Okay, so in this machine learning and all these statistical learning, we are actually in the modeling and data evaluation stage here. So, let's say after we train our model here, and we evaluate our model and get the accuracy. So let's say we are not happy or not satisfied with all this accuracy, we can go back to business understanding stage, then go back to data understanding, then go to data preparation. Then we go into the modeling again. So in this modeling stage, we can also go back to the data preparation stage to let's say, we do more the data processing. And in this modeling stage itself if we want, if we want to, let's say, improve on the accuracy or precision or improve the model, we can either change the settings in the model or change setting these are machine learning or statistical learning algorithm.

Or we can also go into this data processing stage to process the data even more. So, let's say in our apple gramming. So let's say this is our, our model, then we can add some settings our trace line here. So, we can change some of these settings to actually try to improve on the accuracy of the model. We can also do more data cleaning or do more processing of the data. Okay, we can do more on data data cleaning here.

So here I put any means I remove any observation or those missing values. So we can do more data processing here. So so these are Some ways that we can improve on the model accuracy or precision