Breast Cancer Prediction Using Machine Learning

Offered By
Coursera Project Network
In this Guided Project, you will:

Learn to Build Logistic Regression Classifier to Classify Cancer as Malignant or Benign

Learn to download dataset directly from Kaggle using Kaggle API

Learn to work with Google Colab in Cloud

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2 hours long project-based course, you will learn to build a Logistic regression model using Scikit-learn to classify breast cancer as either Malignant or Benign. We will use the Breast Cancer Wisconsin (Diagnostic) Data Set from Kaggle. Our goal is to use a simple logistic regression classifier for cancer classification. We will be carrying out the entire project on the Google Colab environment. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in real-life. We are only using this data for educational purposes. By the end of this project, you will be able to build the logistic regression classifier to classify between cancerous and noncancerous patients. You will also be able to set up and work with the Google colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of the Logistic Regression algorithm. You will need a free Gmail account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Python ProgrammingCancer predictionMachine LearningData Mining

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Import Libraries

  2. Download dataset directly from Kaggle

  3. Load & Explore the Dataset

  4. Perform LabelEncoding

  5. Split the data into Independent and Dependent sets and perform Feature Scaling

  6. Building Logistic Regression Classifier

  7. Evaluate the performance of the model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.