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Exploratory Data Analysis using Rapid Miner

I need to predict the likelihood of a customer becoming a loan delinquency

and forfeiting on a loan. I’m struggling to fulfil the job requirements with RapidMiner and need someone’s expertise to clean and model the data as required.

The following tasks are required:

1) An exploratory data analysis of the training data set using

RapidMiner Studio data mining tool.

2) A Decision Tree model for predicting loan delinquency based on the data set

using RapidMiner and an appropriate set of data mining operators and a

reduced set of variables determined by the exploratory data analysis.

3) Build a Logistic Regression model for predicting loan delinquency based on the

data set using RapidMiner and an appropriate set of data mining operators

and a reduced set of variables determined by exploratory data analysis in Task 1.

4) Conduct a comparative performance evaluation of the Final Decision Tree

Model with a Final Logistic Regression Model for predicting loan delinquency.

*Note* you will need to use the Cross Validation Operator; Apply Model Operator and

Performance (Binominal Classification) Operator in your final data mining process

models (Decision Tree, Logistic Regression) to generate the required model performance

metrics (Accuracy, Miscalculation Rate, True Positive Rate, False Positive Rate, Area

under Roc Chart (AUC), Precision, Recall, Lift, Sensitivity, F Measure) required for Task

4.

I require the RapidMiner file with explanatory notes.

Taidot: Data Cleansing, tiedonlouhinta, Tilastoanalyysi, Tilastotiede

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Tietoa työnantajasta:
( 0 arvostelua ) Maroochydore, Australia

Projektin tunnus: #17911746

Myönnetty käyttäjälle:

raghavajay3

let's discuss over chat

$261 AUD 5 päivässä
(23 Arvostelua)
4.3