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Logistic Regression Practical Case Study

Breast Cancer detection using Logistic Regression

4.56 / 5.0
26768 students1 hours 6 minutes

Created by Hadelin de Ponteves, offered on Udemy

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Student feedback

8.5/10

To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 26768 students which left 3064 reviews at an average rating of 4.56. Impressive!

Course length

8.4/10

We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 1 hours 6 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.

Overall score

8.5/10

This course currently has a bestcourses score of 8.5/10, which makes it a great course to learn from. On our entire platform, only 15% of courses achieve this rating!

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

What you will learn

  • How to build a Logistic Regression model for a Real-World Case Study
  • Work on Google Colab

Requirements

  • Basic theory of Logistic Regression
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Frequently asked questions

  • Price: Free
  • Platform: Udemy
  • Language: English
  • 1 hours 6 minutes
Logistic Regression Practical Case Study thumbnail

bestcourses score: 8.5/10

This course is better than many others in its category.