Logistic Regression Practical Case Study
- Basic theory of Logistic Regression
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).
Uniformity of cell size
Uniformity of cell shape
Single epithelial cell
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:
- Anyone interested in Machine Learning, AI or Data Science
- Anyone who wants to learn how to make accurate predictions
- Getting started13:42
- Dataset + Code + Colab Link00:11
- Importing the libraries02:40
- Importing the dataset13:26
- Splitting the dataset into the Training set and Test set06:55
- Training the Logistic Regression model on the Training set05:53
- Predicting the Test set results04:40
- Making the Confusion Matrix06:15
- Computing the accuracy with k-Fold Cross Validation10:53
- YOUR SPECIAL BONUS01:05
Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created 70+ top-rated educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached 1M+ students in 210 countries.
My name is Kirill Eremenko and I am super-psyched that you are reading this!
Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.
From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.
To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!
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