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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pyexpat import model
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import joblib
from Multiple_Regerision import X_train, X_test, y_train, y_test, y_predict
df=pd.read_csv(r"C:\Users\hp\PycharmProjects\PythonProject5\Iris.csv")
print(df)
#sns.countplot(data=df,x="SepalLengthCm",hue="Species")
#plt.show()
#sns.countplot(data=df,x="SepalWidthCm",hue="Species")
#plt.show()
#sns.countplot(data=df,x="PetalLengthCm",hue="Species")
#plt.show()
#sns.countplot(data=df,x="PetalWidthCm",hue="Species")
#plt.show()
X=df.drop(["Species"],axis=1)
y=df["Species"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
joblib.dump(model,"K Nearest Neighbour.py.pkl")
model = KNeighborsClassifier(n_neighbors=4)
model.fit(X_train,y_train)
y_predict= model.predict(X_test)
acc=accuracy_score(y_test,y_predict)
print(acc)
print(model.predict([[5.2,3.6,1.5,0.3]]))
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