SVM (Support Vector Machine) algorithm finds the hyperplane which is at max distance from nearest points.

# Imports
from sklearn.datasets import load_iris
from sklearn.svm import SVC
import pandas as pd
import numpy as np

# Load Data
iris = load_iris()

# Create a dataframe
df = pd.DataFrame(iris.data, columns = iris.feature_names)
df['target'] = iris.target

# Let's see a sample of created df
df.sample(frac=0.01)
sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
1156.43.25.32.32
114.83.41.60.20
# Let's see target names
targets = iris.target_names
print(targets)
['setosa' 'versicolor' 'virginica']
# Prepare training data for building the model
X_train = df.drop(['target'], axis=1)
y_train = df['target']

# Instantiate the model
cls = SVC()

# Train/Fit the model 
cls.fit(X_train, y_train)

# Make prediction using the model
X_pred = [5.1, 3.2, 1.5, 0.5]
y_pred = cls.predict([X_pred])

print("Prediction is: {}".format(targets[y_pred]))
Prediction is: ['setosa']

That's how we Build SVM | support vector machine classifier

That’s all for this mini tutorial. To sum it up, we learned how to Build SVM | support vector machine classifier

Hope it was easy, cool and simple to follow. Now it’s on you.

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