iris-versicolor

item.xml

<?xml version="1.0" encoding="UTF-8"?> <items> <item> <itemname>Pencil</itemname> <itemrate>10</itemrate> <quantity>3</quantity> </item> <item> <itemname>Pen</itemname> <itemrate>20</itemrate> <quantity>3</quantity> </item> <item> <itemname>Eraser</itemname> <itemrate>5</itemrate> <quantity>5</quantity> </item> <item> <itemname>Sharpner</itemname> <itemrate>8</itemrate> <quantity>5</quantity> </item> <item> <itemname>Compass</itemname> <itemrate>20</itemrate> <quantity>5</quantity> </item> </items>

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Importpandasaspd
Fromsklearn.datasetsi
Fromsklearn.l
mportload_iri
s
inear_modelimportLogi
sticRegressi
Fromsklearn.model_sel
ectionimporttrai
Fromsklearn.metri
csimportaccuracy_score
#loadtheirisdataset
Iri
s=load_iri
s()
#createadataframefromthedataset
Df=pd.DataFrame(iri
s.data,col
on
n_test_spl
it
umns=iris.feature_names)
Df[‘
target’
]=iris.
target
#viewbasicstatisticaldetailsofthedifferentspecies
Print(“StatisticaldetailsofIris-setosa:”)
Print(df[df[‘target’]==0].describe())
Print(“StatisticaldetailsofIris-versicolor:”)
Print(df[df[‘target’]==1].describe())
Print(“StatisticaldetailsofIris-virginica:”)
Print(df[df[‘target’]==2].describe())
#splitthedataintotrainingandtestingsets
X=df.iloc[:,:-1]
Y=df.iloc[:,-1]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
#fitalogisticregressionmodel
Logreg=LogisticRegression()
Logreg.fit(X_train,y_train)
#makepredictionsonthetestset
Y_pred=logreg.predict(X_test)
#calculatetheaccuracyofthemodel
Accuracy=accuracy_score(y_test,y_pred)
Print(“Accuracyofthelogisticregressionmodel:”,accuracy