Scikit-Learn

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With the recent advancement in AI, scikit-learn has become the most popular and essential tools for data scientists and machine learning engineers to develop resilient machine learning models.

Scikit-learn offers a range of algorithms for supervised, unsupervised and reinforcement learning algorithms which include non-linear, linear, ensemble, association, clustering, dimension reduction model and so much more.

It also provides evaluation, scaling, and selection tools to ensure you select the best models for a given objective.

How to install scikit-learn

To install scikit-learn, you can use PyPI or conda

Ps: this installation works for all platform(Windows, MacOS, linux)

Pypi:

To install scikit-learn via PyPI, open your terminal and run the command below


    $ pip install -U scikit-learn

Conda:

To install scikit-learn via conda use the command below.


    $ conda create -n sklearn-env -c conda-forge scikit-learn
    $ Conda activate sklearn-env

Scikit learn example

Let’s create a simple linear regression model with scikit-learn


    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import train_test_split
    import numpy as np

    # Generate some data for the example
    np.random.seed(0)
    X = np.random.rand(1000, 1)
    y = 9 + 6 * X + np.random.rand(1000, 1)

    # Split the data into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # Create a LinearRegression object and fit it to the training data
    reg = LinearRegression()
    reg.fit(X_train, y_train)

    # Make predictions on the test set
    y_pred = reg.predict(X_test)

    # Print the coefficient and intercept of the fitted model
    print("Coefficient: ", reg.coef_)
    print("Intercept: ", reg.intercept_)

This code above uses synthetics data, perform cross validation, and fit the model to linear regression then compute for coefficient and the intercept of the model.

Scikit-learn in action: