Update dataset card.

README.md

@@ -1,3 +1,75 @@
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-license: mit
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+# Dataset Card: Anomaly Detection Metrics Data
+
+## Dataset Summary
+
+This dataset contains system performance metrics collected over time for anomaly detection in time series data. It includes multiple system metrics such as CPU load, memory usage, and other resource utilization statistics, along with timestamps and additional attributes.
+
+## Dataset Details
+
+- **Size:** \~7.3 MB (raw JSON), 345 kB (auto-converted Parquet)
+- **Rows:** 46,669
+- **Format:** JSON
+- **Libraries:** `datasets`, `pandas`, `croissant`
+- **License:** MIT
+
+## Features
+
+| Feature       | Type    | Description                                                                           |
+| ------------- | ------- | ------------------------------------------------------------------------------------- |
+| `metric_name` | string  | Name of the system metric (e.g., `system.cpu.load_average.1m`, `system.memory.usage`) |
+| `timestamp`   | string  | Timestamp of the recorded metric in ISO format                                        |
+| `value`       | float64 | Recorded value of the metric                                                          |
+| `attributes`  | dict    | Additional metadata (e.g., device, state, direction)                                  |
+
+## Usage Example
+
+### Load Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ShreyasP123/anomaly_detection_metrics_data")
+print(dataset["train"][0])  # View first record
+```
+
+### Convert to Pandas DataFrame
+
+```python
+import pandas as pd
+
+df = pd.DataFrame(dataset["train"])
+print(df.head())
+```
+
+## Applications
+
+- Anomaly detection in cloud and edge computing environments
+- Predictive maintenance based on system performance
+- Cybersecurity monitoring for unusual activity
+- Resource optimization in distributed systems
+
+## Limitations
+
+- Requires domain expertise for correct anomaly labeling
+- May not generalize well to all system configurations without retraining
+- Timestamp granularity may impact detection accuracy
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@dataset{shreyasP123_anomaly_detection_metrics_data,
+  author = {ShreyasP123},
+  title = {Anomaly Detection Metrics Data},
+  year = {2025},
+  url = {https://huggingface.co/datasets/ShreyasP123/anomaly_detection_metrics_data}
+}
+```
+
+## Maintainer
+
+- **ShreyasP123**
+
+### If you are having any queries then feel free to contact me .  :
+)