Start to learn Machine Learning with the Iris flower classification using Python with model of
- Support Vector Machine Algorithm
- Logistic Regression
- Decision Tree
- K-Nearest Neighbors
Machine learning is the scientific study of algorithms and statistical models by extracting knowledge from data. It is a subset of studying in artificial intelligence with computer science for prediction and decisions.
The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher. It contains a set of 150 records under five attributes: petal length, petal width, sepal length, sepal width and species. The goal is to create a machine learning model with thid dataset to learn from the measurements of these irises, so that we can predict the species for the new irises that has been found.
Project is created with Python version: 3.6.0 🐍
To run this project, install library:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import ...
The belowing example code shows Support Vector Machine algorithm to study basically machine learning of iris dataset. SVM is a supervised machine learning algorithm which can be used for both classification or regression challenges.
"Support Vector Machine Algorithm"
model = svm.SVC()
model.fit(train_x, train_y)
prediction = model.predict(test_x)
print("The accuracy of SVM: ", metrics.accuracy_score(prediction, test_y))