The marketing campaign dataset has been successfully loaded and contains 2,240 rows and 29 columns. The columns include various demographic and purchasing behavior attributes such as ID, Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds, NumDealsPurchases, NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, AcceptedCmp1, AcceptedCmp2, Complain, Z_CostContact, Z_Revenue, and Response.

The dataset has been successfully loaded and parsed. We have performed initial exploratory data analysis (EDA) including summary statistics and visualizations of the distribution of income and year of birth. The income distribution shows a right-skewed pattern, while the year of birth distribution is relatively uniform with a slight concentration around the 1970s.

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We have continued the analysis by visualizing the distribution of education levels and marital status among the customers. The education distribution shows a higher number of customers with graduation and PhD levels, while the marital status distribution indicates a higher number of married and together statuses.

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We have continued the analysis by visualizing the average income by education level and marital status. The average income by education level shows that customers with a PhD have the highest average income, followed by those with a Master's degree. The average income by marital status indicates that customers who are married or together have higher average incomes compared to other statuses.

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We have continued the analysis by visualizing the total amount spent on different product categories. The plot shows that customers spend the most on wines, followed by meat products, and the least on fruits and sweets.

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