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README.Rmd
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---
output: github_document
bibliography: docs/online_purchasing_intention_refs.bib
---
# Online Purchasing Intention Predictor
- Co-authors: Yazan Saleh, Mai Le, Tran Doan Khanh Vu, Jingjing Zhi
A data analysis project aiming to predict purchasing intentions of online shoppers.
## About
In this project, we compare 3 different algorithms with the aim of building a classification model to predict purchasing intentions of online shoppers given browser session data and pageview data. We also tried each algorithm with both setting the weights so that the classes are "equal" (`class_weight="balanced"`) and SMOTE to see which approach will better address the class imbalance issue. Given our dataset, random forest classifier with SMOTE was identified as the best model compared to support vector machine and logistic regression classifiers. Although the model performed relatively well in terms of accuracy with a score of 0.89, its performance was less robust when scored using `f1` metric. Specifically, the model had an `f1` score of 0.67 and mis-classified 336 observations, 146 of which were false negatives. The 146 incorrect classifications are significant as they represent potential sources of missed revenue for e-commerce businesses. Therefore, we recommend improving this model prior to deployment in the real-world.
The data set used in this project is the "Online Shoppers Purchasing Intention" dataset provided by the [Gözalan Group](http://www.gozalangroup.com.tr/) and used by Sakar et. al in their analysis published in [Neural Computing and Applications](https://link.springer.com/article/10.1007/s00521-018-3523-0) [@Sakar2019]. The data set was sourced from UCI's Machine Learning Repository [@Dua:2019] at this [link](https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset). The specific file used for the analysis found [here](https://archive.ics.uci.edu/ml/machine-learning-databases/00468/online_shoppers_intention.csv). Each row in the data set contains pageview and session information belonging to a different visitor that browsed [Columbia](https://www.columbia.com.tr), an online e-commerce platform based in Turkey. Each row also contains the target class `REVENUE`, a boolean flag indicating whether that user session contributed to revenue or not. Pageview data includes the type of pages that the visitor browsed to and the duration spent on each page. Session information includes visitor-identifying data such as browser information, operating system information as well visitor location and type.
## Reports
The final report can be found [here](https://htmlpreview.github.io/?https://github.com/UBC-MDS/DSCI_522_group_31/blob/main/reports/report.html)
## Usage
To replicate this analysis, please follow these steps:
### Step 1: Clone this GitHub repository
### Step 2A: Using Docker
*note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)*
To replicate the analysis, install [Docker](https://www.docker.com/get-started). Then run the following command at the command line/terminal from the root directory of this project:
```
docker run --rm -v /$(pwd):/home/online_shopping_predictor lephanthuymai/dsci_522_group_31:latest make -C /home/online_shopping_predictor all
```
To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:
```
docker run --rm -v /$(pwd):/home/online_shopping_predictor lephanthuymai/dsci_522_group_31:latest make -C /home/online_shopping_predictor clean
```
### Step 2B: Without using Docker
Create and activate a conda environment using the `env.yaml` at the root of this project by running the following command at the root directory of the project. (Alternatively, you can manually install the dependencies listed in the `env.yaml` file)
```bash
conda env create --file env.yaml
conda activate online_shopping_prediction_env
```
Run the following commands at the command line/terminal from the root directory of this project:
```bash
make all
```
To reset the project to a clean state and re-run the analysis, run the following command at the command line/terminal from the root directory of the project:
```bash
make clean
```
## Dependencies
Please see the project dependencies included in the root env.yaml file
## License
All Online Purchasing Intention Predictor materials are made available under the **Creative Commons Attribution 2.5 Canada License** ([CC BY 2.5 CA](https://creativecommons.org/licenses/by/2.5/ca/)).
# References