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Unravel the correlations among customers' personal background, shopping behaviors and spending trends, as well as provide insights into the optimal marketing strategies for customers divided into different segments.
Tasks:
Data cleaning - Categorize data columns, handle missing data, encode categorical data, scale/normalize numerical data and parse datetime data; Feature engineering - Tranform, combine and/or reduce original feature columns; Exploratory Data Analysis (EDA) - Visualize correlations among features; Clustering/segmentation: Use K-means clustering to divide customers into groups for marketing purposes.