER |
+----+----------+-----------+---------+
| 11 | 665      | 1001      | 20100   |
| 11 | 665      | 1006      | 20105   |
| 13 | 667      | 1023      | 20110   |
| 13 | 669      | 1015      | 20104   |
| 13 | 669      | 1006      | 20105   |
+----+----------+-----------+---------+


I want to perform EDA over the data and also build a predictive model from the same. Please point out some of the standard techniques and methods for tackling such a problem.
really hard answer for u question cause there is to little information. Try to make EDA and attach it to question. Cause EDA define the model

Anywhere, for low corellated data, try to use k-NN. If u use Python: scikit-learn have implementation. Also u can try decision trees.

Sorry, but i'm also new in DS and can be wrong:)