Update README.md

README.md

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-# Codium Windurf System Monitoring Dataset
-
-## Overview
-This dataset contains system monitoring metrics collected from macOS systems. The data is collected at 1-second intervals and includes various system metrics useful for AI fine-tuning, time-series forecasting, anomaly detection, and predictive maintenance.
-
-## Dataset Details
-- **Dataset Name**: Codium Windurf System Monitoring Dataset
-- **Storage Limit**: 4GB
-- **Data Formats**: CSV & Parquet
-- **Collection Interval**: 1 second
-- **Programming Language**: Python (macOS)
-
-## Metrics Collected
-- `timestamp`: Date and time of collection
-- `cpu_usage`: CPU usage per core (%)
-- `memory_used_mb`: RAM usage (MB)
-- `disk_read_mb`: Disk read (MB)
-- `disk_write_mb`: Disk write (MB)
-- `net_sent_mb`: Network upload (MB)
-- `net_recv_mb`: Network download (MB)
-- `battery_status`: Battery percentage
-- `cpu_temp`: CPU temperature (°C)
-
-## Target Use Cases
-- AI Fine-Tuning (System Monitoring Models)
-- Time-Series Forecasting (CPU & Memory Usage)
-- Anomaly Detection (Overheating & Failures)
-- Predictive Maintenance (System Optimization)
-
-## Dataset Structure
-The dataset is available in two formats:
-1. `system_monitoring_dataset.csv`
-2. `system_monitoring_dataset.parquet`
-
-## Usage
-To use this dataset, simply download the files and load them using your preferred data analysis tools. The Parquet format is recommended for efficient storage and processing.
-
-## License
-MIT License
+---
+title: Codium Windurf System Monitoring Dataset
+tags:
+- system-monitoring
+- time-series
+- anomaly-detection
+- predictive-maintenance
+- macOS
+license: mit
+language:
+- en
+---
+
+# Codium Windurf System Monitoring Dataset (100% Open-Source)
+
+## 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.
+
+## 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
+### **1️⃣ Load in Python**
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("bniladridas/codium-windurf-system-monitoring")
+df = dataset["train"].to_pandas()
+
+print(df.head())
+```
+
+### **2️⃣ 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 & 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")
+```
 
 ## Contact
-For questions or feedback, please contact bniladridas@gmail.com
+For questions or feedback, please contact bniladridas@gmail.com