Notebook: Telco Network Analytics

[NiloFreitas]

Description

This issue proposes the development of a new notebook that demonstrates how to perform telco network analytics using BigQuery and Vertex AI. The notebook should cover the following aspects:

Data Ingestion and Preparation

Ingest telco network data from a public dataset or a synthetic dataset into BigQuery.
Perform data cleaning, transformation, and feature engineering to prepare the data for analysis using BigQuery Dataframes.
Explore and visualize the data

Model Development and Training

Develop machine learning models to predict network outages, identify root causes, and optimize network performance.
Utilize Vertex AI for model training, hyperparameter tuning, and model deployment.

Consider using various model architectures, such as:
Predictive models for network outages (e.g., binary classification, time series forecasting).
Clustering models for root cause analysis (e.g., k-means, DBSCAN).
Regression models for network optimization (e.g., linear regression, decision trees).

Model Evaluation and Deployment

Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
Deploy trained models on Vertex AI for real-time predictions and analysis.
Draw insights from the prediction results.

Resources

• https://cloud.google.com/python/docs/reference/bigframes/latest

Contributing

Contributing guidelines: CONTRIBUTING.md

Note: Please refer to the contributing guidelines for detailed instructions on how to contribute to this repository.

This notebook will provide a valuable resource for users interested in applying BigQuery Dataframes, Gemini. We encourage contributions from the community to help develop this notebook.

We appreciate a lot your contribution! :)