Build SVM | support vector machine classifier
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)
# Let's see target names
targets = iris.target_names
print(targets)
# 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]))
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.