Machine Learning with Logistic Regression in Excel, R, and Power BI
2h 50mIntermediate2021-11-05
Authors
Helen Wall
Data analytics and business analysis expert
Course details
Excel, R, and Power BI are applications universally used in data science and across businesses and organizations around the world. If you’ve spent any time trying to figure out how to better model your data to get useful insights from it that you can act upon, you’ve most likely encountered these applications. In this course, Helen Wall shows how to use Excel, R, and Power BI for logistic regression in order to model data to predict the classification labels like detecting fraud or medical trial successes. Helen walks through several examples of logistic regression. She shows how to use Excel to tangibly calculate the regression model, then use R for more intensive calculations and visualizations. She then illustrates how to use Power BI to integrate the capabilities of Excel calculations and R in a scalable, sharable model.
Skills covered
RPower BIStatisticsBusiness AnalyticsBusiness IntelligenceMachine LearningSpreadsheetsMicrosoft ExcelData AnalysisArtificial Intelligence (AI)Programming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Apply logistic regressions to solve problems
- 02 - What you should know
- 03 - Introduction to the course project
- 04 - Configuring the Excel Solver Add-in
- 05 - Working with R
- 06 - Configuring R in Power BI
1. Distributions and Probabilities
- 07 - Introducing AI and logistic regression
- 08 - Differentiating between odds and probabilities
- 09 - Differentiating between distributions
- 10 - Calculating logs and exponents
- 11 - Sigmoid curve
- 12 - Utilizing training and testing data sets
2. Binomial Logistic Regression
- 13 - Calculating linear regression
- 14 - Working with the logit model
- 15 - Calculating log likelihood
- 16 - Constructing MLE
- 17 - Solving MLE
- 18 - Predicting outcomes
- 19 - Visualizing logistic regression
- 20 - Challenge - Calculating logistic regression
- 21 - Solution - Calculating logistic regression
3. Fine-Tuning the Model
- 22 - Adding more independent variables
- 23 - Transforming variables
- 24 - Calculating correlations
- 25 - Using statistics
- 26 - Configuring confusion tables
- 27 - Challenge - Fine-tuning the model
- 28 - Solution - Fine-tuning the model
4. Multinomial Regression
- 29 - Calculating odds for multinomial models
- 30 - Calculating probabilities for multinomial models
- 31 - Calculating multinomial log likelihoods
- 32 - Running MLE
- 33 - Making the predictions
5. Working in Power BI with R
- 34 - Running R scripts in the Power Query Editor
- 35 - Running R standard visuals
- 36 - Interacting between visual components
- 37 - Challenge - Moving into Power BI
- 38 - Solution - Moving into Power BI
Conclusion
- 39 - Next steps with logistic regressions