Scikit-learn is a popular open-source machine learning library for Python. It provides a range of supervised and unsupervised learning algorithms.

1. Install scikit-learn:

To install scikit-learn, use one of the following commands:

pip install scikit-learn

conda install scikit-learn

2. Import the library:

To use scikit-learn, you need to import it in your Python code. This is done using the following command:

import sklearn

3. Load the data:

The next step is to load the data into your Python environment. This can be done using the pandas library, which is a popular data manipulation library.

import pandas as pd

data = pd.read_csv(‘data.csv’)

4. Pre-process the data:

Before you can use the data for machine learning, you need to pre-process it. This involves cleaning the data, handling missing values, and transforming the data into a format that is suitable for machine learning.

5. Split the data:

Once the data is pre-processed, it needs to be split into training and test datasets. This is done using the train_test_split() function from scikit-learn.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

6. Build the model:

Once the data is split, you can build the machine learning model. Scikit-learn provides a range of supervised and unsupervised learning algorithms.

from sklearn.linear_model import LinearRegression

model = LinearRegression()

7. Train the model:

Once the model is built, it needs to be trained on the training data. This is done using the fit() function.

model.fit(X_train, y_train)

8. Evaluate the model:

Once the model is trained, it needs to be evaluated on the test data. This is done using the score() function.

score = model.score(X_test, y_test)

9. Make predictions:

Finally, the model can be used to make predictions on new data. This is done using the predict() function.

predictions = model.predict(X_new)