Bank-Marketing

This repository contains a Python script that analyzes and models the bank.csv dataset. The script uses pandas, seaborn, matplotlib, scikit-learn libraries to perform data preprocessing, feature engineering, and model selection. Finally it will predict giving loan or not to a customer.

Dependencies

• pandas
• seaborn
• matplotlib
• scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/Nishef/bank-marketing.git
   

Usage

1. Load the dataset by running the main.py script:

   python main.py
   

2. The script will output the summary statistics of numerical variables, create a correlation matrix using seaborn's heatmap function, check for duplicated values, encode categorical columns, split the data into train and test sets, and create two machine learning models: a logistic regression model and a random forest classifier model.

3. The script will output the classification report and confusion matrix for each model.

License

This project is licensed under the MIT License - see the LICENSE file for details.