What is Regression Analysis with Example? | Model Evaluation & Selection | Regularization Techniques
Regression Analysis is a powerful statistical method that helps us understand and quantify the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It is widely used across various industries to make predictions, identify trends, and uncover hidden patterns.
Thank you for watching this video, For more details or a free demo with our expert, write to us at sales@infosectrain.com
Agenda:
Regression Analysis
Simple Linear Regression
Model Evaluation and Selection
Multiple Linear Regression
Regularization Techniques: Ridge and Lasso
Model Evaluation Metrics: R-squared, MSE, MAE
Watch now: https://www.youtube.com/watch?v=EZOBwVSRDZc&t=2s
#DataScience #RegressionAnalysis #DataDriven #Analytics #DecisionMaking #BusinessInsights #MachineLearning #AI #Statistics #statistics #infosectrain #learntorise
Regression Analysis is a powerful statistical method that helps us understand and quantify the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It is widely used across various industries to make predictions, identify trends, and uncover hidden patterns.
Thank you for watching this video, For more details or a free demo with our expert, write to us at sales@infosectrain.com
Agenda:
Regression Analysis
Simple Linear Regression
Model Evaluation and Selection
Multiple Linear Regression
Regularization Techniques: Ridge and Lasso
Model Evaluation Metrics: R-squared, MSE, MAE
Watch now: https://www.youtube.com/watch?v=EZOBwVSRDZc&t=2s
#DataScience #RegressionAnalysis #DataDriven #Analytics #DecisionMaking #BusinessInsights #MachineLearning #AI #Statistics #statistics #infosectrain #learntorise
What is Regression Analysis with Example? | Model Evaluation & Selection | Regularization Techniques
Regression Analysis is a powerful statistical method that helps us understand and quantify the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It is widely used across various industries to make predictions, identify trends, and uncover hidden patterns.
Thank you for watching this video, For more details or a free demo with our expert, write to us at sales@infosectrain.com
β‘οΈAgenda:
π Regression Analysis
π Simple Linear Regression
π Model Evaluation and Selection
π Multiple Linear Regression
π Regularization Techniques: Ridge and Lasso
π Model Evaluation Metrics: R-squared, MSE, MAE
Watch now: https://www.youtube.com/watch?v=EZOBwVSRDZc&t=2s
#DataScience #RegressionAnalysis #DataDriven #Analytics #DecisionMaking #BusinessInsights #MachineLearning #AI #Statistics #statistics #infosectrain #learntorise
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