Project Title: Development of Classification and Regression Tree (CART) Model
Primary Skill: Statistical Algorithm Development
Additional Skills: Statistical knowledge of predictive modeling algorithms for handling large data is essential.
We need professional to develop the following Predictive model for modeling large data coming from Banking, finance, Insurance, Telecommunication and such other fields.
Project Description: We need professional to develop the following Predictive model for modeling large data coming from Banking, finance, Insurance, Telecommunication and such other fields.
Classification and Regression Tree Model:
The set up is: One a response variable which can assume a set of categorical or continuous values (for ex., Customer Satisfaction (CSAT) response measured as Highly Satisfied, Satisfied, Less satisfied, Daily or weekly changes in Stock Market Prices etc.) and several predictors variables(may be continuous or categorical). Usually the data size is large and it will be stored in EXCEL or ASCII format. Assume that column represents a response variable while Rows indicate the cases.
1. Develop a Statistical algorithm to build the Classification and Regression Tree model to model the response variable. The procedure should handle large data. The algorithm should clearly and unambiguously mention all relevant computational aspects of the fitting procedure.
2. Develop a suitable JAVA code corresponding to the above procedure without using any of the third-party library functions.
3. Use suitable split-up criterion such as Gini index/ Misclassification error and tree pruning criterion.
4. Final model with model validation measures such as K-fold cross-validation.
5. Test case results to demonstrate how the program behaves at extreme values, Invalid inputs etc. Your program should work correctly for large data. Include at least one example to demonstrate working of the program.
Time Line: Time to develop a statistical algorithm and its Java implementation should not exceed Two months.
Budget: $4000-$ 6000