Description: This project involves analyzing the data from a previous personal loan campaign conducted by HBFC bank to understand the profile of potential loan customers. The bank aims to increase its customer base of asset/loan customers by targeting potential clients more effectively. The dataset provided includes demographic information, customer relationship with the bank, and response to the last personal loan campaign for 5000 customers.
Determine the percentage of customers who availed personal loans compared to those who did not. Calculate descriptive statistics (min, max, median, average) for numeric variables. Create a new categorical variable for experience and plot a bar graph. Visualize the relationship between age and experience using a scatter plot. Identify the top three ZIP codes where the bank's customers are located. Determine the number of customers with fixed deposits and credit cards but not personal loans. Compare the median income of customers who availed personal loans with those who did not. Create pivot tables to summarize data by percentage values for different categorical variables. Analyze the pivot tables to identify important categorical variables for further study. Provide recommendations to optimize the cost of future marketing campaigns based on the analysis. This analysis aims to assist HBFC bank in targeting potential customers more effectively for their personal loan offers, ultimately optimizing their marketing campaign strategy.
