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Before you can build machine learning models, you need to load your data into memory.
In this post you will discover how to load data for machine learning in Python using scikit-learn.
Discover how to prepare data with pandas, fit and evaluate models with scikit-learn, and more in my new book, with 16 step-by-step tutorials, 3 projects, and full python code.
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- Update March/2018: Added alternate link to download the dataset as the original appears to have been taken down.
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The scikit-learn library is packaged with datasets. These datasets are useful for getting a handle on a given machine learning algorithm or library feature before using it in your own work.
This recipe demonstrates how to load the famous Iris flowers dataset.
# Load the packaged iris flowers dataset
# Iris flower dataset (4x150, reals, multi-label classification)
iris = load_iris()
Load from CSV
It is very common for you to have a dataset as a CSV file on your local workstation or on a remote server.
From the prepared X and y variables, you can train a machine learning model.
# Load the Pima Indians diabetes dataset from CSV URL
import numpy as np
# URL for the Pima Indians Diabetes dataset (UCI Machine Learning Repository)
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
# download the file
raw_data = urllib.urlopen(url)
# load the CSV file as a numpy matrix
dataset = np.loadtxt(raw_data, delimiter=",")
# separate the data from the target attributes
X = dataset[:,0:7]
y = dataset[:,8]
In this post you discovered that the scikit-learn method comes with packaged data sets including the iris flowers dataset. These datasets can be loaded easily and used for explore and experiment with different machine learning models.
You also saw how you can load CSV data with scikit-learn. You learned a way of opening CSV files from the web using the urllib library and how you can read that data as a NumPy matrix for use in scikit-learn.