This project explores the Bank Marketing dataset obtained from the UCI Machine Learning Repository. The dataset contains information related to direct marketing campaigns of a Portuguese banking institution. The primary objective of this project is to analyze the data and develop predictive models to identify potential customers who are likely to subscribe to term deposits, aiding the bank's marketing strategies.
The Bank Marketing dataset consists of both numerical and categorical features, including demographic information, past marketing campaign details, economic indicators, and client behaviors. The target variable indicates whether the client subscribed to a term deposit (binary classification: yes/no).
-Dataset Source: UCI Machine Learning Repository - Bank Marketing Dataset -Number of Instances: 41,188 -Number of Attributes: 20 (including the target variable) -Attribute Information: Detailed attribute descriptions are available on the dataset webpage. -Project Structure
*data/: Directory containing the dataset files.
*notebooks/: Directory containing Jupyter notebooks used for data exploration, preprocessing, modeling, and evaluation.
*models/: Directory to store trained machine learning models.
*reports/: Directory to save project reports, such as model evaluation results, visualizations, etc.
*README.md: Main project documentation file (the one you are currently reading).
To replicate this project, follow these steps:
Clone this repository: git clone https://github.com/aajinr/bank_marketing.git Navigate to the project directory: cd bank-marketing-dataset-project Set up your Python environment with the required dependencies listed in requirements.txt: pip install -r requirements.txt Explore the dataset and notebooks provided in the notebooks/ directory.