# Building predictive model with low correlated data [closed]

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.

• The values you have provided all look like identity columns. Is there anything in the data that makes sense to you intuitively? What would you like to predict from this data? Nov 30 '18 at 17:32
• @Skiddles, I have to predict if given a OAG code, what all SHIPPER or CONSIGNEE will he be shipping his code to. Dec 1 '18 at 10:43

It seems like a challenging problem. If it were my task, I would start with a probabilistic approach like apriori, but you may want to check out Naive Bayes based approach. There are some differences in these approaches, but, either one may produce decent results. More generally, I think the analysis you want to perform is $$Association\ Rule\ Learning$$.