Feature Selection for Banking Dataset

This project aims to utilize Genetic Algorithms (GA) to perform feature selection for a bank dataset to enhance the efficiency of predictive models.

Prerequisites

For this analysis, you need the following Python libraries:

• pandas
• numpy
• sklearn
• lightgbm
• genetic_selection
• matplotlib
• itertools
• DataTable
• warnings

Data Preprocessing

The bank dataset bank-full.csv is loaded and preprocessed. This involves:

• Reading the data into a pandas DataFrame.
• Factorizing categorical data to convert them into numeric form.
• Splitting the data into training and testing datasets.

Decision Tree

A decision tree classifier is utilized as a baseline model for comparison.

Genetic Algorithm (GA) for Feature Selection

A Genetic Algorithm approach is utilized to perform feature selection:

• Multiple configurations of the GA parameters such as population size, crossover rate, and mutation rate are tested.
• The best features are selected based on the highest accuracy achieved using cross-validation.
• The results for each configuration are stored, and the best set of features is identified.

Results Visualization

Various plots are generated:

• A plot to visualize the best fitness value of the GA across generations for different configurations.
• Error plots to understand the standard error for various configurations of the GA.

Output

The results are stored in a CSV file named GA_report.csv.

Acknowledgment

• Dataset: Moro, Sérgio & Cortez, Paulo & Laureano, Raul. (2012). Enhancing Bank Direct Marketing through Data Mining. CAlg European Marketing Academy. Available at: https://repositorium.sdum.uminho.pt/handle/1822/21409
• GA Package: Manuel Calzolari. (2021, April 3). manuel-calzolari/sklearn-genetic: sklearn-genetic 0.4.1 (Version 0.4.1). Zenodo. http://doi.org/10.5281/zenodo.4661248