Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+tags:
+- telecommunications
+- mobile
+- internet
+- 4G
+pretty_name: Synthetic Mobile Performance Dataset
+size_categories:
+- 10M<n<100M
+---
+
+
+# Dataset Card: Synthetic Mobile Network Performance
+
+**Dataset Description**
+
+This dataset contains synthetically generated mobile signal measurements designed to mirror real-world data in the UK. The data represents geolocated signal quality metrics from mobile devices, capturing a range of environmental and temporal conditions over several months in 2025.
+
+All data has been anonymized, aggregated, and processed to protect user privacy. The synthetic dataset has undergone pre-processing, including coordinate validation and outlier removal.
+
+**Fields**
+
+The dataset contains the following columns:
+
+*   **timestamp**: (date) The date and time of the measurement, recorded in UTC.
+*   **unique_cell**: (string) A unique identifier for the mobile network cell to which the device was connected.
+*   **measurement_type_name**: (string) The type of measurement recorded. This column appears to be null in the provided sample.
+*   **in_outdoor_state**: (string) A categorical label indicating the predicted environment of the device, with values such as "Surely Indoor," "Probably Indoor," and "Surely Outdoor."
+*   **value**: (float) A numerical value associated with the measurement. The specific KPI this represents is not defined.
+*   **latitude**: (float) The latitude of the device's location at the time of measurement.
+*   **longitude**: (float) The longitude of the device's location at the time of measurement.
+*   **signal_level**: (float) The received signal strength, likely measured in dBm.
+
+**Potential Uses**
+
+This dataset is suitable for a variety of analyses, including:
+
+*   **Network Coverage Mapping**: Visualizing signal strength across different geographic areas to identify zones with strong or weak coverage.
+*   **Performance Analysis**: Correlating signal quality with factors like location (indoor/outdoor), time of day, and cell tower.
+*   **Mobility Pattern Simulation**: Understanding how user movement impacts network performance.
+*   **Machine Learning Model Training**: Developing models to predict signal quality based on location and environmental factors.