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Logistic Regression with Sklearn

Python
Supervised Learning

Here we initialise and train a Logistic Regression model before using the model to make predictions. Finally we find the intercept and cofficients of the model along with analysing the models performance on test data using the Classification Report.

#Import Library
from sklearn.linear_model import LogisticRegression

#Initialise & Train Model
model = LogisticRegression()
model.fit(X_train, y_train)

#Use Model to Make Predictions
y_pred = model.predict(X_test)

#Get the Model Intercept & Coefficients
print('Intercept:',model.intercept_)
coef = pd.DataFrame(model.coef_.reshape(30,1), cols, columns=['Coefficients'])
print(coef)

#Get the Classification Report
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))

By analyseup - Last Updated Jan. 10, 2022, 11:27 p.m.

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