How to improve results from ML model? (spam classification)
In your results you can observe the usual problem with imbalanced data: the classifier favors the majority class 0 (I assume this is class &quot;ham&quot;). In other words it tends to assign &quot;ham&quot; to instances which are actually &quot;spam&quot; (false negative errors). You can think of it like this: with the &quot;easy&quot; instances, the classifier gives the correct class, but for the instances which are difficult (the classifier &quot;doesn't know&quot;) it chooses the majority class because it's the most likely.
There are many things you could do:

Undersampling the majority class or oversampling the minority class is the easy way to deal with class imbalance.
Better feature engineering is more work but it's often how to get the best improvement. For example I guess that you use all the words in the emails as features right? So you probably have too many features and that probably causes overfitting, try reducing dimensionality by removing rare words.
Try different models, for instance Naive Bayes or Decision Trees. Btw Decision Trees are a good way to investigate what happens inside the model.