Logistic Regression
Logistic Regression  Data Science Project
Classification in Depth with Scikit-Learn

Logistic Regression

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.
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Logistic Regression Logistic Regression
Project Created by

Verónica Barraza

Project Activities

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

Logistic Function

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

Did you find any missing value?

codevalidated

Standardize the data

Use StandardScaler to standardize the features and store the results in the variables X_train_scaler and X_test_scaler.

codevalidated

Use the model to make predictions on the test data and evaluate its accuracy score, based on the following code:

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

Fit a logistic regression model using the given input and target values. Once fitted, obtain the first coeficient of this model.

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

Fit a logistic regression model using the given input and output patterns X and Y, respectively. Once fitted, determine the accuracy score of the test dataset

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

Logistic Regression Logistic Regression
Project Created by

Verónica Barraza

This project is part of

Classification in Depth with Scikit-Learn

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