Final Project JCDS 09 - Priscilla Widjaja

Deposit Term Subscription Prediction Machine Learning

A prediction machine learning project using classification algorithm that predicts whether the client will subscribe a term deposit or not.

Datasets

http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#

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).

Cleaning and Pre-Processing

1. Drop rows containing 'unknown' values and age outliers.
2. Encode string datas to numerical.
3. Scale datas using RobustScaler.

Features Selection

1. Drop columns that has strong correlations with each other.

2. Choosing best 10 features to use.

Handling Imbalance Target

y	Data (%)
No	87.75
Yes	12.25

The target was imbalance. So SMOTE Oversampling technique was used to balance the target.

Modelling & Hyperparameter Tuning

Using GridSearchCV to choose the best parameters.

F1 Score Before & After Hyperparameter Tuning

	Before	After
Logistic Regression	41.909023	40.391335
Decision Tree	32.446134	46.787879
Random Forest	40.638607	42.477876
KNN Classifier	37.958533	46.511628

Based on F1 Score, the best model is Decision Tree Classifier.

Confusion Matrix Decision Tree Classifier

Before Hyperparameter Tuning

After Hyperparameter Tuning

Conclusions:

• This machine learning helps company to reduce telemarketing costs by eliminating customers who aren't going to subscribe.
• The results can be improved by having more datas of bank clients who susbcribed.

Business Insights:

• The company should create marketing campaign targeted to bank clients who are older than 38 years old.
• The company needs to create products which will attract bank clients who are self-employed, unemployed, entrepreneurs dan housemaids.

Dashboard

Home

Dataset

Business Insight

Prediction

Result

Thank you for reading.