We have 3 customer data sets. One has customer informations with around 25 attributes like customerID, Gender,Relationship,Job_Level (manager, non-manager), Income level (<25000 USD, between 25000 to 50000 USD, <70000 USD etc) The second dataset consiste of all those cutomers who bought the car. The attributes are cutomerID, Month& year of purchase,Model (like sports, eco etc), Feature 1 used (say cruise control) and so on. The thrid data set is new customer data set which has similar attributes as that in dataset1 (around 25 attributes).
Based on correlation between data set 1&2, we want to identify cutomers from data set 3 with high propensity to buy a car. In addition, the expected solution can also indicate the kind of products and features the potential leads will be interested in along with tentaive time i.e calender month.
In total we have 1000 sample values in data set 1. Out of which 891 are "No" and 109 are "yes" i.e bought the car.
We are exploring Weka for classification, and clustering. However we have basic doubts about selection of algorithem for each stage
preprocessing Stage - what filters to be used such that attributes which are not significant will be automatically removed. i.e Is there any algo. which will suggest/identify unimportant attributes ?
Which Classification algorithem we should we explore to predict pattern of cutomers who would buy the car?
We are not sure how to determine probable possible month in which new set of customer may be interetsed in buying.
Would really appreciate any inputs which will take us in right direction.
So far, We tried WEKA decision tree(J48) & Rules (PART, Jrip,Prism).However none of them are giving us rules or tree for "yes" path i.e rule which leads us to customer pattern who bought the car.
If needed, shall post the output of each of the algorithms we tried in WEKA.