https://github.com/aman5319/bank-marketing-analysis

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
https://github.com/aman5319/bank-marketing-analysis

bank-marketing-analysis desiciontree imbalanced-data logistic-regression roc-auc sklearn smote

Last synced: about 1 month ago
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The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).

• Host: GitHub
• URL: https://github.com/aman5319/bank-marketing-analysis
• Owner: aman5319
• Created: 2019-03-08T12:39:04.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-03-08T13:01:32.000Z (about 6 years ago)
• Last Synced: 2025-02-03T13:12:50.238Z (3 months ago)
• Topics: bank-marketing-analysis, desiciontree, imbalanced-data, logistic-regression, roc-auc, sklearn, smote
• Language: Jupyter Notebook
• Size: 287 KB
• Stars: 1
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bank-Marketing-Analysis

## Objective

1. Bank Marketing dataset is collected from direct marketing campaign of a bank institution from Portuguese.

2. Marketing campaign can be understood as phone calls to the clients to  convince them accept to make a term deposit with their bank.

3. After each call, they are being noted as to no - being the client did  not make a deposit and yes - being the client on call accepted to make a  deposit.

4. The purpose of this project is to predict if the client on call would  accept to make a term deposit or not based on the information of the  clients.

5. For More Information refer https://archive.ics.uci.edu/ml/datasets/Bank+Marketing

## Main  Issues with the dataset

1. There is data imbalance between two classes The number of yes(1) is very low in comparison to no(0)

2. Missing Value in the dataset.

## Techniques Used

1. Visualizing the data and filling missing value of each column with DecisionTreeClassifier

2. To deal with data imbalance we use SMOTE - Synthetic Minority

   Over-sampling Technique. 

   * SMOTE creates synthetic (not duplicate) samples of the minority class. Hence 

     making the minority class equal to the majority class. SMOTE does this 

     by selecting similar records and altering that record one column at a 

     time by a random amount within the difference to the neighbouring 

     records.

3. Use Logistic regression for training

## Result

![](roc.png)

### AUC = 0.931

class|precision|recall|f1-score

-----------|-----------|--------|--------

0|0.98|0.85|0.91

1|0.44|0.89|0.59