The dissertation aims to provide an insight into what it means to apply statistical methods (particulary machine learning) inside a large company. The thesis will firstly expose the topic of data inside the company as well as the structure of the company, especially the food service segment, then cover topics mainly concerning Cluster Analysis and Survival Analysis applied to the Italian customer base in order to obtain useful results for the company. Lastly it will conclude with a few remarks about the results found, their limitations, and possible future developments
The dissertation aims to provide an insight into what it means to apply statistical methods (particulary machine learning) inside a large company. The thesis will firstly expose the topic of data inside the company as well as the structure of the company, especially the food service segment, then cover topics mainly concerning Cluster Analysis and Survival Analysis applied to the Italian customer base in order to obtain useful results for the company. Lastly it will conclude with a few remarks about the results found, their limitations, and possible future developments
Exploratory Data Analysis and Machine Learning in Lavazza
CANETTI, ALESSIO
2023/2024
Abstract
The dissertation aims to provide an insight into what it means to apply statistical methods (particulary machine learning) inside a large company. The thesis will firstly expose the topic of data inside the company as well as the structure of the company, especially the food service segment, then cover topics mainly concerning Cluster Analysis and Survival Analysis applied to the Italian customer base in order to obtain useful results for the company. Lastly it will conclude with a few remarks about the results found, their limitations, and possible future developmentsFile | Dimensione | Formato | |
---|---|---|---|
Tesi_CANETTI.pdf
non disponibili
Dimensione
13.3 MB
Formato
Adobe PDF
|
13.3 MB | Adobe PDF |
I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/20.500.14240/9351