The Logistic Regression Algorithm in Python Video

The Logistic Regression Algorithm in Python Video

Objectives:

1. Learn about logistic regression and be able to contrast it with linear regression.
2. 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.
3. 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.
4. Create a logistic model using the Titanic dataset.
5. Predict the output from a logistic model, using the scikit-learn’s predict() function.
6. 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