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Iris_data_set.py
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Iris_data_set.py
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#Building to Model the Iris dataset using Skitlearn
#importing libs
import numpy as np
import matplotlib.pyplot as mplt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import linear_model
#load iris data set - pre built
iris = datasets.load_iris()
print("Iris sample data set - 5 Rows printing")
print(iris.keys())
print(iris.feature_names[:])
print(iris.data[:5])
print(iris.target[:5])
X = iris.data[:]
y = iris.target[:]
#spliting the dataset into test and train
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#train using SGD Classifier
sgd_clf = linear_model.SGDClassifier()
sgd_clf.fit(X_train, y_train)
# train using KNNeighbour
from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_neighbors=8)
knn_clf.fit(X_train,y_train)
print('Score (SGD): %.2f',sgd_clf.score(X_test,y_test) * 100)
print('Score (SGD): %.2f',knn_clf.score(X_test,y_test) * 100)