The Shopping Trends Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience.
This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more.
Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences.
Here's a breakdown of the variables in the Shopping Trends Dataset:
📌 Customer ID - Unique identifier for each customer.
📌 Age - Age of the customer.
📌 Gender - Gender of the customer (Male/Female)
📌 Item Purchased - The item purchased by the customer
📌 Category - Category of the item purchased
📌 Purchase Amount (USD) - The amount of the purchase in USD
📌 Location - Location where the purchase was made
📌 Size - Size of the purchased item
📌 Color - Color of the purchased item
📌 Season - Season during which the purchase was made
📌 Review Rating - Rating given by the customer for the purchased item
📌 Payment Method - Customer's most preferred payment method
📌 Shipping Type - Type of shipping chosen by the customer
📌 Previous Purchases - Number of previous purchases made by the customer
📌 Preferred Payment Method - Method which was used to pay for the purchase
📌 Frequency of Purchases - Frequency at which the customer makes purchases
📌 Customer Segmentation - The official label of a customer
📌 Seasonal Trends - The purpose of purchase
📌 Delivery Time - Duration it takes for a products to be delivered
📌 Number of Items Purchased - The amount of items purchased by a customer
- Pandas
- Streamlit
- Plotly express
- Folium