Skip to content

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).

Notifications You must be signed in to change notification settings

aman5319/Bank-Marketing-Analysis

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 

Repository files navigation

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

AUC = 0.931

class precision recall f1-score
0 0.98 0.85 0.91
1 0.44 0.89 0.59

About

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).

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published