Data analysis customer shopping preferences using python

Data Loading: The dataset is loaded using pandas

Data Inspection: Inspect the dataset's shape, columns, and information (data types, non-null counts).

Separate categorical and numerical features into distinct lists.

Data Cleaning: Identify and handle missing values and duplicate entries.

Descriptive Statistics:Calculate basic statistics, such as average age, most common items, and total purchase amounts by category.

Exploratory Analysis: Explores individual features (e.g., gender, category, location) using value counts, bar plots, and pie charts. It also generates a word cloud for location. for nummerical feature explore the distributions.

In-depth Analysis:It answers specific questions about the data, such as the average review rating for male and female customers, the most common payment method, and the average purchase amount for customers with subscriptions.

Correlation Analysis:** Calculate the correlation coefficient (e.g., Pearson) between age and previous purchases.