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Loading Data in Scikit-Learn

Before you can train a model, you need to get your data into a format that Scikit-Learn understands. Scikit-Learn works primarily with NumPy arrays or Pandas DataFrames, but it also provides built-in tools to help you get started quickly.

1. The Scikit-Learn Data Format​

Regardless of how you load your data, Scikit-Learn expects two main components:

  1. The Feature Matrix (XX): A 2D array of shape (n_samples, n_features).
  2. The Target Vector (yy): A 1D array of shape (n_samples) containing the labels or values you want to predict.

2. Built-in "Toy" Datasets​

Scikit-Learn comes bundled with small datasets that require no internet connection. These are perfect for testing your code or learning new algorithms.

  • load_iris(): Classic classification dataset (flowers).
  • load_diabetes(): Regression dataset.
  • load_digits(): Classification dataset (handwritten digits).
from sklearn.datasets import load_iris

# Load the dataset
iris = load_iris()

# Access data and labels
X = iris.data
y = iris.target

print(f"Features: {iris.feature_names}")
print(f"Target Names: {iris.target_names}")

3. Fetching Large Real-World Datasets​

For larger datasets, Scikit-Learn provides "fetchers" that download data from the internet and cache it locally in your ~/scikit_learn_data folder.

  • fetch_california_housing(): Predict median house values.
  • fetch_20newsgroups(): Text dataset for NLP.
  • fetch_lfw_people(): Labeled Faces in the Wild (for face recognition).
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(f"Dataset shape: {housing.data.shape}")

4. Loading from External Sources​

In a professional environment, you will rarely use the built-in datasets. You will likely load data from CSVs, SQL Databases, or Pandas DataFrames.

From Pandas to Scikit-Learn​

Scikit-Learn is designed to be "Pandas-friendly." You can pass DataFrames directly into models.

import pandas as pd
from sklearn.linear_model import LinearRegression

# Load your own CSV
df = pd.read_csv('my_data.csv')

# Split into X and y
X = df[['feature1', 'feature2']] # Select specific columns
y = df['target_column']

# Train model directly
model = LinearRegression().fit(X, y)

5. Generating Synthetic Data​

Sometimes you need to create "fake" data to test how an algorithm handles specific scenarios (like high noise or non-linear patterns).

from sklearn.datasets import make_blobs, make_moons

# Create 3 distinct clusters for a classification task
X, y = make_blobs(n_samples=100, centers=3, n_features=2, random_state=42)

6. Understanding the "Bunch" Object​

When you use load_* or fetch_*, Scikit-Learn returns a Bunch object. This is essentially a dictionary that contains:

  • .data: The feature matrix.
  • .target: The labels.
  • .feature_names: The names of the columns.
  • .DESCR: A full text description of where the data came from.
tip

Use as_frame=True in your loader to get the data returned as a Pandas DataFrame immediately: data = load_iris(as_frame=True).frame

References for More Details​

Now that you can load data, the next step is to ensure it's in the right shape and split correctly for training and testing.