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R programming ( Datamining, Classification and validation )

I have a project using R programming or Python with report

a) the description of attributes (columns) e.g. the number of discrete and continuous attributes

(make factors from discrete ones, get rid of useless ones or remove one attribute from each pair

of strongly correlated attributes). Find or make your target class attribute.

b) the description of used classification methods and their validation process (changing their

parameters e.g. minsplit, cp, k-fold crossvalidation, boosting, bagging, adding cost matrices to

them, modifing input data e.g. standardization i.e. standard score, normalization, removing NA

and so on).

c) the validation process summary including ROC plots and their comparison.

d) You may use some clustering methods for not labeled datasets (without an obvious target

attribute). After this operation you can learn classifiers utilising input data and obtained cluster

labels.

e) Enclose please your R code with comments and links to data used in R code (if it was

changed before [login to view URL] then dropbox links to your new data).

Skills: Data Mining, Python, R Programming Language, Statistical Analysis, Statistics

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About the Employer:
( 4 reviews ) Warszawa, Poland

Project ID: #17190195

38 freelancers are bidding on average $167 for this job

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