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SCIKIT Learn Introduction with example

Machine learning is getting very high in popularity nowadays. It helps us in many different aspects. Because we hand over the task of doing deep research to a machine. And it help us in making a better decision. In the past, we heard the concept of a support system. Machine learning is perhaps one of those support systems. 

When we are talking about machine learning, we will always be guided to learn from one of the most popular languages and easy to learn. The name is Python. Python since decades has been a great language to jump into programming and coding. 

And when we talk about machine learning in Python, we need to familiarize ourselves with Scikit Learn. What is Scikit learn? You can find their website here. 

Scikit learn is perhaps now the most popular machine learning library in the Python ecosystem. It is pretty mature and got strong support from the community. The most common algorithm is there. They also have a great interface. They have so many classifiers and its easy to connect each of them. 

In our learning course in Youtube Notafra.id Learn with address https://notafra.id we will rely heavily on Scikit Learn.

I want to start with a simple classifier in Sklearn. 

#sample decision with tree classifier
from sklearn import datasets
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier

#load iris datasets
dataset = datasets.load_iris()

#fit a CART model to the data
model = DecisionTreeClassifier()
model.fit(dataset.data, dataset.target)
print(model)

#make predictions
expected = dataset.target
predicted = model.predict(dataset.data)

#summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))

Paste the code above to our Trinket python environment here

When you run, you will get the result!

So what does that mean from the result 

Scitkit Resul

The next learn can be found under the hashtag or keyword of Scikit

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