Breast Cancer Prediction Using Machine Learning

This 2-hour project-based course will teach you how to use Scikit-learn to create a Logistic regression model to classify breast cancer as Malignant or Benign. The Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle will be used. Objective is to classify cancer using a basic logistic regression classifier. The entire project will be completed in the Google Colab environment. To accomplish this project, you'll need a free Gmail account.

Please keep in mind that the dataset and model used in this project cannot be used in real-life situations. This information is solely used for instructional reasons. You will be able to construct a logistic regression classifier to distinguish between malignant and noncancerous patients at the conclusion of this project. You'll be able to set up and use the Google colab environment as well. You will be able to clean and prepare data for analysis as well. You should be familiar with the Python programming language as well as the Logistic Regression technique on a theoretical level. To accomplish this project, you'll need a free Gmail account.

Note: This course is best suited to students in the North American area. They were working on bringing the same experience to other parts of the world.

THE SKILLS YOU WILL DEVELOP:

• Python Programming
• Cancer prediction
• Machine Learning
• Data Mining

LEARN STEP BY STEP:

• Introduction and Import Libraries
• Download dataset directly from Kaggle
• Load & Explore the Dataset
• Perform LabelEncoding
• Split the data into Independent and Dependent sets and perform Feature Scaling
• Building Logistic Regression Classifier
• Evaluate the performance of the model

Rating: 4.2/5

Enroll here: coursera.org/projects/breast-cancer-prediction-using-machine-learning