Let’s use the Built-In Scikit-Learn Datasets for your Machine Learning practice.
Preparation
We must install the Scikit-Learn package, as we will use it. We also need to install the Pandas and Matplotlib packages.
pip install -U pandas scikit-learn matplotlib
With the packages installed. Let’s get into the main part of the tutorial.
Leverage Scikit-Learn’s Built-In Datasets
Scikit-Learn provides many open-source datasets in their package that we can use for machine learning experiments. Most of the tasks we want to try out can be facilitated with these built-in datasets. Let’s explore the available datasets.
from sklearn import datasets print(datasets.__all__)
There are so many built-in datasets we can use. Let’s try out several examples for each task: Tabular Classification, Tabular Regression, and Image Classification.
For the Tabular Classification task, we can use the Iris Built-In dataset. Let’s try to examine the Iris dataset and use them in the model classification.
import pandas as pd from sklearn.datasets import load_iris iris = load_iris() df_iris = pd.DataFrame(data=iris.data, columns=iris.feature_names) df_iris['target'] = iris.target df_iris.info()
The output:
RangeIndex: 150 entries, 0 to 149 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sepal length (cm) 150 non-null float64 1 sepal width (cm) 150 non-null float64 2 petal length (cm) 150 non-null float64 3 petal width (cm) 150 non-null float64 4 target 150 non-null int32 dtypes: float64(4), int32(1) memory usage: 5.4 KB
As you can see, the dataset contains four different columns with one target column. Let’s use it for classification model training.
from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42) model = LogisticRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred))
The output:
precision recall f1-score support
0 1.00 1.00 1.00 10
1 1.00 1.00 1.00 9
2 1.00 1.00 1.00 11
accuracy 1.00 30
macro avg 1.00 1.00 1.00 30
weighted avg 1.00 1.00 1.00 30
The Iris dataset it’s a great built-in dataset for experiments in the classification task.
How about the Tabular Regression task? We can use the California Housing dataset. It’s a built-in dataset for regression tasks where we try to predict the housing price.
from sklearn.datasets import fetch_california_housing california = fetch_california_housing() df_california = pd.DataFrame(data=california.data, columns=california.feature_names) df_california['target'] = california.target df_california.info()
The output:
RangeIndex: 20640 entries, 0 to 20639 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 MedInc 20640 non-null float64 1 HouseAge 20640 non-null float64 2 AveRooms 20640 non-null float64 3 AveBedrms 20640 non-null float64 4 Population 20640 non-null float64 5 AveOccup 20640 non-null float64 6 Latitude 20640 non-null float64 7 Longitude 20640 non-null float64 8 target 20640 non-null float64 dtypes: float64(9) memory usage: 1.4 MB
There are seven features for the predictors and one target column to predict in this California Housing dataset.
We can use the dataset above for the Regression modeling task,
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
X_train, X_test, y_train, y_test = train_test_split(california.data, california.target, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R^2 Score:", r2_score(y_test, y_pred))
The output:
Mean Squared Error: 0.5558915986952426 R^2 Score: 0.5757877060324521
Lastly, we can use a built-in image dataset called the Digits dataset for image classification tasks. Let’s examine it.
from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
digits = load_digits()
fig, axes = plt.subplots(1, 5, figsize=(10, 3))
for ax, image, label in zip(axes, digits.images, digits.target):
ax.set_axis_off()
ax.imshow(image)
ax.set_title(f'Target: {label}')
plt.show()
The dataset contains collections of handwritten number images with their actual target. We can use them to experiment with an image classification task using the code below.
from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score, classification_report X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42) model = MLPClassifier(hidden_layer_sizes=(64,), max_iter=300, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_test) print(classification_report(y_test, y_pred))
The output:
precision recall f1-score support
0 0.97 1.00 0.99 33
1 1.00 1.00 1.00 28
2 0.97 1.00 0.99 33
3 1.00 0.97 0.99 34
4 1.00 1.00 1.00 46
5 0.96 0.94 0.95 47
6 0.97 0.97 0.97 35
7 1.00 0.97 0.99 34
8 0.97 1.00 0.98 30
9 0.97 0.97 0.97 40
accuracy 0.98 360
macro avg 0.98 0.98 0.98 360
weighted avg 0.98 0.98 0.98 360
There are still so many built-in datasets by Scikit-Learn. Find the one that is suitable for your use cases.
Additional Resources
- Awesome list of datasets in 100+ categories
- A Guide to Getting Datasets for Machine Learning in Python
- How to Access Sample Datasets in Pandas
