Online Shopping Intention Predictor

• author: Lesley Miller, Cheng Min, Shivam Verma

About

Here we attempt to build a classification model using the light gradient boosting algorithm which can use webpage metrics from a given online shopping website to predict whether the final action of a new customer is purchasing (i.e., Revenue is TRUE) or not (i.e., Revenue is FALSE). Our final classifier performed well on an unseen test data set, with the F1 score of 0.646 and the test accuray calculated to be 90.9%. The precision and recall of our classifier on the test set are 0.699 and 0.6 respectively. Due to substantially high number of false positives & negatives, we recommend further iteration to improve this model.

The data set used in this project is of online shopping webpage metrics created by C. Okan Sakar, S. Olcay Polat, Mete Katircioglu & Yomi Kastro(Sakar et al. 2019). It was sourced from the UCI Machine Learning Repository(Dua and Graff 2019) and can be found here. Each row in the data set represents summary behavior extracted from the URL information ,including the final action (purchase or not) and several other measurements (e.g., Number of Distinct Product Related pages, Time spent on Product Related pages, closeness of site visitng time to a special day, etc.).

Report

The final report can be found here.

Diagrams

The dependency diagram below illustrates script orders and dependencies for our Makefile and was created using makefile2graph, click here for the original graph:

Usage:

To replicate the analysis, there are two suggested ways to run this analysis:

1. Using Docker

note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash), if you are using Windows Command Prompt, replace /$(pwd) with PATH_ON_YOUR_COMPUTER.

1. Install Docker
2. Download/clone this repository
3. Use the command line to navigate to the root of this downloaded/cloned repo
4. Type the following:

docker run --rm -v /$(pwd):"/DSCI522_309" chengmin/dsci522-group309:v1.0 bash -c "make -C /DSCI522_309 all"

5. To reset/undo the analysis, type the following:

docker run --rm -v /$(pwd):"/DSCI522_309" chengmin/dsci522-group309:v1.0 bash -c "make -C /DSCI522_309 clean"

2. After installing all dependencies (does not depend on Docker)

1. Clone this repo, and using the command line, navigate to the root of this project.
2. To run the analysis, type the following commands:

make all

3. To reset/undo the analysis, type the following commands:

make clean

Dependencies

• Python 3.7.4 and Python packages:
  • docopt==0.6.2
  • pandas==1.0.0
  • numpy==1.17.2
  • sklearn==0.22.1
  • lightgbm==2.3.0
• R version 3.6.2 and R packages:
  • knitr==1.27
  • tidyverse==1.3.0
  • caret==6.0.85
  • kableExtra==1.1.0
  • scales==1.1.0
  • docopt==0.6.1
  • testthat==2.3.1
  • data.table==1.12.8
  • here==0.1
  • pheatmap==1.0.12
• GNU make 4.2.1

License

The Online Shopping Intention Predictor materials here are licensed under the Creative Commons Attribution 2.5 Canada License (CC BY 2.5 CA). If re-using/re-mixing please provide attribution and link to this webpage.

References

Dua, Dheeru, and Casey Graff. 2019. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml.

Sakar, C Okan, S Olcay Polat, Mete Katircioglu, and Yomi Kastro. 2019. “Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Multilayer Perceptron and Lstm Recurrent Neural Networks.” Neural Computing and Applications 31 (10): 6893–6908.