Logistic Regression Practical Case Study

Breast Cancer detection using Logistic Regression

4.61 (4327 reviews)
Udemy
platform
English
language
Data Science
category
Logistic Regression Practical Case Study
38,493
students
1 hour
content
Mar 2024
last update
FREE
regular price

What you will learn

How to build a Logistic Regression model for a Real-World Case Study

Work on Google Colab

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

Content

Introduction

Getting started
Dataset + Code + Colab Link
Importing the libraries

Data Preprocessing

Importing the dataset
Splitting the dataset into the Training set and Test set

Training and Inference

Training the Logistic Regression model on the Training set
Predicting the Test set results

Evaluating the Model

Making the Confusion Matrix
Computing the accuracy with k-Fold Cross Validation

BONUS Lectures

YOUR SPECIAL BONUS

Reviews

Sakshaleni
October 31, 2023
I enrolled in A-Z Machine Learning. The link for this was given in the lecture. I am proud of myself that I can do most of the coding on my own. Thank you.
Antoni
October 30, 2023
free bonus course having the same high quality as the main full course on Machine learning, clear explanations of every necessary detail of code provided by the tutor
Sourav
September 24, 2023
this was an amazing practical example to get handson experience with some real life datasets. great practice for someone looking to apply learned skills into practice
Vedang
August 11, 2023
It was a good course. It was nice to go through the steps of Logistic Regression and do the k-fold Cross Validation at the end to evaluate model accuracy.
Jaime
August 10, 2023
This practice lesson provides much more clarity for multivariate machine learning classification logistic model
Hiroki
July 30, 2023
I must say that it was an extremely enriching experience. The instructor has managed to strike the perfect balance between theory and application, making this complex statistical concept very accessible. The practical case study used to illustrate logistic regression was thoughtfully chosen. It helped me to see how this technique can be applied in a real-world scenario, and I found the step-by-step approach to building and evaluating the model particularly helpful. I especially appreciated the inclusion of code snippets, the guidance on data preprocessing, and the attention to model evaluation metrics. It wasn't just about getting the model to work; it was about understanding why it worked and how to ensure its reliability. The additional resources and exercises provided at the end of the lecture were the cherry on top, allowing me to deepen my understanding further. This lecture is a must for anyone looking to grasp logistic regression in a hands-on and intuitive way. Thank you for this excellent learning opportunity!
Oswaldo
July 18, 2023
Case studies such as Breast Cancer Prediction and Energy Output, are a nice way to practice ML, and my Mechanical Engineering background. I have always been fascinated with the Medical, Defense, and Energy fields.
Shiv
July 17, 2023
The introduction and then the details are so well explained. There was no doubt at all. Could understand easily. Hope the later sections follow the same.
Ashwanth
July 6, 2023
This was an amazing add on exercise to the main course. PLease input more of this into the course. Looks like the UCI repository is different today and I was unable to download the CVS file directly. Please update the video for the updated website.
Karthik
June 18, 2023
It is a very good course to get an idea about how these modes are built, and also to apply them practically.. Enjoyed Machine Learning!!
Farhad
June 15, 2023
I thought he is gonna explain how to visualize the chart of Logistic Regression... But what he did was just importing libraries and dataset etc...
Ran
April 6, 2023
Very nice. Note i took it as part of the bigger course. In that course they direct you to this one after finishing the Logistic Regression course
Hiya
January 14, 2023
The course is absolutely great for a fresher like me in the field of Machine Learning. I would specifically like to leverage the coding part which I struggled to deal with before attending the course.
Osman
November 22, 2022
I came here from the machine learning a-z course to brush up on my logistic regression skills. Again clear instructions. I recommend this course to everyone interested in machine learning. Also, code templates are fantastic and so usefull.
Ashish
September 17, 2022
Since , I enrolled in Machine Learning this practical activity is exciting and easy. I feel happy to do the activity and enjoyed it very well.

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2968644
udemy ID
4/7/2020
course created date
4/10/2020
course indexed date
Lee Jia Cheng
course submited by