The meat company Candidez Carnica SL, which has a factory where it manufactures packs of cooked ham for a well-known distribution chain, has asked us for a database to manage its packs for a well-known distribution chain, asked us for a database to manage the hourly records of its factory workers and the time records of its factory workers and the units manufactured of its assorted products to know the productivity of the company’s shifts.

Continue loading the first part of the project final dataset. Preparing dataset to work with different analysis models, so in its current state, derived from phase 2

We will go through the first 3 phases of the project that will lead to the acquisition of a dataset with which we will tackle the modelling and testing phases in a second part of the project.

We will address the creation of a supervised data mining project using a classification algorithm, focus in the decision tree model. We will rely on the ‘German Credit’ dataset from “UCI Machine Learning Repository: Statlog (German Credit Data) Data Set”

In this project we will work with unsupervised machine learning models, with special emphasis on the modification of parameters to refine the results. The objectives are generation, interpretation, and evaluation of a k-means clustering model and a DBSCAN model. In doing so, we will not overlook the phases of data preparation, model quality, and initial knowledge extraction.

In this project, we intend to show how the “life cycle” applies to data mining (DM) projects.The ‘project life cycle’ refers to the sequential phases that a project goes through from its initiation to its completion. It provides a structured framework for managing projects and encompasses all the activities, processes, and deliverables involved in the project’s lifespan. We are talking abour a general framework that establishes the stages or phases a project goes through from initiation to completion. The project lifecycle provides a structure for managing the project as a whole, from defining the objectives to delivering the results. In the other hand, while the CRISP-DM “What Is CRISP DM? - Data Science Process Alliance” (n.d.) model specifically focuses on the processes and steps involved in data mining, the project lifecycle is a broader framework that can be applied to different types of projects, including data mining projects. Then the CRISP-DM model can be considered as a specific methodology within the project lifecycle of a data mining project. It provides detailed guidelines on the specific stages to be followed when conducting a data mining project, such as understanding the business, understanding the data, preparing the data, modeling, evaluating, and deploying.

The mental health department of a geriatric company has identified indicators that suggest a link between hypertension and worsening mental disorders, as well as the occurrence of psychotic episodes and crises.

This work is a study case part of the eighth course "Google Data Analytics Capstone" of the certification program

This work is a study case part of the eighth course ["Google Data Analytics Capstone"](https://www.coursera.org/learn/google-data-analytics-capstone) of the ["Google Data Analyst"](https://www.coursera.org/professional-certificates/google-data-analytics) program.