Update README.md

README.md

@@ -1,12 +1,134 @@
 ---
-license: mit
+license: cc-by-4.0
 task_categories:
-- text-classification
+- time-series-forecasting
+- classification
 language:
 - en
 tags:
-- code
-pretty_name: AI_battery_opti
+- battery
+- smartphone
+- energy
+- optimization
+pretty_name: AI Battery Optimizer Dataset
 size_categories:
 - n<1K
----
+---
+
+# Dataset Card for AI Battery Optimizer
+
+The **AI Battery Optimizer Dataset** contains **synthetic smartphone battery usage logs** created during the development of the **AI Battery Optimizer App**.  
+It is intended for research and experimentation on **battery prediction, app usage forecasting, and adaptive resource management**.
+
+---
+
+## Dataset Details
+
+### Dataset Description
+- **Curated by (Team):**  
+  - Aishwarya Singh  
+  - Lavanya Arora  
+  - Shreya Kathuria  
+  - Navya Jain  
+
+- **Funded by:** Self / Academic Project  
+- **Shared by:** Team NeuralBattery  
+- **Language(s):** English (column headers, labels)  
+- **License:** Creative Commons Attribution 4.0 (CC BY 4.0)  
+
+This dataset logs:
+- Battery percentage over time  
+- Power usage (mW)  
+- Estimated time remaining  
+- Predicted app usage with confidence score  
+- Screen brightness level  
+- Frame rate (FPS)  
+
+---
+
+### Dataset Sources
+- **Repository:** Hugging Face Dataset Repo – AI Battery Optimizer  
+- **Related Project:** [AI Battery Optimizer App](https://huggingface.co/)  
+
+---
+
+## Uses
+
+### Direct Use
+- Training **time-series models** (Chronos, TBATS, PatchTSMixer) for predicting battery drain  
+- Evaluating **ML-based app usage predictions**  
+- Research on **energy optimization in smartphones**  
+- Simulating **adaptive energy-saving systems**  
+
+### Out-of-Scope Use
+- Real-world personal battery health monitoring  
+- Any application requiring sensitive/private user data (dataset is **synthetic**)  
+
+---
+
+## Dataset Structure
+
+**Format:** CSV / JSON  
+
+**Fields:**
+- `timestamp` → Log time (UTC)  
+- `battery_percentage` → Battery level (%)  
+- `power_usage_mw` → Power consumption in milliwatts  
+- `time_remaining_min` → Estimated time left (minutes)  
+- `predicted_app` → Next app predicted (e.g. Instagram, YouTube)  
+- `confidence` → ML prediction confidence score (0–1)  
+- `brightness` → Screen brightness (%)  
+- `fps` → Frame rate setting  
+
+**Example Row:**
+
+| timestamp           | battery_percentage | power_usage_mw | time_remaining_min | predicted_app | confidence | brightness | fps |
+|---------------------|-------------------|----------------|---------------------|---------------|------------|------------|-----|
+| 2025-08-28 12:30:00 | 85                | 850            | 272                 | Instagram     | 0.87       | 75         | 60  |
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+Battery drain is influenced by **app usage, FPS, brightness, and background processes**.  
+This dataset was created to **simulate realistic smartphone usage patterns** for developing an **ML-driven energy optimization system**.
+
+### Source Data
+- Synthetic logs generated during **AI Battery Optimizer app simulations**  
+- Inspired by real smartphone usage, but fully anonymized  
+
+### Data Collection and Processing
+- Battery drain simulated every 30s via backend API  
+- App predictions generated every 15s with probabilistic ML logic  
+- Logs normalized into CSV format for training  
+
+---
+
+## Annotations
+- Predictions contain **confidence scores**  
+- Users can validate predictions inside the app (feedback loop)  
+- Dataset can be extended with these feedback labels  
+
+---
+
+## Personal and Sensitive Information
+- Dataset is **synthetic**  
+- No personal or sensitive user data included  
+
+---
+
+## Bias, Risks, and Limitations
+- Synthetic dataset may not capture **all real-world battery usage variability**  
+- Predictions are approximations, not exact reflections of real device usage  
+- Should be treated as a **benchmark/simulation dataset**  
+
+### Recommendations
+- Use this dataset for prototyping and model training  
+- Fine-tune with **real anonymized battery logs** for production apps  
+
+---
+
+## Citation
+
+**BibTeX:**