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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).

Ferdigheter: Data Mining, Python, R programmeringspråk, Statistisk analyse, Statistikk

Se mer: online exam project using java, mini project using java script, configure java project using serverxml tomcat, k fold cross validation r caret, cross validation in r, k fold cross validation r example, decision tree classification in r, cross validation in r linear regression, k fold cross validation decision tree in r, r caret decision tree example, decision tree in r studio, data processing, python, accounting, mathematics, financial research, statistics, business analysis, analytics, financial analysis

Om arbeidsgiveren:
( 4 omtaler ) Warszawa, Poland

Prosjekt-ID: #17190195

38 frilansere byr i snitt $167 for denne jobben

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blakehamm

With 2+ years in R programming and piloting of successful R projects, I am an ideal candidate. I have experience with a variety of packages and data formats including Datamining, Classification and Validation. Furtherm Mer

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(21 Omtaler)
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schoudhary1553

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inspiredwhiz

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braincenter

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