I collected 8800 samples total, but after data cleaning and outlier detection I was left with 3507 samples.
Is this enough to put through machine learning models? (lasso, linear regression, decision tree)
Should I scrape more?
I expect more data is needed, but I want to check with others before wasting time.
Also, how much data should I use for training and testing?
When it comes to Machine Learning, more data is always better
In general, as your model gets more complex, you'll need more data to prevent overfitting.
For example, a single-variable linear regression requires less data to train than a convolutional neural network. This is because the neural network has more weights than the single-variable model.
Unfortunately, a simple model has less predictive power than a complex one. In our example, this means the linear regression will yield a prediction farther from the actual value than a neural network when trying to model a variable that depends on more than the single input.
As for train/test split, I recommend randomly ordering all the data, and then using 80% for training and 20% for testing. Repeating this process multiple times to check if your model is a good fit regardless of training data selected is called K-Fold Cross Validation
