Breast Cancer detection using Logistic Regression
- Free tutorial
- Rating: 4.6 out of 54.6 (3,871 ratings)
- 34,144 students
- 1hr 4min of on-demand video
- Created by Hadelin de Ponteves, SuperDataScience Team, Ligency Team
- English
What you’ll learn
- How to build a Logistic Regression model for a Real-World Case Study
- Work on Google Colab
Requirements
- Basic theory of Logistic Regression
Description
Did you know that approximately 70% of data science problems involve classification and logistic regression is a common solution for binary problems?
Logistic regression has many applications in data science, but in the world of healthcare, it can really drive life-changing action.
In this SuperDataScience case study course, learn how to detect breast cancer by applying a logistic regression model on a real-world dataset and predict whether a tumor is benign (not breast cancer) or malignant (breast cancer) based off its characteristics.
By the end of the course, you will be able to build a logistic regression model to identify correlations between the following 9 independent variables and the class of the tumor (benign or malignant).
- Clump thickness
- Uniformity of cell size
- Uniformity of cell shape
- Marginal adhesion
- Single epithelial cell
- Bare Nuclei
- Bland chromatin
- Normal nucleoli
- Mitoses
Logistic regression can identify important predictors of breast cancer using odds ratios and generate confidence intervals that provide additional information for decision-making. Model performance depends on the ability of the radiologists to accurately identify findings on mammograms.
Join AI expert Hadelin de Ponteves as you code the solution along with him in this 1-hour, 3-part case study:
Part 1: Data Preprocessing
- Importing the dataset
- Splitting the dataset into a training set and test set
Part 2: Training and Inference
- Training the logistic regression model on the training set
- Predicting the test set results
Part 3: Evaluating the Model
- Making the confusion matrix
- Computing the accuracy with k-Fold cross-validation
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
Plus, you’ll do it all using Google’s Colab free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will save you time and supercharge your data science toolkit.
Click the ‘Enroll Now’ button to join Hadelin’s class today!
More about logistic regression:
Logistic regression is a method of statistical analysis used to predict a data value based on prior observations of a dataset. A logistic regression model predicts the value of a dependent variable by analyzing the relationship between one or more existing independent variables.
In data science, logistic regression is a Machine Learning algorithm used for classification problems and predictive analysis.
More real-world applications of logistical regression include:
- Bankruptcy predictions
- Credit scoring
- Consumer behavior
- Customer retention
- Spam detection
Who this course is for:
- Anyone interested in Machine Learning, AI or Data Science
- Anyone who wants to learn how to make accurate predictions
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Course content
5 sections • 11 lectures • 1h 6m total lengthCollapse all sections
Introduction4 lectures • 17min
- Getting started13:42
- Dataset + Code + Colab Link00:11
- Study Tips for Success00:46
- Importing the libraries02:40
Data Preprocessing2 lectures • 20min
- Importing the dataset13:26
- Splitting the dataset into the Training set and Test set06:55
Training and Inference2 lectures • 11min
- Training the Logistic Regression model on the Training set05:53
- Predicting the Test set results04:40
Evaluating the Model2 lectures • 17min
- Making the Confusion Matrix06:15
- Computing the accuracy with k-Fold Cross Validation10:53
BONUS Lectures1 lecture • 1min
- YOUR SPECIAL BONUS01:05
