Segment the accounts on their transactional behavior and find the accounts which are more likely to subscribe for loans.
2-91) Transaction amount aggregated on days for last 90 days
92) is_subscriber? (0/1)
1) Normalized the data (x-min(x)) / max(x) - min(x)
2) Clustering on column 2-91
3) Analyze the clusters to check which cluster has the maximum percentage of subscribers (is_subscriber? == 1)
4) Sort the clusters on the basis of maximum percentage of subscribers
5) The remaining non-subscribers (is_subscriber? == 0) of cluster with highest percentage are most likely to get subscribed.
But the results I am getting doesn't look promising.
The data is already prepared. No other variable can be added.
1) which data normalization to use?
2) Which distance to use in clustering?
3) Which clustering algorithm to use?
4) Considering the data if there is any other approach you can suggest?
Data can be found here: https://drive.google.com/open?id=1ydL1AQmozBlfiqz0nsM3f_E2JK2KB8tD