Add Hugging Face dataset card

README.md

@@ -1,3 +1,180 @@
----
-license: mit
----
+---
+license: mit
+pretty_name: Home Monitoring System
+task_categories:
+- tabular-classification
+language:
+- en
+tags:
+- smart-home
+- home-monitoring
+- iot
+- sensor-data
+- time-series
+- tabular
+- anomaly-detection
+- activity-monitoring
+- energy-monitoring
+- ambient-assisted-living
+size_categories:
+- 1K<n<10K
+---
+
+# Home Monitoring System
+
+## Dataset Summary
+
+**Home Monitoring System** is a tabular smart-home sensor dataset for research and prototyping in home monitoring, Internet of Things (IoT), activity-aware systems, energy monitoring, and baseline anomaly-detection workflows.
+
+The dataset contains **5,040 timestamped records** from a home-monitoring scenario sampled at regular 6-minute intervals over 21 days. Each row combines door activity, hallway motion, living-room temperature, fridge power consumption, and a label field.
+
+## Dataset Files
+
+| File | Description |
+|---|---|
+| `train.csv` | Main dataset file with timestamped smart-home sensor measurements |
+
+## Dataset Details
+
+| Field | Value |
+|---|---|
+| Dataset type | Tabular time-series sensor data |
+| Number of rows | 5,040 data rows |
+| Number of columns | 8 |
+| Time range | 2025-01-01 00:00:00 to 2025-01-21 23:54:00 |
+| Sampling interval | 6 minutes |
+| Label values in current file | `none` |
+| License | MIT |
+
+## Column Description
+
+| Column | Type | Description |
+|---|---|---|
+| `timestamp` | datetime | Timestamp for each observation |
+| `door_state_front` | numeric | Front-door sensor signal |
+| `door_state_front_event_duration_seconds` | numeric | Duration of the front-door event in seconds |
+| `motion_detected_hallway` | numeric | Hallway motion sensor signal |
+| `motion_detected_hallway_event_duration_minutes` | numeric | Duration of hallway motion event in minutes |
+| `temperature_living_room` | numeric | Living-room temperature reading |
+| `power_consumption_fridge` | numeric | Fridge power consumption reading |
+| `label` | categorical | Event or condition label; current dataset rows are labeled `none` |
+
+## Basic Statistics
+
+| Feature | Minimum | Maximum | Mean |
+|---|---:|---:|---:|
+| `temperature_living_room` | 9.77 | 37.11 | 20.19 |
+| `power_consumption_fridge` | 9 | 605 | 134.80 |
+| `door_state_front` | 0 | 5.20 | 0.13 |
+| `motion_detected_hallway` | 0 | 5.20 | 1.06 |
+
+Non-zero activity appears in 135 rows for `door_state_front` and 1,104 rows for `motion_detected_hallway`.
+
+## Intended Uses
+
+This dataset can be used for:
+
+- Smart-home monitoring prototypes
+- IoT sensor data analysis
+- Time-series feature engineering
+- Baseline modeling for normal home operation
+- Anomaly-detection experiments using normal-only data
+- Energy monitoring and appliance-consumption analysis
+- Activity-aware home automation research
+- Teaching examples for tabular time-series preprocessing
+
+## Out-of-Scope Uses
+
+This dataset should not be used as a standalone safety, health, clinical, elder-care, or security monitoring system. Any deployment in a real home-monitoring environment requires external validation, privacy review, operational testing, and domain-specific safeguards.
+
+## Loading the Dataset
+
+### Hugging Face `datasets`
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("MBJamshidi/HomeMonitoringSystem")
+train = dataset["train"]
+print(train[0])
+```
+
+### pandas
+
+```python
+import pandas as pd
+
+df = pd.read_csv("train.csv", parse_dates=["timestamp"])
+print(df.head())
+```
+
+## Example Preprocessing
+
+```python
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+df = pd.read_csv("train.csv", parse_dates=["timestamp"])
+
+df["hour"] = df["timestamp"].dt.hour
+df["day_of_week"] = df["timestamp"].dt.dayofweek
+
+features = [
+    "door_state_front",
+    "door_state_front_event_duration_seconds",
+    "motion_detected_hallway",
+    "motion_detected_hallway_event_duration_minutes",
+    "temperature_living_room",
+    "power_consumption_fridge",
+    "hour",
+    "day_of_week",
+]
+
+X = df[features]
+y = df["label"]
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X,
+    y,
+    test_size=0.2,
+    shuffle=False,
+)
+```
+
+## Notes for Machine Learning
+
+- The current `label` column contains only `none`, so supervised multi-class classification is not meaningful without additional labels.
+- The dataset is well suited to normal-baseline modeling, exploratory time-series analysis, and unsupervised anomaly-detection workflows.
+- Use chronological train/test splitting for time-series experiments.
+- Report feature engineering, scaling, split dates, and evaluation metrics clearly for reproducibility.
+
+## Limitations
+
+- The dataset covers one 21-day period only.
+- The current file contains normal or unlabeled records only, based on the `none` label.
+- Sensor definitions are limited to the available column names and should be interpreted conservatively.
+- Models trained on this dataset should be externally validated before use in operational monitoring.
+
+## Citation
+
+If you use this dataset in research, software, reports, or educational material, please cite the dataset repository:
+
+```bibtex
+@misc{jamshidi_home_monitoring_system,
+  title={Home Monitoring System},
+  author={Jamshidi, Mohammad Behdad},
+  year={2026},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/MBJamshidi/HomeMonitoringSystem}}
+}
+```
+
+## License
+
+This dataset is released under the MIT License.
+
+## Maintainer
+
+Mohammad Behdad Jamshidi
+
+- Hugging Face: [MBJamshidi](https://huggingface.co/MBJamshidi)