https://github.com/tathithienthanh/datamining-banking-dataset

Implement some learned data mining techniques and predict if the client will subscribe to a term deposit
https://github.com/tathithienthanh/datamining-banking-dataset

apriori association-rules classification clustering data-analysis data-mining data-processing google-colab ipynb kmeans naive-bayes py python scikit-learn svm visualization

Last synced: 4 days ago
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Implement some learned data mining techniques and predict if the client will subscribe to a term deposit

• Host: GitHub
• URL: https://github.com/tathithienthanh/datamining-banking-dataset
• Owner: tathithienthanh
• Created: 2024-02-02T07:05:34.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-02T07:37:50.000Z (12 months ago)
• Last Synced: 2024-11-26T01:15:23.685Z (2 months ago)
• Topics: apriori, association-rules, classification, clustering, data-analysis, data-mining, data-processing, google-colab, ipynb, kmeans, naive-bayes, py, python, scikit-learn, svm, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 3.18 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DataMining-Banking-Dataset

Implement some learned data mining techniques and predict if the client will subscribe to a term deposit

# Citation

This dataset is publicly available for research. It has been picked up from the UCI Machine Learning with random sampling and a few additional columns.

Please add this citation if you use this dataset for any further analysis.

*S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014*

# Past Usage

The full dataset was described and analyzed in:

* S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology.

* In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

# Detailed Information about the dataset

https://www.kaggle.com/code/santhoshtsk/banking-dataset-analysis-and-prediction/input

# About the report and code

* Used techniques: preprocessing, clustering with K-Means, association rule with apriori, classification with Gaussian Naive Bayes and SVM, ...

* Re-edit the path if you use our code for importing or loading the dataset.

* The report is submitted and scored by the instructor.

* The report is for reference only, please do not edit or use for other purposes.

*All the files are done by me and @phamcongthuan, if you reuse the code please add the citation*