One of the most used Python libraries for Machine Learning is scikit-learn. It provides implementation a broad range of algorithm, and is relatively simple to use out-of-the-box. Let’s look at an example of Logistic Regression with the classic ‘iris’ dataset.

# Import libraries
import pandas as pd
import seaborn as sns
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

Load dataset

# Load sample dataset into a DataFrame
X = sns.load_dataset('iris')
X

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
…	…	…	…	…	…
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

# Pop the target column 'species' and store in a distinct Series
y = X.pop('species')

Split into train and test sets

# Use `train_test_split()` function from sklearn to extract a 20% random test set 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Fit model

# Instantiate a logistic regression model with default parameters
model = LogisticRegression()

# Fit model on training set
model.fit(X_train, y_train)

# Predict on test set
y_pred = model.predict(X_test)

Evaluate model performance

# Classification report
print(sklearn.metrics.classification_report(y_test, y_pred))

        precision    recall  f1-score   support

      setosa       1.00      1.00      1.00         6
  versicolor       0.83      1.00      0.91        10
   virginica       1.00      0.86      0.92        14

    accuracy                           0.93        30
   macro avg       0.94      0.95      0.94        30
weighted avg       0.94      0.93      0.93        30

# Plot confusion matrix
sklearn.metrics.plot_confusion_matrix(model, X_test, y_test)

Train Machine Learning algorithms with Python scikit-learn

Load dataset

Split into train and test sets

Fit model

Evaluate model performance