Update README: rebrand to harpertokenSysMon, remove noise and emojis, add contact email

README.md

@@ -1,5 +1,5 @@
 ---
-title: Codium Windurf System Monitoring Dataset
+title: harpertokenSysMon Dataset
 tags:
 - system-monitoring
 - time-series
@@ -11,17 +11,17 @@ language:
 - en
 ---
 
-# Codium Windurf System Monitoring Dataset (100% Open-Source)
+# harpertokenSysMon Dataset
 
 ## Dataset Summary
-The **Codium Windurf System Monitoring Dataset** is a **100% open-source dataset** designed for **time-series analysis, anomaly detection, and system performance optimization**. It captures **real-time system metrics** from macOS, providing a structured collection of CPU, memory, disk, network, and thermal sensor data. The dataset is ideal for **fine-tuning AI models** for predictive maintenance, anomaly detection, and system load forecasting.
+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:**  
+- 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  
@@ -33,17 +33,17 @@ The **Codium Windurf System Monitoring Dataset** is a **100% open-source dataset
   - `cpu_temp`: CPU temperature in °C  
 
 ## Usage Examples
-### **1️⃣ Load in Python**
+### 1. Load in Python
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("bniladridas/codium-windurf-system-monitoring")
+dataset = load_dataset("harpertoken/harpertokenSysMon")
 df = dataset["train"].to_pandas()
 
 print(df.head())
 ```
 
-### **2️⃣ Train an Anomaly Detection Model**
+### 2. Train an Anomaly Detection Model
 ```python
 from sklearn.ensemble import IsolationForest
 
@@ -59,34 +59,13 @@ df["anomaly"] = model.predict(df[["cpu_usage_avg", "memory_used_mb", "disk_read_
 ```
 
 ## 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  
+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 & Contributions
-- **License:** MIT (100% Open-Source & Free)  
-- **Contributions:** PRs are welcome! Open an issue for improvements.  
-
-## How to Upload to Hugging Face
-### **1️⃣ Install Hugging Face CLI**
-```bash
-pip install huggingface_hub
-huggingface-cli login
-```
-
-### **2️⃣ Push the Dataset**
-```python
-from datasets import Dataset
-import pandas as pd
-
-# Load the dataset
-df = pd.read_csv("system_monitoring_dataset.csv")
-dataset = Dataset.from_pandas(df)
-
-# Upload to Hugging Face
-dataset.push_to_hub("bniladridas/codium-windurf-system-monitoring")
-```
+## Licensing
+- License: MIT
 
 ## Contact
-For questions or feedback, please contact bniladridas@gmail.com
+For questions or feedback, please contact harpertoken@icloud.com