I'm currently working on a part-time project which involves predicting the likelihood of customers going to buy a product using data analytics. The company I'm interning with has given me a customer CSV file with all current customers and their attributes and needs to make a prediction model to classify whether prospects are feasible to pursue or not.
However since they have given me a list of all their successful customers or leads, in marketing terms, is it possible to train a model like K-means with PCA (and k-fold cross validation?) and get results? I have to train my model to fit a value, say 10, which I will add to the CSV, and further test it.
I am using pandas. Another issue is that there is a lot of demographical data, but I managed to overcome it using
get_dummies(). The number of columns escalated from about 10 to 47, though.
I'm just entering into the world of data analysis, hence I'm a bit clueless as to what path to take or whether what I'm doing is right.
The exact analysis is called Predictive Lead Scoring/Analysis, in marketing terminology.
I followed what @HonzaB did and, hence did get a decision tree. However, since I had 40 columns, it looks like this
I had to take a screenshot of it, as it was over 2 MB.
Obviously it's really big, and I have to prune the tree somehow, but I not sure how to do so on pandas. Also, is there any way that I can just generate the best characteristics as a text file or something that can be understood without the help of a data scientist?
I've read up on a question that is quite similar to what I need to do. Predictive modeling based on RFM scoring indicators. In it there is a link to a paper([Data Mining using RFM Analysis]) that talks about rule-based classification. Ideally this is what I need to do, and what is most suitable to the company's need.
I want to know if it's possible to do this on Python/pandas. Or is it possible to traverse the decision tree and generate the rules?
I found another website Decision trees in python again, cross-validation that uses cross validation and hyperparameter optimisation to get a better solution. Also they have included Python code to get readable code. It's a feasible solution, however it's quite complicated and I can't understand how it works. Will it work?
PS I solved the "really big decision-tree" problem from Edit 1, by reducing max-depth. I didn't know at all.