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Learner Reviews & Feedback for Breast Cancer Prediction Using Machine Learning by Coursera Project Network
44 ratings
About the Course
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 classi...
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 classi...
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1 - 7 of 7 Reviews for Breast Cancer Prediction Using Machine Learning
By Yashwanth N
•Jul 28, 2021
Wonderful, just right way to guide the beginner to complete his first Machine Learning project.
By Barbara U
•Jan 14, 2024
The instructor is great!
By Harith Y
•May 24, 2025
Please teach more in-depth, about the theory, how the model actually works.
By Sawan K S
•Aug 22, 2023
Good to get started with ML journey and learn about logistic regression.
By Kachidere O
•Jan 7, 2022
A really great course, highly recommended
By Neeklesh S
•Mar 18, 2022
Good
By Eduard-Alexandru K
•Apr 8, 2025
Big error at X_train = scaler.fit_transform(X_train) X_test = scaler.fit_transform(X_test)