---
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](https://img.shields.io/badge/Dataset-Ecommerce-green)](https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://www.apache.org/licenses/LICENSE-2.0)
[![Rows](https://img.shields.io/badge/Rows-500-blue)](https://huggingface.co/datasets/Srikanth-Karthi/ecommerce-predictor-data)

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

```python
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:**

```python
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:
- [Srikanth-Karthi/ecommerce-spending-predictor](https://huggingface.co/Srikanth-Karthi/ecommerce-spending-predictor)

## Demo

Try the live prediction demo:
- [Srikanth-Karthi/ecommerce-predictor-demo](https://huggingface.co/spaces/Srikanth-Karthi/ecommerce-predictor-demo)

## Citation

```bibtex
@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}
}
```