Step $2$: Create a Logistic Regression Model

Create a logistic regression model named logreg using LogisticRegression$().$

What is the model accuracy on the training set? Hint: you may use $.$score$()$ to get the model accuracy, covered in Week $2$ Lab.

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from sklearn.linear$\_$model import LogisticRegression

logreg $=$ LogisticRegression$()$

logreg$.$fit$($X$\_$train, y$\_$train$)$

Then we evalute the model performance on the test set and see how it generalizes to a dataset that is never used to create the model.

First get the predictions for test set using $.$predict$(),$ save them in y$\_$pred, then use accuracy$\_$score$()$ to get the model accuracy on test set.

What is model performance on the test set?

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print$("$Accuracy on the training set:$\{$:$\mathrm{.}3$f$\}".$format$($logreg$.$score$($X$\_$train, y$\_$train$)))$

Accuracy on the training set:$0\mathrm{.}850$

Now let's create a dataframe to show the coefficient of each variable in this logistic regression model. We first save the names of all variables in to an object called heart$\_$feature$\_$names, then use it as the index in $.$DataFrame$().$

The coefficients are saved in the first element in $.$coef$\_$ attribute.

Which variable has the highest coefficient?