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# DataFlair Iris Classification # Import
Packages
import
numpy
as np import
matplotlib.pyplot
as
plt import
seaborn
as
sns import
pandas
as pd
columns = ['Sepal
length',
'Sepal width',
'Petal length',
'Petal width',
'Class_labels']
# As per the iris dataset information
# Load the data
df = pd.read_csv('iris.data',
names=columns)
df.head()
print('') print('1-
df.head, shows the first 5 rows from the data of
iris.data') print(df.head())
# Some basic statistical analysis about the
data
df.describe()
print('') print('2-
df.describe, Analyze and visualize the dataset:
iris.data') print(df.describe())
# Visualize the whole dataset - iris.data'
sns.pairplot(df, hue='Class_labels')
plt.figure(1)
plt.show()
# Seperate features and target
data = df.values
X = data[:,0:4]
Y = data[:,4]
print('') print('3-
df.values, Seperate features and target') print(df.values)
# Calculate avarage of each features for all
classes
Y_Data = np.array([np.average(X[:, i][Y==j].astype('float32'))
for i
in
range
(X.shape[1])
for j
in (np.unique(Y))])
Y_Data_reshaped = Y_Data.reshape(4, 3)
Y_Data_reshaped = np.swapaxes(Y_Data_reshaped,
0, 1)
X_axis = np.arange(len(columns)-1)
width = 0.25
# Plot the avarage
plt.figure(2)
plt.bar(X_axis, Y_Data_reshaped[0], width,
label =
'Setosa') plt.bar(X_axis+width, Y_Data_reshaped[1],
width, label =
'Versicolour') plt.bar(X_axis+width*2, Y_Data_reshaped[2],
width, label =
'Virginica')
plt.xticks(X_axis, columns[:4])
plt.xlabel("Features") plt.ylabel("Value
in cm.") plt.title('Plot
the avarage ')
plt.legend()
plt.show()
# Split the data to train and test dataset.
from
sklearn.model_selection
import
train_test_split
X_train, X_test, y_train, y_test =
train_test_split(X, Y, test_size=0.2)
# Support vector machine algorithm
from
sklearn.svm
import
SVC
svn = SVC()
svn.fit(X_train, y_train)
# Predict from the test dataset
predictions = svn.predict(X_test)
# Calculate the accuracy
from
sklearn.metrics
import
accuracy_score
accuracy_score(y_test, predictions)
print('') print('4-
Calculate the accuracy') print(accuracy_score(y_test,
predictions))
# A detailed classification report
from
sklearn.metrics
import
classification_report print('') print('5-
A detailed classification report')
#print(accuracy_score(y_test, predictions))
print(classification_report(y_test,
predictions))
X_new = np.array([[3, 2, 1, 0.2], [ 4.9, 2.2,
3.8, 1.1 ], [ 5.3, 2.5, 4.6, 1.9 ]])
#Prediction of the species from the input
vector
prediction = svn.predict(X_new)
print('') print('6-
Prediction of the species from the input vector') print("Prediction
of Species: {}".format(prediction))
# save the model using pickle format
import
pickle with
open('SVM1.pickle',
'wb')
as f:
pickle.dump(svn, f)
# Load the model
with
open('SVM1.pickle',
'rb')
as f:
model = pickle.load(f)
model.predict(X_new)
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