I'm not really sure how to word this and I'm sorry if the formatting is wrong, but I'm trying to get a foundation to be able to tackle this problem myself.

I am trying to develop a prediction algorithm for a set of data of "Hip Surgery Patients" that looks like:

```
Readmission Time | Symptom Code | Symptom Note    |  Related
               6 |         2334 | swelling in hip |     Yes       
              12 |         1324 | anxiety         |     Maybe       
               8 |         2334 | swelling in hip |     Yes       
              30 |         1111 | Headaches       |     No
               3 |         7934 | easily bruising |     Yes  

```

For context, doctors can identify whether or not a given "Symptom Code" is related to the "Hip Replacement Surgery" that occurred X days ago. I have about 200 entries in my data set that match this format, and my goal is to be able to match results in the given set as well as predict new results in the "Related" Column (with certainty statistics on predicted results) based on new inputs. For example given:

```
Input:  20 | 2334 | swelling in hip
Output: Yes (90% confidence)

```

I'm very new to Data Analytics and Machine Learning so I would really just like to get some kind of pointers of things to look up or where to get started on my research. I imagine there's an optimal function/model that would handle this best but as I said I'm very new to the topic so I have no clue as to where to start. Since I have a relatively small data set I'm looking for a technique that isn't easily over trained if possible

I really appreciate any help and pointers on where to get started.