Analytical Competitor
Posted: Mon Dec 23, 2024 4:51 am
We find ourselves with deep strategic knowledge.
There is continuous development and renewal, processes are fully integrated, and technology represents support for the entire organization in the practice of business intelligence and business analysis.
They have professional profiles within the organization with great analytical capabilities, the management is fully involved, there is even a management committee.
It is a culture of fact-based decision making and continuous learning.
Data life cycle
We see in a brief but visual way the life cycle and its corresponding phases.
Data Mining - Data Life Cycle
CRISP-DM implementation methodology
We now analyze this methodology in depth and everything it entails.
Understanding the business
As with any project, it is important to know the objective that is being achieved in order to complete it.
To do this, we must establish the business objectives, evaluate the current situation, detect the objectives sought in data mining and the project milestones.
Data compression
This phase focuses on understanding and analyzing the capture of the data and its sources, understanding the information that describes them, and the relationships established between the different attributes.
Exploratory analysis will use graphs and calculations to determine the need for transformations of the captured data.
Likewise, we will evaluate the quality, ensuring its suitability for correct modeling.
In understanding, we must verify the objectives set in the previous phase.
Preparation
This phase requires the greatest consumption of time in the project.
After the two previous phases, we will carry out the necessary tasks to select, clean, integrate and build our data sets for modeling.
Some activities in this phase are:
Scaling of numerical variables to shift said variables to equivalent r dubai state name list anges.
Balancing records of the target variable in the case of classification algorithms.
Elimination of outliers or atypical values from an observation that distort the model.
Removing or filling null or empty values.
If necessary, eliminated from input variables for presenting correlations with other variables or with those of low quality.
Grouping, binning, or other transformations of input variables.
Modeling
We will make decisions about the modeling technique seeking to make its behavior more efficient.
To do this, we will define the appropriate metrics that allow us to compare the quality between the different algorithms, usually the success rate, the error rate or the confusion matrices.
Each model allows you to adjust a set of parameters to modulate its behavior.
Repeated evaluation of the results will allow defining the value of these parameters to find the optimal behavior.
It may be necessary to return to the previous phase to make
There is continuous development and renewal, processes are fully integrated, and technology represents support for the entire organization in the practice of business intelligence and business analysis.
They have professional profiles within the organization with great analytical capabilities, the management is fully involved, there is even a management committee.
It is a culture of fact-based decision making and continuous learning.
Data life cycle
We see in a brief but visual way the life cycle and its corresponding phases.
Data Mining - Data Life Cycle
CRISP-DM implementation methodology
We now analyze this methodology in depth and everything it entails.
Understanding the business
As with any project, it is important to know the objective that is being achieved in order to complete it.
To do this, we must establish the business objectives, evaluate the current situation, detect the objectives sought in data mining and the project milestones.
Data compression
This phase focuses on understanding and analyzing the capture of the data and its sources, understanding the information that describes them, and the relationships established between the different attributes.
Exploratory analysis will use graphs and calculations to determine the need for transformations of the captured data.
Likewise, we will evaluate the quality, ensuring its suitability for correct modeling.
In understanding, we must verify the objectives set in the previous phase.
Preparation
This phase requires the greatest consumption of time in the project.
After the two previous phases, we will carry out the necessary tasks to select, clean, integrate and build our data sets for modeling.
Some activities in this phase are:
Scaling of numerical variables to shift said variables to equivalent r dubai state name list anges.
Balancing records of the target variable in the case of classification algorithms.
Elimination of outliers or atypical values from an observation that distort the model.
Removing or filling null or empty values.
If necessary, eliminated from input variables for presenting correlations with other variables or with those of low quality.
Grouping, binning, or other transformations of input variables.
Modeling
We will make decisions about the modeling technique seeking to make its behavior more efficient.
To do this, we will define the appropriate metrics that allow us to compare the quality between the different algorithms, usually the success rate, the error rate or the confusion matrices.
Each model allows you to adjust a set of parameters to modulate its behavior.
Repeated evaluation of the results will allow defining the value of these parameters to find the optimal behavior.
It may be necessary to return to the previous phase to make