Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).
In machine learning, logistic regression is a classification algorithm used to predict a categorical dependent variable. It's one of the most popular algorithms for binary classification problems.
- Logistic function (sigmoid) and its properties
- Decision boundary in logistic regression
- Maximum likelihood estimation
- Interpreting logistic regression coefficients
- Evaluating logistic regression models (accuracy, precision, recall, F1-score)
Exercise: Titanic Survival Prediction
Using the Titanic dataset, build a logistic regression model to predict passenger survival.
- Load the Titanic dataset
- Perform exploratory data analysis
- Handle missing values and categorical features
- Build a logistic regression model
- Evaluate the model using appropriate metrics
- Interpret the coefficients of your model
StatQuest: Logistic Regression
Main resource for today
Scikit-learn Documentation
Official documentation for LogisticRegression
Towards Data Science
Logistic Regression for Machine Learning
Khan Academy
Introduction to logistic regression
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