https://github.com/ssiarhei115/customer-classification

Developing ML model predicting bank' customer inclination to open a deposit
https://github.com/ssiarhei115/customer-classification

big-data big-data-analytics data data-science data-visualization mashine-learning

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Developing ML model predicting bank' customer inclination to open a deposit

• Host: GitHub
• URL: https://github.com/ssiarhei115/customer-classification
• Owner: ssiarhei115
• Created: 2023-10-31T09:48:39.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2023-10-31T09:57:59.000Z (about 1 year ago)
• Last Synced: 2023-10-31T10:40:17.911Z (about 1 year ago)
• Topics: big-data, big-data-analytics, data, data-science, data-visualization, mashine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bank customer classification

## Main goal

Developing ML model predicting bank' customer inclination to open a deposit

### Tasks 

1) EDA;

2) Feature preprocessing;

3) Feature selection;

4) Data scaling;

5) Fitting Models (LogisticRegression, DecisionTree, RandomForrest); Hyperparameter optimization; Scores evaluation

## Data set feature description

### Customer details:

- age;

- job;

- marital (relationship status);

- education (level of education);

- default (has got an expired credit);

- housing (has got a housing loan);

- loan (has got a personal loan);

- balance.

### Features related to the last contact:

- contact (contact type with a customer);

- month (month of the last contact);

- day (day of the last contact);

- duration (contact duration, seconds).

### Other features:

- campaign (quantity of contacts with a client durint the current campaign);

- pdays (quantity of days missed since the last marketing campaign till the contact in the current campaign);

- previous (quantity of contacts till the current campaign)

- poutcome (the result of the previous campaign).

### Target:

- deposit. Defines if a customer agrees to open a credit in a bank.

## Summary

    * Following classifiers were tested and compaired during the investigation: LogisticRegression, DecisionTree, RandomForest, GradientBoosting, Stacking

    * DecisionTree, RandomForest, GradientBoosting models provided comparable scores after hyperparameters optimization  

    * The best scores obtained with StackingClassifier after threshold optimizing: f1-score=0.83, accuracy=0.83

## Tools used

pandas==2.0.1

numpy==1.23.5

matplotlib==3.6.3

optuna==3.3.0

seaborn==0.12.2

scikit-learn==1.3.0