Hi sthembiso. Apologies for the delay in reply.
This question requires a detailed answer, which is for you to figure out, so that you can understand the basic principles behind these algorithms. But I can provide a way for you to follow, to answer this question.
Read the article, as it clearly describes how a decision tree works. The factors mentioned in your problem act as features, which you will use to predict the potential deals. You could also get more features using internal or external resources, as mentioned in your problem. Then you will train your model on already available data of potential deals and no deals. Also, there is a way mentioned in the article, using which you can get the individual importance of each feature you use. Finally, the model is ready for generating predictions.
As far as other data science algorithms are concerned, you’ll need to read about them for their pros and cons. But, in general, the basic procedure involves using important features, training the model and the using it to predict deals. Which model will work best for you, is something you’ll figure out as you get more experienced.
