Classification in Depth with Scikit-Learn

During this project, you'll learn about Logistic Regression using a real dataset. The objective is to diagnose whether or not a patient has diabetes based on diagnostic measurements included in the dataset. Using this example, you will learn the foundation of logistic regression, how to implement it using scikit-learn, and understand the decision boundaries of this model.

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

codevalidated

Now, create a set of x-values, using np.linspace() function, with a range from -10 to 10 and 100 data points. Store the result in the variable `x`

.

Next, we use the logistic function to calculate $y$ values (Store the result in the variable `y`

), and finally, we use matplotlib.pyplot to plot the $x$,$y$ values.

The resulting plot will show the S-shaped curve of the logistic function, which is useful for modeling probabilities.

multiplechoice

codevalidated

Use `StandardScaler`

to standardize the features and store the results in the variables `X_train_scaler`

and `X_test_scaler`

.

codevalidated

```
y_pred = logreg.predict(---)
print("Accuracy:", logreg.score(---, ---))
```

input

```
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=2,
random_state=0, cluster_std=0.85)
```

For the model set random_state = 0.

Round to three decimal places

input

```
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=2,
random_state=49, cluster_std=1.95)
```

For the model set random_state = 0.

Test dataset:

```
X_test= [[0.77499332, 5.10445441],
[2.33615249, 3.733497 ],
[3.53929109, 1.13492994],
[2.82894249, 4.00864077],
[4.61800816, 2.39809546]]
y_test=[0, 1, 1, 0, 0]
```

Round to two decimal places

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