Update README.md

README.md

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   - split: test
     path: data/test-*
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+# Lead Scoring Dataset
+
+## Overview
+
+This dataset contains lead scoring data for X Education, a company that provides online courses. The dataset is designed for binary classification to predict whether a lead will convert to a customer using an LLM.
+
+- **Source**: [Kaggle - Lead Scoring Dataset](https://www.kaggle.com/datasets/amritachatterjee09/lead-scoring-dataset)
+- **Target Variable**: `Converted` (0 = Not Converted, 1 = Converted)
+
+## Features
+
+The processed dataset includes the following 7 key features:
+
+1. **Prospect ID** - Unique identifier for each lead
+2. **Lead Origin** - How the lead was generated (API, Landing Page Submission, etc.)
+3. **Lead Source** - Specific source of the lead (Google, Direct Traffic, Organic Search, etc.)
+4. **Last Activity** - Most recent interaction (Email Opened, Page Visited, etc.)
+5. **Tags** - Lead categorization tags (Ringing, Will revert after reading email, etc.)
+6. **What is your current occupation** - Lead's current job status (Student, Unemployed, etc.)
+7. **Converted** - Target variable indicating conversion (0/1)
+
+## Usage
+
+The processed dataset is available on Hugging Face Hub at: `shawhin/lead-scoring-x`
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("shawhin/lead-scoring-x")
+
+# Access train, validation, and test splits
+train_data = dataset['train']
+valid_data = dataset['valid'] 
+test_data = dataset['test']
+```
+
+## License
+
+Please refer to the [original Kaggle dataset](https://www.kaggle.com/datasets/amritachatterjee09/lead-scoring-dataset) license for usage terms.