README.md

---
title: harpertokenSysMon Dataset
tags:
- system-monitoring
- time-series
- anomaly-detection
- predictive-maintenance
- macOS
license: mit
language:
- en
---

# harpertokenSysMon Dataset

## Dataset Summary
This open-source dataset captures real-time system metrics from macOS for time-series analysis, anomaly detection, and predictive maintenance.

## Dataset Features
- OS Compatibility: macOS  
- Data Collection Interval: 1-5 seconds  
- Total Storage Limit: 4GB  
- File Format: CSV & Parquet  
- Data Fields:  
  - `timestamp`: Date and time of capture  
  - `cpu_usage`: CPU usage percentage per core  
  - `memory_used_mb`: RAM usage in MB  
  - `disk_read_mb`: Disk read in MB  
  - `disk_write_mb`: Disk write in MB  
  - `net_sent_mb`: Network upload in MB  
  - `net_recv_mb`: Network download in MB  
  - `battery_status`: Battery percentage (Mac only)  
  - `cpu_temp`: CPU temperature in °C  

## Usage Examples
### 1. Load in Python
```python
from datasets import load_dataset

dataset = load_dataset("harpertoken/harpertokenSysMon")
df = dataset["train"].to_pandas()

print(df.head())
```

### 2. Train an Anomaly Detection Model
```python
from sklearn.ensemble import IsolationForest

# Convert time-series to numerical format
df["cpu_usage_avg"] = df["cpu_usage"].apply(lambda x: sum(x) / len(x))

# Train model
model = IsolationForest(contamination=0.05)
model.fit(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])

# Predict anomalies
df["anomaly"] = model.predict(df[["cpu_usage_avg", "memory_used_mb", "disk_read_mb", "disk_write_mb"]])
```

## Potential Use Cases
AI Fine-Tuning for real-time system monitoring models  
Time-Series Forecasting of CPU & memory usage  
Anomaly Detection for overheating and system failures  
Predictive Maintenance for proactive issue detection  

## Licensing
- License: MIT

## Contact
For questions or feedback, please contact [email protected]