This project focuses on exploratory data analysis (EDA) of bank customer data to understand customer behavior and analyze the effectiveness of marketing campaigns. The goal is to identify key factors that influence customer subscription decisions.
- Analyze bank customer data to discover patterns and trends
- Understand factors affecting customer subscription (
y) - Evaluate campaign effectiveness using visualizations
- Gain insights to support data-driven decision making
The dataset contains information related to bank customers and marketing campaigns, including:
- Customer demographics (job, education, marital status)
- Campaign-related attributes (contact type, duration, campaign count)
- Previous campaign outcomes
- Subscription status (target variable)
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
- VS Code
- Data loading and preprocessing
- Exploratory Data Analysis (EDA)
- Subscription analysis by job, education, and marital status
- Campaign analysis using duration and contact frequency
- Previous campaign outcome analysis
- Month-wise and day-wise subscription trends
- Visualization using bar plots and box plots
- Longer call duration increases the probability of subscription
- Certain job and education groups show higher subscription rates
- Excessive campaign contacts may reduce effectiveness
- Positive outcomes in previous campaigns improve future responses
- Subscription behavior varies by month and day of the week
bank-data-analysis/ │ ├── bank_data_analysis.ipynb │ → Jupyter Notebook containing full analysis and visualizations │ ├── Bank.csv │ → Dataset used for analysis │ ├── bank_data_analysis_report.pdf │ → Detailed project report │ └── README.md → Project documentation