The Logistic Regression Algorithm in Python Video
- Learn about logistic regression and be able to contrast it with linear regression.
- Practice the four ways of preprocessing data before performing logistic regression: missing data handling, categorical data handling, splitting into train and test set, and feature scaling.
- Perform data visualization using Seaborn, which is a Python data visualization library based on matplotlib. Seaborn provides the high-level interface to create statistical graphs.
- Create a logistic model using the Titanic dataset.
- Predict the output from a logistic model, using the scikit-learn’s predict() function.
- Check the accuracy of a logistic model. Gauge the performance of a logistic model using the confusion matrix.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date