p$,$x$,$s$,$n$,$t$,$p$,$f$,$c$,$n$,$k$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$s$,$u

e$,$x$,$s$,$y$,$t$,$a$,$f$,$c$,$b$,$k$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$n$,$g

e$,$b$,$s$,$w$,$t$,$l$,$f$,$c$,$b$,$n$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$n$,$m

p$,$x$,$y$,$w$,$t$,$p$,$f$,$c$,$n$,$n$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$s$,$u

e$,$x$,$s$,$g$,$f$,$n$,$f$,$w$,$b$,$k$,$t$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$e$,$n$,$a$,$g

e$,$x$,$y$,$y$,$t$,$a$,$f$,$c$,$b$,$n$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$n$,$g

e$,$b$,$s$,$w$,$t$,$a$,$f$,$c$,$b$,$g$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$n$,$m

e$,$b$,$y$,$w$,$t$,$l$,$f$,$c$,$b$,$n$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$s$,$m

p$,$x$,$y$,$w$,$t$,$p$,$f$,$c$,$n$,$p$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$v$,$g

e$,$b$,$s$,$y$,$t$,$a$,$f$,$c$,$b$,$g$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$s$,$m

e$,$x$,$y$,$y$,$t$,$l$,$f$,$c$,$b$,$g$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$n$,$g

e$,$x$,$y$,$y$,$t$,$a$,$f$,$c$,$b$,$n$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$s$,$m

e$,$b$,$s$,$y$,$t$,$a$,$f$,$c$,$b$,$w$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$s$,$g

p$,$x$,$y$,$w$,$t$,$p$,$f$,$c$,$n$,$k$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$v$,$u

e$,$x$,$f$,$n$,$f$,$n$,$f$,$w$,$b$,$n$,$t$,$e$,$s$,$f$,$w$,$w$,$p$,$w$,$o$,$e$,$k$,$a$,$g

e$,$s$,$f$,$g$,$f$,$n$,$f$,$c$,$n$,$k$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$y$,$u

e$,$f$,$f$,$w$,$f$,$n$,$f$,$w$,$b$,$k$,$t$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$e$,$n$,$a$,$g

p$,$x$,$s$,$n$,$t$,$p$,$f$,$c$,$n$,$n$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$k$,$s$,$g

p$,$x$,$y$,$w$,$t$,$p$,$f$,$c$,$n$,$n$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$s$,$u

p$,$x$,$s$,$n$,$t$,$p$,$f$,$c$,$n$,$k$,$e$,$e$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$s$,$u

e$,$b$,$s$,$y$,$t$,$a$,$f$,$c$,$b$,$k$,$e$,$c$,$s$,$s$,$w$,$w$,$p$,$w$,$o$,$p$,$n$,$s$,$m

p$,$x$,$white

$100\%$ accuracy

Rule P$\_4)$ may also be

P$\_4\text{'})$ population$=$clustered.AND.cap$\_$color$=$white

These rule involve $6$ attributes $($out of $22).$ Rules for edible

mushrooms are obtained as negation of the rules given above, for

example the rule:

odor$=($almond$.$OR$.$anise.OR$.$none$).$AND.spore$-$print$-$color$=$NOT.green

gives $48$ errors, or $99\mathrm{.}41\%$ accuracy on the whole dataset.

Several slightly more complex variations on these rules exist,

involving other attributes, such as gill$\_$size, gill$\_$spacing,

stalk$\_$surface$\_$above$\_$ring, but the rules given above are the simplest

we have found.

$4.$ Relevant Information:

This data set includes descriptions of hypothetical samples

corresponding to $23$ species of gilled mushrooms in the Agaricus and

Lepiota Family $($pp$.500-525).$ 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$\text{'}\text{'}$ for Poisonous Oak and Ivy.

$5.$ Number of Instances: $8124$

$6.$ Number of Attributes: $22($all nominally valued$)$

$7.$ Attribute Information: $($classes: edible$=$e$,$ poisonous$=$p$)$

$1.$ cap$-$shape: bell$=$b$,$conical$=$c$,$convex$=$x$,$flat$=$f$,$

knobbed$=$k$,$sunken$=$s

$2.$ cap$-$surface: fibrous$=$f$,$grooves$=$g$,$scaly$=$y$,$smooth$=$s

$3.$ cap$-$color: brown$=$n$,$buff$=$b$,$cinnamon$=$c$,$gray$=$g$,$green$=$r$,$

pink$=$p$,$purple$=$u$,$red$=$e$,$white$=$w$,$yellow$=$y

$4.$ bruises?: bruises$=$t$,$no$=$f

$5.$ odor: almond$=$a$,$anise$=$l$,$creosote$=$c$,$fishy$=$y$,$foul$=$f$,$

musty$=$m$,$none$=$n$,$pungent$=$p$,$spicy$=$s

$6.$ gill$-$attachment: attached$=$a$,$descending$=$d$,$free$=$f$,$notched$=$n

$7.$ gill$-$spacing: close$=$c$,$crowded$=$w$,$distant$=$d

$8.$ gill$-$size: broad$=$b$,$n I have such data. Classify this data according to RANDOM FORE CLASSIFICATION and write its python code.

Create certain tables, these tables are: Percentiles table, Variance table, Correlation chart, Correlation Matrix table and lastly Statistics Correlation etc. causality

and analyze it by making explanations accordingly. ABOUT THE DATA SCIENCE