This project aims to perform Exploratory Data Analysis (EDA) on the Banking Dataset for Marketing Targets from Kaggle. The goal is to uncover patterns, identify significant variables, and provide insights that can help in developing targeted marketing strategies.
The dataset can be found here. It includes various attributes related to customers' banking details and the marketing campaigns they were subjected to.
The dataset contains the following columns:
age: Age of the customer job: Job type of the customer marital: Marital status of the customer education: Education level of the customer default: Indicates if the customer has credit in default (binary: "yes","no") balance: Account balance housing: Indicates if the customer has a housing loan (binary: "yes","no") loan: Indicates if the customer has a personal loan (binary: "yes","no") contact: Contact communication type day: Last contact day of the month month: Last contact month of the year duration: Last contact duration, in seconds campaign: Number of contacts performed during this campaign and for this client pdays: Number of days that passed by after the client was last contacted from a previous campaign previous: Number of contacts performed before this campaign and for this client poutcome: Outcome of the previous marketing campaign y: Has the client subscribed a term deposit? (binary: "yes","no")
To run the EDA, you need the following libraries installed:
pandas
numpy
matplotlib
seaborn
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/yourusername/customer-segmentation.git- Navigate to the project directory:
cd customer-segmentation- Create a virtual environment:
python3 -m venv venv- Activate the virtual environment: On Windows:
venv\Scripts\activateOn MacOS/Linux:
source venv/bin/activate- Install the dependencies:
pip install -r requirements.txtThis project is licensed under the MIT License. See the LICENSE file for details.