Oppiskele Introduction to Predictive Analytics in HR

Pyyhkäise näyttääksesi valikon

Predictive analytics is a powerful branch of data analysis that focuses on using historical data to forecast future outcomes. In the context of People Analytics, predictive analytics helps you anticipate key HR outcomes such as employee attrition, performance, or engagement. By leveraging patterns in existing data, you can move beyond simply describing what has happened to making informed predictions about what is likely to happen. This approach provides HR professionals with actionable insights, enabling proactive strategies to retain top talent, improve performance, and optimize workforce planning.


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import pandas as pd

# Create a simple DataFrame with features relevant for attrition prediction
data = {
    "age": [25, 34, 45, 29, 41],
    "tenure": [2, 6, 10, 3, 8],
    "satisfaction": [0.8, 0.6, 0.4, 0.9, 0.5],
    "attrition": [0, 1, 1, 0, 1]  # 0 = stayed, 1 = left
}

df = pd.DataFrame(data)
print(df)

When building predictive models in HR, the choice of features—also called variables or predictors—is critical. Features such as age, tenure, and satisfaction are selected because they have been shown to correlate with outcomes like attrition. For instance, employees with lower satisfaction scores or shorter tenure may be more likely to leave. By carefully selecting relevant features, you ensure the model focuses on the most informative factors, which increases the accuracy and usefulness of predictions.


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# Separate features (X) and target (y) for modeling
X = df[["age", "tenure", "satisfaction"]]
y = df["attrition"]

print("Features (X):")
print(X)
print("\nTarget (y):")
print(y)

1. What is the goal of predictive analytics in HR?

2. Fill in the blank: In predictive modeling, features are the ____ used to make predictions.

3. Why is feature selection important in predictive analytics?

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