# Coupon purchase prediction github

The settings in the side-panel are the same as before.

In the screenshot below we see a coefficient or rather an odds-ratio plot with confidence intervals. The relative importance of gender and class compared to age clearly stands out. Probabilities, are more convenient for interpretation than coefficients or odds from a logistic regression model.

The figure above shows that the probabilities drop sharply for 2nd and 3rd class passengers compared to 1st class passengers. For males of average age approx. For 60 year old males in 3rd class the probability drops to around 3.

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For a more comprehensive overview of the influence of gender, age, and passenger class on the chances of survival we can generate a full table of probabilities by selecting Command from the Prediction input dropdown in the Predict tab and selecting Titanic from the Predict for profiles. There are too many numbers to easily interpret in table form but the figure gives a clear overview of how survival probabilities change with age , gender , and pclass:.

We will use the dataset dvd. We can use logistic regression to estimate the effect of the coupon on purchase of a newly released DVD. Customers who received the coupon and purchased the DVD are identified in the data by the variable buy. To keep the example simple, we use only information on the value of the coupon customers received.

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Hence, buy is our response variable and coupon is our explanatory or predictor variable. The regression output shows that coupon value is a statistically significant predictor of customer purchase. The coefficient from the logistic regression is 0. Because the odds ratio is larger than 1, a higher coupon value is associated with higher odds of purchase. Also, because the p.

## Build a Coupon Purchase Prediction Model in R

An odds ratio of 1 is equivalent to a coefficient estimate of 0 in a linear regression and implies that the explanatory or predictor variable has no effect on the response variable. The estimated odds ratio of 2. If a plot was created it can be customized using ggplot2 commands or with gridExtra. Functionality To estimate a logistic regression we need a binary response variable and one or more explanatory variables.

Additional output that requires re-estimation: Odds-ratios can be hard to compare if the explanatory variables are measured on different scales. By standardizing the data before estimation we can see which variables move-the-needle most. Note that a one-unit change is now equated to 2 x the standard deviation of the variable. Replace all explanatory variables X by X - mean X. Cancel reply Your email address will not be published. All Posts posts. Alumni 31 posts. Capstone 91 posts. Community 35 posts. Data Science News and Sharing 37 posts. Featured 21 posts.

Machine Learning posts. Meetup posts. R posts. Note for OSX and Windows users: This is due to the fact that Clang and Miniconda does not support OpenMP, and installing an OpenMP-enabled version of gcc is complicated and labour-intensive.

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Building with the default Python distribution included in OSX is also not supported; please try the version from Homebrew or Anaconda. On many systems it may be more convenient to try LightFM out in a Docker container. This repository provides a small Dockerfile sufficient to run LightFM and its examples.

## Model > Estimate > Logistic regression (GLM)

To run it:. Model fitting is very straightforward using the main LightFM class. To get predictions, call model.

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User and item features can be incorporated into training by passing them into the fit method. This implementation uses asynchronous stochastic gradient descent [6] for training. This can lead to lower accuracy when the interaction matrix or the feature matrices are very dense and a large number of threads is used. In practice, however, training on a sparse dataset with 20 threads does not lead to a measurable loss of accuracy.