# What machine learning model would be the best fit for this problem [Sample Data-set and Plot Attached]?

So I've got a dataset that looks like:

Weight(KG)  Count
25          9
4           17
55          9
4           25
4           7
....
....


My aim is to find any relation between the weight and count and maybe predict count based on a given weight. I'm using Linear Regression (I don't know if this is the right way to go). Results were pretty disappointing:
Without label encoding (categorizing) and feature scaling (normalizing) the R squared metric was around 4%. With the encoding and scaling down it 'improved' to 6%.

My Regression plot looks like this ( X_train (weights) and Y_train (counts) ):

I'm completely lost as to what model would actually give me some insight into this problem. So my questions are:

1. What model would work better for the given data and plot?
2. Should I convert the problem into some kind of classification problem and try to predict whether for a given weight the count is above, say, 20 ?
3. Should I try adding in more features and try my luck? If so, would it still be a regression problem?

I'd appreciate any comments on all these questions or even on a single one.

I would try a Poisson Generalized Linear Model, also known as Poisson Regression. Most statistics packages should have it (for example in the Python package statsmodels you can find it for example under statsmodels.api.Poisson)