# Maximizing ROI

Return on investment is a profitability measure that many companies use to quantify their efforts and make important business decisions. As an example, we will look at ROI related to product sales and promotions at Walmart. Using state-of-the-art data science techniques, and especially TreeNet gradient boosting, we will optimize product promotion options and maximize revenue.

## Video

Step-By-Step demonstration

## Maximizing ROI

Return on investment is a profitability measure that many companies use to quantify their efforts and make important business decisions. As an example, we will look at ROI related to product sales and promotions at Walmart. Using state-of-the-art data science techniques, and especially TreeNet gradient boosting, we will optimize product promotion options and maximize revenue.

• Presentation Slides Open
• Step-by-Step Tutorial Open
• Kaggle competition rules prevent us from distributing any data without your electronic acceptance of the terms of use, the data used in this presentation can be found (data set) here

## Tips & Tricks for Improving Your Logistic Regression

Logistic regression is a commonly used tool to analyze binary classification problems. However, lgiositic regression still faces the limitations of detecting nonlinearities and interactions in data. In this webinar, you will learn more advanced and intuitive machine learning techniques that improve on standard logistic regression in accuracy and other aspects. As an APPLIED example, we will demonstrate using a banking dataset where we will predict future financial stress of a loan applicant in order to determine whether they should be granted a loan. Although the focus is related to finance and loans, the concepts are relevant for anyone who actively uses logistic regression and wishes to improve accuracy and predictor understanding.

• Presentation Slides Open
• Step by Step Tutorial Open
• Dataset bank_marketing.csv Open
• Dataset delinquency_prediction.csv Open

## 3 Ways to Improve Your Regression

Linear regression plays a big part in the everyday life of a data analyst, but the results aren’t always satisfactory. What if you could drastically improve prediction accuracy in your regression with a new model that handles missing values, interactions, AND nonlinearities in your data? Instead of proceeding with a mediocre analysis, we will show you how MARS regression, TreeNet gradient boosting, and Random Forests can take your regression model to the next level with modern algorithms that are designed to expertly handle your modeling woes. With these state-of-the-art techniques, you’ll boost model performance without confusing coefficients or problematic p-values!

• Presentation Slides Open
• Step by Step Tutorial Open
• Data set Concrete.xls Open

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