Machine learning with scikit-learn

Supervised Learning with scikit-learn

George Boorman

Core Curriculum Manager, DataCamp

What is machine learning?

  • Machine learning is the process whereby:
    • Computers are given the ability to learn to make decisions from data
    • without being explicitly programmed!
Supervised Learning with scikit-learn

Examples of machine learning

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books

Supervised Learning with scikit-learn

Unsupervised learning

  • Uncovering hidden patterns from unlabeled data

  • Example:

    • Grouping customers into distinct categories (Clustering)

cluster analysis

Supervised Learning with scikit-learn

Supervised learning

  • The predicted values are known

  • Aim: Predict the target values of unseen data, given the features

 

features and target variables

Supervised Learning with scikit-learn

Types of supervised learning

  • Classification: Target variable consists of categories

atm

  • Regression: Target variable is continuous

 

properties

Supervised Learning with scikit-learn

Naming conventions

  • Feature = predictor variable = independent variable

  • Target variable = dependent variable = response variable

 

features and target variables

Supervised Learning with scikit-learn

Before you use supervised learning

  • Requirements:
    • No missing values
    • Data in numeric format
    • Data stored in pandas DataFrame or NumPy array

 

  • Perform Exploratory Data Analysis (EDA) first
Supervised Learning with scikit-learn

scikit-learn syntax

from sklearn.module import Model

model = Model()

model.fit(X, y)

predictions = model.predict(X_new)

print(predictions)

array([0, 0, 0, 0, 1, 0])
Supervised Learning with scikit-learn

Let's practice!

Supervised Learning with scikit-learn

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