12.1.3: Modeling with scikit-learn
- Page ID
- 53425
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Modeling with scikit-learn
For predictive modeling, scikit-learn provides a consistent interface. For example, to train a simple linear regression model:
from sklearn.linear_model import LinearRegression
import numpy as np
# Assume df has numeric features and target
X = df_filtered.values # feature matrix
y = df_filtered["units_sold"].values # target vector
model = LinearRegression()
model.fit(X, y) # train model
y_pred = model.predict(X) # predictions
print("Coefficients:", model.coef_, "Intercept:", model.intercept_)
This code uses LinearRegression from scikit-learn to fit a model predicting units sold from ad spend and promotion spend. After calling fit, we can inspect coefficients or make predictions. Scikit-learn encourages separating data into feature matrix X and target array y, then calling model.fit(X, y) and model.predict(X). A wide variety of algorithms are available (trees, SVM, clustering, etc.) with similar usage patterns.