Hey everyone! Today, we're going to talk about how to get started with machine learning in scikit-learn. Scikit-learn is a free, open-source machine learning library for Python. It is one of the most popular machine learning libraries, and it is a great place to start if you are new to machine learning.
What is scikit-learn?
Scikit-learn provides a wide range of machine learning algorithms, including supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms. Scikit-learn also provides a number of tools for data preprocessing, model evaluation, and model selection.
How to get started with scikit-learn
To get started with scikit-learn, you will need to install the scikit-learn library. You can do this using the pip package manager:
pip install scikit-learn
Once you have installed scikit-learn, you can start using it to build machine learning models. To build a machine learning model, you will need to:
Load your data.
Preprocess your data.
Choose a machine learning algorithm.
Train the machine learning algorithm.
Evaluate the machine learning algorithm.
Make predictions with the machine learning algorithm.
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
# Load the sample dataset
data = pd.read_csv('sample_dataset.csv')
# Prepare the data
X = data[['feature1', 'feature2']]
y = data['label']
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
# Create the machine learning model
model = LogisticRegression()
# Train the machine learning model
model.fit(X_train, y_train)
# Evaluate the machine learning model
accuracy = model.score(X_test, y_test)
print('Accuracy:', accuracy)
# Make predictions with the machine learning model
new_data = np.array([[10, 20]])
prediction = model.predict(new_data)
print('Prediction:', prediction)
You should also check out my youtube channel on machine learning: