Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data
- برمجة بايثون
- ذكاء صنعي
- 2021-09-23
- mhanasmh00489829403
الأجوبة
# Import necessary modules
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
iris = pd.read_csv("iris.csv")
#Drop id column
iris = iris.drop('Id',axis=1)
X = iris.iloc[:, :-1].values
y = iris.iloc[:, 4].values
#Split arrays or matrices into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
knn = KNeighborsClassifier(n_neighbors=7)
knn.fit(X_train, y_train)
a_index=list(range(1,11))
a=pd.Series()
# Calculate the accuracy of the model for different values of k
for i in np.arange(1, 10):
knn2 = KNeighborsClassifier(n_neighbors=i)
knn2.fit(X_train, y_train)
print("For k = %d accuracy is"%i,knn2.score(X_test,y_test))
# Visual presentation: Various values of n for K-Nearest nerighbours
print("\nVisual presentation: Various values of n for K-Nearest nerighbours:")
for i in list(range(1,11)):
model=KNeighborsClassifier(n_neighbors=i)
model.fit(X_train,y_train)
prediction=model.predict(X_test)
a=a.append(pd.Series(metrics.accuracy_score(prediction,y_test)))
plt.plot(a_index, a)Sample Output:
For k = 1 accuracy is 0.9666666666666667 For k = 2 accuracy is 0.9666666666666667 For k = 3 accuracy is 1.0 For k = 4 accuracy is 1.0 For k = 5 accuracy is 1.0 For k = 6 accuracy is 0.9666666666666667 For k = 7 accuracy is 0.9666666666666667 For k = 8 accuracy is 0.9333333333333333 For k = 9 accuracy is 0.9666666666666667 Visual presentation: Various values of n for K-Nearest nerighbours:
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