init,llm gateway & import_analyse

demo/e-commerce-orders_desc.md

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+E-commerce Customer Order Behavior Dataset
+A synthetic e-commerce dataset containing 10,000 orders with realistic customer behavior patterns, suitable for e-commerce analytics and machine learning tasks.
+
+Dataset Card for E-commerce Orders
+Dataset Summary
+This dataset simulates customer order behavior in an e-commerce platform, containing detailed information about orders, customers, products, and delivery patterns. The data is synthetically generated with realistic distributions and patterns.
+
+Supported Tasks
+regression: Predict order quantities or prices
+classification: Predict delivery status or customer segments
+clustering: Identify customer behavior patterns
+time-series-forecasting: Analyze order patterns over time
+Languages
+Not applicable (tabular data)
+
+Dataset Structure
+Data Instances
+Each instance represents a single e-commerce order with the following fields:
+
+{
+    'order_id': '5ea92c47-c5b2-4bdd-8a50-d77efd77ec89',
+    'customer_id': 2350,
+    'product_id': 995,
+    'category': 'Electronics',
+    'price': 403.17,
+    'quantity': 3,
+    'order_date': '2024-04-20 14:59:58.897063',
+    'shipping_date': '2024-04-22 14:59:58.897063',
+    'delivery_status': 'Delivered',
+    'payment_method': 'PayPal',
+    'device_type': 'Mobile',
+    'channel': 'Paid Search',
+    'shipping_address': '72166 Cunningham Crescent East Nicholasside Mississippi 85568',
+    'billing_address': '38199 Edwin Plain Johnborough Maine 81826',
+    'customer_segment': 'Returning'
+}
+
+Data Fields
+Field Name	Type	Description	Value Range
+order_id	string	Unique order identifier (UUID4)	-
+customer_id	int	Customer identifier	1-3,000
+product_id	int	Product identifier	1-1,000
+category	string	Product category	Electronics, Clothing, Home, Books, Beauty, Toys
+price	float	Product price	$5.00-$500.00
+quantity	int	Order quantity	1-10
+order_date	datetime	Order placement timestamp	Last 12 months
+shipping_date	datetime	Shipping timestamp	1-7 days after order_date
+delivery_status	string	Delivery status	Pending, Shipped, Delivered, Returned
+payment_method	string	Payment method used	Credit Card, PayPal, Debit Card, Apple Pay, Google Pay
+device_type	string	Ordering device	Desktop, Mobile, Tablet
+channel	string	Marketing channel	Organic, Paid Search, Email, Social
+shipping_address	string	Delivery address	Street, City, State, ZIP
+billing_address	string	Billing address	Street, City, State, ZIP
+customer_segment	string	Customer type	New, Returning, VIP
+Data Splits
+This dataset is provided as a single CSV file without splits.
+
+Dataset Creation
+Source Data
+This is a synthetic dataset generated using Python with pandas, numpy, and Faker libraries. The data generation process ensures:
+
+Realistic customer behavior patterns
+Proper data distributions
+Valid relationships between fields
+Realistic address formatting
+Annotations
+No manual annotations (synthetic data)
+
+Considerations for Using the Data
+Social Impact of Dataset
+This dataset is designed for:
+
+Development of e-commerce analytics systems
+Testing of order processing systems
+Training of machine learning models for e-commerce
+Educational purposes in data science
+Discussion of Biases
+As a synthetic dataset, care has been taken to:
+
+Use realistic distributions for order patterns
+Maintain proper relationships between dates
+Create realistic customer segments
+Avoid demographic biases in address generation
+However, users should note that:
+
+The data patterns are simplified compared to real e-commerce data
+The customer behavior patterns are based on general assumptions
+Geographic distribution might not reflect real-world patterns
+Dataset Statistics
+Total Records: 10,000
+
+Distribution Statistics:
+
+Delivery Status:
+
+Delivered: 70%
+Shipped: 20%
+Pending: 5%
+Returned: 5%
+Customer Segments:
+
+VIP: ~15%
+Returning: ~35%
+New: ~50%
+Loading and Usage
+Using Huggingface Datasets:
+
+from datasets import load_dataset
+
+dataset = load_dataset("path/to/e-commerce-orders")
+
+# Example: Load as pandas DataFrame
+df = dataset['train'].to_pandas()
+
+# Example: Access specific columns
+orders = dataset['train']['order_id']
+prices = dataset['train']['price']
+
+Data Quality
+The dataset has been validated to ensure:
+
+No missing values
+Proper value ranges
+Valid categorical values
+Proper date relationships
+Unique order IDs
+Valid address formats