Linear Regression with Python Video

Linear Regression with Python Video

Objectives:

1. Learn the key concepts in linear regression including Targets, Predictors, Outliers, and Independent and Dependent Variables.
2. Understand the five assumptions that must be in place to perform linear regression: linear relationship, multivariate normality, little or no multicollinearity, no auto-correlation, and homoscedasticity.
3. Load data from different sources into Pandas. Pandas is a software  library written for the Python programming language used for data  manipulation and analysis.
4. Explore the different reasons why data can be missing, including due to  incomplete extracts and corrupt data.
5. Practice data visualization using Matplotlib for both functional and object-oriented methods.
6. Split data to avoid overfitting when performing linear regression.
7. Create a linear regression model using python and several libraries.
8. Evaluate the linear regression model you created, using Root Mean Squared Error (RMSE) and R-squared (R2).
9. Make predictions based on a linear regression model.
10. Follow along and predict future values using your model.

Instructor: Dhiraj Kumar

Length: 1 hour

Access period: For one year starting from purchase date