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FOLDER 1 : agaricus - lepiota.data p , x , s , n , t , p , f , c , n , k
FOLDER : agaricuslepiota.data pxsntpfcnkeesswwpwopksu
exsytafcbkecsswwpwopnng
There are nearly thousand lines as above FOLDER : agaricus lepiota.names Logical rules for the mushroom data sets.
Logical rules given below seem to be the simplest possible for the
mushroom dataset and therefore should be treated as benchmark results.
Disjunctive rules for poisonous mushrooms, from most general
to most specific:
P odorNOTalmondORanise.ORnone
poisonous cases missed, accuracy
P sporeprintcolorgreen
cases missed, accuracy
P odornone.AND.stalksurfacebelowringscaly.AND.
stalkcoloraboveringNOT.brown
cases missed, accuracy
P habitatleaves.AND.capcolorwhite
accuracy
Rule P may also be
P populationclustered.AND.capcolorwhite
These rule involve attributes out of Rules for edible
mushrooms are obtained as negation of the rules given above, for
example the rule:
odoralmondORanise.ORnoneAND.sporeprintcolorNOT.green
gives errors, or accuracy on the whole dataset.
Several slightly more complex variations on these rules exist,
involving other attributes, such as gillsize, gillspacing,
stalksurfaceabovering, but the rules given above are the simplest
we have found.
Relevant Information:
This data set includes descriptions of hypothetical samples
corresponding to species of gilled mushrooms in the Agaricus and
Lepiota Family pp Each species is identified as
definitely edible, definitely poisonous, or of unknown edibility and
not recommended. This latter class was combined with the poisonous
one. The Guide clearly states that there is no simple rule for
determining the edibility of a mushroom; no rule like leaflets
three, let it be for Poisonous Oak and Ivy.
Number of Instances:
Number of Attributes: all nominally valued
Attribute Information: classes: ediblee poisonousp
capshape: bellbconicalcconvexxflatf
knobbedksunkens
capsurface: fibrousfgroovesgscalyysmooths
capcolor: brownnbuffbcinnamoncgrayggreenr
pinkppurpleuredewhitewyellowy
bruises?: bruisestnof
odor: almondaaniselcreosotecfishyyfoulf
mustymnonenpungentpspicys
gillattachment: attachedadescendingdfreefnotchedn
gillspacing: closeccrowdedwdistantd
gillsize: broadbnarrown
gillcolor: blackkbrownnbuffbchocolatehgrayg
greenrorangeopinkppurpleurede
whitewyellowy
stalkshape: enlargingetaperingt
stalkroot: bulbousbclubccupuequale rhizomorphszrootedrmissing
stalksurfaceabovering: fibrousfscalyysilkyksmooths
stalksurfacebelowring: fibrousfscalyysilkyksmooths
stalkcolorabovering: brownnbuffbcinnamoncgraygorangeo pinkpredewhitewyellowy
stalkcolorbelowring: brownnbuffbcinnamoncgraygorangeo pinkpredewhitewyellowy
veiltype: partialpuniversalu
veilcolor: brownnorangeowhitewyellowy
ringnumber: nonenoneotwot
ringtype: cobwebbycevanescenteflaringflargelnonenpendantpsheathingszonez
sporeprintcolor: blackkbrownnbuffbchocolatehgreen r orangeopurpleuwhitewyellowy
population: abundantaclusteredcnumerousn scatteredsseveralvsolitaryy
habitat: grassesgleaveslmeadowsmpathspurbanuwastewwoodsd
Missing Attribute Values: of them denoted by all for
attribute #
Class Distribution: edible: poisonous: total: instances I have the files I posted above, with the same name, attached as text in PayCharm.First of all, this data science project assignment and its subject is random fore classificatication write python code here according to the instructions given. TABLE OF PERCENTAGES,VARIANCE TABLE,CORRELATION CHART,CORRELATION MATRIX,TABLE STATISTICAL,CORRELATION AND CAUSALITY ANALYSIS. We are asked to extract this information from the code and analyze it Please it one by on and complete it
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