[et_pb_section fb_built=”1″ _builder_version=”4.17.0″ custom_padding=”0px||||false|false” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_row _builder_version=”4.17.0″ _module_preset=”default” custom_padding=”0px||||false|false” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_column type=”4_4″ _builder_version=”4.17.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_gallery gallery_ids=”1428,1432,1434,1436″ fullwidth=”on” _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”et_body_layout” sticky_enabled=”0″][\/et_pb_gallery][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”et_body_layout”][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”]<\/p>\n
Telecommunications companies collect traffic data on mobile networks, which they use to manage the services they offer to customers, as well as to ensure the quality of those services. This information can be used to obtain valuable information. In this work, it is proposed to segment customers according to their behavior in the network, so that the company can know the groups that are formed, what characterizes them, and the tariff that best suits them.<\/span><\/p>\n The objective of the work is to use the data that the company stores on customers to find behavioral patterns, analyzing which attributes are the most important and the rate they should have. For this purpose, different Artificial Intelligence models are applied; in addition, process mining techniques are used, converting the large amount of unstructured data into easily interpretable models.<\/span><\/p>\n To solve the problem, first of all, a set of synthetic data is created in order to verify that both the algorithms used and the metrics are correct, thus starting from a certain base. Once this work has been done, we move on to pre-processing and experimentation with the real data provided by M\u00e1sM\u00f3vil.<\/span><\/p>\n The conclusions drawn from the experimentation are basically three: that prepaid and postpaid customers have different behaviors, that the study cannot be applied to business customers because they have highly personalized tariffs, and that the different segments created are differentiated mainly according to GB consumption for downloading and uploading content, in addition to calls made.<\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.18.0″ _module_preset=”default” custom_margin=”20px||||false|false” custom_padding=”0px||||false|false” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_column type=”1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_image src=”https:\/\/storage.googleapis.com\/wp-uploads.bucket.wp.uc3m.es\/wp-content\/uploads\/sites\/70\/2024\/06\/26101506\/Copia-de-1698139617183.png” title_text=”Photo” _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”et_body_layout” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.18.0″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_text _builder_version=”4.25.1″ _module_preset=”default” custom_padding=”||0px|||” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”et_body_layout” sticky_enabled=”0″]<\/p>\n BACHELOR’S THESIS BY:<\/strong><\/span><\/p>\n MAR\u00cdA EMILIA N\u00da\u00d1EZ GUERRERO<\/strong><\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”]<\/p>\n <\/strong><\/p>\n <\/strong><\/p>\n <\/strong><\/p>\n Academic Experience<\/strong><\/strong><\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_column type=”4_4″ _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”et_body_layout”][et_pb_text _builder_version=”4.25.1″ _module_preset=”default” custom_padding=”||9px|||” global_colors_info=”{}” theme_builder_area=”et_body_layout”]<\/p>\n Work Experience<\/strong><\/strong><\/p>\n AI Developer – Enthec (Febrero 2024 – actualidad)<\/p>\n<\/li>\n Machine Learning Researcher – Universidad Carlos III de Madrid in collaboration with Grupo M\u00e1sM\u00f3vil (September 2023 – May 2024)<\/p>\n<\/li>\n<\/ul>\n <\/strong><\/p>\n Awards and Certifications Community of Madrid Excellence Scholarship 2022\/23<\/p>\n<\/li>\n Best Creative Idea in Digital Transformation in the field of Occupational Health and Safety (2022)<\/p>\n<\/li>\n Banco Santander Excellence Award 2022<\/p>\n<\/li>\n Community of Madrid Excellence Scholarship 20221\/22<\/p>\n<\/li>\n Community of Madrid Excellence Scholarship 2020\/21<\/p>\n<\/li>\n Santander Progreso 2021 Scholarship<\/p>\n<\/li>\n Santander Progreso 2020 Scholarship<\/p>\n<\/li>\n<\/ul>\n\n
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Technical skills<\/strong><\/p>\n