- author: DSCI 522 Group10
- Group Member: Justin Fu, Junting He, Chuck Ho, Asma Al-Odaini
A data analysis project for DSCI 522 (Data Science workflows); a course in the Master of Data Science program at the University of British Columbia.
With a banking institution's telemarketing campaign data, we are attempting to build a classification model using the Logistice Regression algorithm to help us identify and pursue customers that will subscribe to a term deposit product. Our final classification model has reasonable performance on the test data, with a f1-score of 0.6 and a recall score of 0.90. On the 8238 test data cases, we correctly predicted 7091. However, it incorrectly predicted 1147 cases with 1056 being false positives and 91 being false negatives. We were more focused on reducing false negatives to capture more revenue. The false positives are also important as it may require more resources to reach out to potential customers, but our bias was towards revenue (recall). This model is not sufficient to be used in the industry since the f-1 score still has a lot to improve on. We recommend continuing to improve the classification performance before we use this in production.
The data set used in this project is from from a marketing campaign of a Portuguese bank and created by S. Moro, P. Cortez and P. Rita (Moro, Cortez, and Rita 2014). It was sourced from the UCI Machine Learning Repository (@ Dua and Graff 2017) and can be found here. Each row in the data set represents summary statistics with detail information of the contacted client, including bank client info (e.g. age, job, loan experience, etc.), other campaign attributes (e.g. number of contact, previous campaign outcome, etc) and social and economic attributes. (e.g. consumer confidence index, euribor rate, etc.) They were using telemarketing to attempt to get customers to sign up for the bank's term deposit product. The target in this dataset is yes or no to subscribing to the term deposit product.
The report can be found here.
There are two suggested ways to run this analysis:
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. Then clone this GitHub repository and run the following command at the command line/terminal from the root directory of this project:
docker run --rm -v /$(pwd):/home/rstudio/bank_tele_campaign_analysis jufu/ubcmds_522_group10_bank:v0.3.0 make -C /home/rstudio/bank_tele_campaign_analysis all
To reset the repo to a clean slate with no intermediate and result files, run the following command from the root directory of this project:
docker run --rm -v /$(pwd):/home/rstudio/bank_tele_campaign_analysis jufu/ubcmds_522_group10_bank:v0.3.0 make -C /home/rstudio/bank_tele_campaign_analysis clean
To replicate the analysis, clone this GitHub repository, install the dependencies listed below, and run the following commands at the command line/terminal from the root directory of this project:
#Activate the Conda environment first
conda activate bank
make all
To reset the repo to a clean slate with no intermediate and result files, run the following command from the root directory of this project:
make clean
We are providing you with a conda environment file which is available here. You can download this file and create a conda environment for this project and activate it as follows.
conda env create -f env-bank_marketing.yaml
conda activate bank
channels:
- conda-forge
- defaults dependencies:
- ipykernel
- matplotlib>=3.2.2
- scikit-learn>=0.23.2
- pandas>=1.1.3
- requests>=2.24.0
- graphviz
- python-graphviz
- altair>=4.1.0
- altair_saver
- docopt
- jinja2
- pip>=20
- pandas-profiling>=1.4.3
- seaborn
- pip:
- psutil>=5.7.2
- xgboost>=1.*
- lightgbm>=3.*
- git+git://github.com/mgelbart/plot-classifier.git
R version 3.6.1 and R packages:
- knitr==1.26
- tidyverse==1.2.1
- caret==6.0-84
- ggridges==0.5.1
- ggthemes==4.2.0
- docopt==0.7.1
- rmarkdown==2.5
The Post Campaign Bank Term Deposit Subscription 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.
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. 2014. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml.
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