Update README.md

README.md

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 ---
 license: cc-by-4.0
 ---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series
+language:
+- en
+tags:
+- railway
+- predictive-maintenance
+- failure-detection
+- transportation
+- iot
+- machine-learning
+- synthetic-data
+- analytics
+pretty_name: Indian Railway Failure Detection & Maintenance (100K)
+size_categories:
+- 100K<n<1M
+---
+
+# 🚆 Indian Railway Failure Detection & Maintenance (100K)
+
+## Overview
+
+This dataset contains **100,000 synthetic yet realistic railway maintenance records** designed for predictive maintenance, failure detection, and transportation analytics. The data simulates real-world railway operations through equipment wear, maintenance history, environmental conditions, operational metrics, and IoT-inspired sensor readings.
+
+Version 2 incorporates more realistic feature relationships, seasonal weather patterns, equipment aging effects, structured missing values, and sensor outliers to better reflect operational railway environments.
+
+## Features
+
+The dataset includes information related to:
+
+- 🚄 Train operations
+- ⚙️ Equipment health indicators
+- 📡 IoT sensor measurements
+- 🌦️ Weather and environmental conditions
+- 🛤️ Track health metrics
+- 🔋 Electrical system indicators
+- 🔧 Maintenance history
+- ⚠️ Failure types and severity levels
+- 📊 Inspection and risk scores
+
+## Target Variables
+
+### Maintenance Required
+Binary target:
+
+- 0 = No Maintenance Required
+- 1 = Maintenance Required
+
+### Failure Type
+
+Possible values:
+
+- None
+- Brake Failure
+- Wheel Defect
+- Track Defect
+- Signal Failure
+- Bearing Failure
+
+### Failure Severity
+
+Possible values:
+
+- None
+- Low
+- Medium
+- High
+- Critical
+
+## Machine Learning Applications
+
+This dataset can be used for:
+
+- Predictive Maintenance
+- Failure Detection
+- Multiclass Classification
+- Risk Assessment
+- Anomaly Detection
+- Data Imputation
+- Regression Tasks
+- Feature Engineering
+- Explainable AI (XAI)
+
+## Data Characteristics
+
+- 📈 Records: 100,000
+- 📊 Features: 25+
+- 🧩 Realistic Missing Values
+- 📡 Sensor Outliers Included
+- 🌦️ Seasonal Weather Effects
+- ⚙️ Correlated Equipment Wear
+- 🚄 Multiple Train Categories
+- 🎯 Multiple Prediction Targets
+
+## Synthetic Data Notice
+
+This dataset is **synthetically generated by the author** and does not contain real railway operational records.
+
+Although synthetic, realistic relationships, operational patterns, maintenance logic, missing values, and failure mechanisms have been incorporated to emulate real-world predictive maintenance scenarios.
+
+## Example Use Cases
+
+- Railway Failure Prediction
+- Predictive Maintenance Systems
+- Industrial IoT Analytics
+- Transportation Analytics
+- Educational Projects
+- Data Science Portfolios
+- Kaggle Competitions & Notebooks
+
+## License
+
+This dataset is released under the **CC BY 4.0 License**.
+
+Users are free to share and adapt the dataset with appropriate attribution.
+
+## Citation
+
+If you use this dataset in research, projects, notebooks, or publications, please provide attribution to the dataset author.
+
+---
+
+🚆 Built for machine learning, analytics, predictive maintenance, and transportation research.
+---