FUTURE_DS_03 is a data analytics project completed as part of the Future Interns Data Science & Analytics Internship.
The project focuses on analyzing marketing funnel performance and customer conversion behavior using the UCI Bank Marketing Campaign Dataset. The analysis explores how customers progress through the marketing funnel, identifies potential drop-off points, evaluates campaign and communication channels, and generates data-driven insights that can support marketing decision-making.
The project demonstrates practical skills in data cleaning, exploratory data analysis (EDA), KPI analysis, data visualization, funnel analysis, and business analytics.
The main objectives of this project are to:
- Analyze customer behavior throughout the marketing funnel.
- Measure conversion and response rates.
- Identify major funnel drop-off points.
- Evaluate the performance of different marketing channels.
- Explore customer characteristics associated with successful conversions.
- Identify patterns and trends within the campaign data.
- Generate actionable insights for improving marketing performance.
The project uses the UCI Bank Marketing Dataset, which contains information from direct marketing campaigns conducted by a Portuguese banking institution.
The dataset includes customer and campaign-related variables such as:
- Age
- Job
- Marital status
- Education
- Account balance
- Housing loan status
- Personal loan status
- Contact method
- Campaign duration
- Number of contacts
- Previous campaign outcome
- Campaign response
The target outcome is whether the customer subscribed to the offered term deposit.
The project was developed using:
- Python
- Jupyter Notebook
- Pandas – Data manipulation and analysis
- NumPy – Numerical computing
- Matplotlib – Data visualization
- Seaborn – Statistical visualization
- Jupyter – Interactive analysis environment
- Git & GitHub – Version control and project documentation
The dataset was inspected and prepared for analysis by:
- Checking dataset structure
- Identifying missing values
- Examining duplicate records
- Handling categorical variables
- Checking data types
- Preparing variables for analy