|
|
| (173 revisões intermediárias por 6 usuários não estão sendo mostradas) |
| Linha 1: |
Linha 1: |
| = Step I - Project Presentation =
| | * Link: |
| | | ** http://intranet/wiki/index.php/Telco_Analytics |
| <br>
| |
| | |
| | |
| Telco Analytics
| |
|
| |
| | |
| <br>
| |
| | |
| == Purpose ==
| |
| | |
| Investment optimization and generation of new revenues to support Digital Transformation based on Data
| |
| | |
| <br>
| |
| | |
| == Project Keys ==
| |
| | |
| <br>
| |
| | |
| 1- Semantic Data Integration
| |
| 2- Analytics - Correlação de Dados
| |
| 3- Advanced Anomaly Detection
| |
| 4- Embedded IoT Analytics to the Edge
| |
| 5- Machine Learning
| |
| 6- Open Data - For Peoples (PL 53/2018)
| |
| 7- Decision Management
| |
| 8- Immersive User Experience
| |
| 9- Geospatial And Location Intelligence
| |
| 10- Digital Twins
| |
| | |
| <br>
| |
| | |
| == Presentation ==
| |
| | |
| <br>
| |
| | |
| Link to the presentation [https://docs.google.com/presentation/d/1MU4oSvaPymDCDg-cJnSJU7Fuh_kVGYKxWfm_zBhrjFo/edit?ts=5bd1ce1a#slide=id.g465dc625f9_1_153]
| |
| <br>
| |
| | |
| <br>
| |
| | |
| = Step 2 - Studies =
| |
| | |
| <br>
| |
| | |
| == Usable Books ==
| |
| Open this Drive Link to find 3 books [https://drive.google.com/drive/folders/1EzfmV4nbfNedVVHPzLsXsEFqtvOVoLi9?usp=sharing] :
| |
| * Data Mining for the masses 2nd edition
| |
| * Python for data Analysis
| |
| * Data Mining concepts and techniques
| |
| <br>
| |
| | |
| ==Methodology==
| |
| <br>
| |
| | |
| CRISP-DM stands for cross-industry process for data mining.
| |
| | |
| The CRISP-DM methodology provides a structured approach to planning a data mining project.
| |
| Phase of the process:
| |
| 1- Business understanding
| |
| 2- Data understanding
| |
| 3- Data preparation
| |
| 4- Modeling
| |
| 5- Evaluation
| |
| 6- Deployment
| |
| <br>
| |
| | |
| = Step 3 - Business Case Example<br> =
| |
| | |
| <br>
| |
| | |
| == Benefits to anyone who offers this solution ==
| |
| | |
| Offer a machine learning Template for data mining in order to:
| |
| * Improve the broadband customer experience
| |
| * Remote data management and processing with IoT optimizing investments
| |
| * Network scanning for monitoring, testing and predicting events.
| |
| <br>
| |
| | |
| <br>
| |
| | |
| == Benefits to the user ==
| |
| | |
| Make Intelligent predictions more faster.
| |
| | |
| <br><br>
| |
| | |
| == Business Models ==
| |
| | |
| We can use some Classification models Like:
| |
| - Random Forest
| |
| - Decision Tree
| |
| - XGBoost
| |
| <br>
| |
| | |
| = Step 4 - Business-oriented prototype =
| |
| | |
| <br>
| |
| | |
| == Scoop ==
| |
| | |
| <br>
| |
| | |
| Join Statistics and programming to better understand huge Data and make decisions in order to improve the company.
| |
| <br>
| |
| | |
| == Technical details ==
| |
| | |
| <br>
| |
| | |
| Tools for Studing Steps:
| |
| * Knime
| |
| * Anaconda / Jupyter notebook : Python users
| |
| * R studio : R users
| |
| | |
| Tools for Collaborative Work:
| |
| * Data Lake / DW : data extraction
| |
| * Google Data Studio : Data visualization
| |
| * Google Colab: Python users
| |
| * Rstudio Cloud : R users
| |
| | |
| <br>
| |
| | |
| <br>
| |
| | |
| <br>
| |
| | |
| <br>
| |
| | |
| = Cronograma Macro =
| |
| | |
| <br>
| |
| | |
| = Histórico =
| |
| | |
| <br>
| |
| | |
| <br>
| |
| | |
| = Pesquisadores =
| |
| | |
| *<br>
| |