Linear Regression with Python Video
- Learn the key concepts in linear regression including Targets, Predictors, Outliers, and Independent and Dependent Variables.
- 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.
- Load data from different sources into Pandas. Pandas is a software library written for the Python programming language used for data manipulation and analysis.
- Explore the different reasons why data can be missing, including due to incomplete extracts and corrupt data.
- Practice data visualization using Matplotlib for both functional and object-oriented methods.
- Split data to avoid overfitting when performing linear regression.
- Create a linear regression model using python and several libraries.
- Evaluate the linear regression model you created, using Root Mean Squared Error (RMSE) and R-squared (R2).
- Make predictions based on a linear regression model.
- Follow along and predict future values using your model.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date