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Datasets:
Srikanth-Karthi
/
ecommerce-predictor-data

Tasks:
Tabular Regression
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
< 1K
Tags:
e-commerce
customer-analytics
spending-prediction
marketing
retail
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
license: apache-2.0
task_categories:
  - tabular-regression
  - tabular-classification
language:
  - en
tags:
  - e-commerce
  - customer-analytics
  - spending-prediction
  - marketing
  - retail
size_categories:
  - n<1K

E-commerce Customer Spending Dataset

Dataset License Rows

A dataset containing customer behavior metrics from an e-commerce platform, used to predict yearly spending.

Dataset Description

This dataset contains information about customers of an e-commerce company that sells clothing online and also has in-store style sessions. Customers can come to the store for personal styling sessions, then order clothes through a mobile app or website.

Features

Column Type Description
Email string Customer email address
Address string Customer address
Avatar string Avatar color chosen by customer
Avg. Session Length float Average in-store session length (minutes)
Time on App float Time spent on mobile app (minutes)
Time on Website float Time spent on website (minutes)
Length of Membership float Years of membership
Yearly Amount Spent float Total yearly spending (USD) - Target Variable

Dataset Statistics

Feature Mean Std Min Max
Avg. Session Length 33.05 0.99 29.53 36.14
Time on App 12.05 0.99 8.51 15.13
Time on Website 37.06 1.01 33.91 40.01
Length of Membership 3.53 1.00 0.27 6.92
Yearly Amount Spent 499.31 79.31 256.67 765.52

Quick Start 

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Srikanth-Karthi/ecommerce-predictor-data")

# View first few rows
print(dataset["train"][0])

Or with Pandas:

import pandas as pd
from huggingface_hub import hf_hub_download

# Download and load
path = hf_hub_download(
    repo_id="Srikanth-Karthi/ecommerce-predictor-data",
    filename="Ecommerce.csv",
    repo_type="dataset"
)
df = pd.read_csv(path)
print(df.head())

Use Cases

  • Regression: Predict yearly customer spending
  • Customer Segmentation: Cluster customers by behavior
  • Feature Analysis: Understand what drives spending
  • Marketing Optimization: Target high-value customers

Key Insights

  1. Length of Membership has the strongest correlation with yearly spending
  2. Time on App shows higher correlation than Time on Website
  3. Suggests focusing on mobile app development over website improvements

Associated Model

This dataset was used to train:

Demo

Try the live prediction demo:

Citation 

@misc{ecommerce-spending-dataset,
  author = {Srikanth-Karthi},
  title = {E-commerce Customer Spending Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data}
}