Dataset selection and preparation

1. Introduction, competencies and objectives

The original document frames the assignment as a real analytical project rather than a disconnected coding exercise. It explains the competencies involved, the lifecycle of a data-mining workflow and the objective of moving from business questions to properly prepared data and interpretable models.

2. Dataset choice and problem statement

The selected dataset is the Wine dataset from the UCI repository, built from chemical analyses of wines from the same Italian region but belonging to three different classes. The notebook justifies this choice because the variables support clustering, discretization, dimensionality reduction and later predictive modeling.

In other words, the file is not chosen only because it is available, but because it is rich enough to support several analytical families within the same project.

3. Exploratory analysis and preparation

Once the dataset has been introduced, the notebook inspects its variables, distributions and basic structure in order to understand what kind of transformations are needed before modeling. This is the stage where the problem moves from dataset selection to actionable preparation work.

A good preparation step should answer four questions before any algorithm is executed: which variables are useful, which variables are noisy, which values are missing or inconsistent, and whether the target variable is accidentally leaking information into the features. Those checks protect the later model from optimistic but misleading results.

4. Discretization methods

A large part of the assignment is devoted to discretization. The notebook compares several ways of transforming the variables under different preprocessing conditions:

• Without discretization or normalization: direct application of the methods on the raw values.
• With standard normalization: repeating the study after rescaling the variables.
• With 0-1 normalization: testing a bounded transformation before clustering.

Within those settings, the work compares k-means, equal-width partitioning, equal-frequency partitioning and a linear-distribution approach related to discriminant analysis.

5. k-means on 0-1 normalized data

After the discretization experiments, the notebook dedicates a specific section to k-means on 0-1 normalized data. This makes it easier to compare how scaling affects cluster behavior and whether the transformed feature space is more suitable than the raw one.

6. Conclusions and dimensionality reduction

The final sections summarize the preparation process and then move to dimensionality reduction through SVD and PCA. This broadens the notebook from pure cleaning and discretization into a first analysis of lower-dimensional representations of the data.

The full notebook below remains in Spanish and contains the complete step-by-step development, tables and code outputs.

The important SEO and learning value of this page is that it explains the preparation layer, not only the final algorithms. Many data-mining examples jump directly to clustering or classification, but a model is only useful if the input data has been selected, documented, normalized and checked for bias or leakage.

As a practical checklist, keep the raw dataset unchanged, document every transformation, separate exploratory decisions from evaluation decisions, and repeat the analysis after scaling to confirm that the result is not an artifact of variable magnitude.

In this particular Wine dataset, that means checking whether chemical measurements with larger numeric ranges dominate distance-based methods such as k-means. It also means keeping the class label out of unsupervised preparation steps unless it is being used only for interpretation after the clusters or components have already been computed.

That distinction is small but important: preparation should make the data easier to learn from, not quietly inject the answer into the features. This is one of the reasons why a preparation article deserves its own URL instead of being hidden inside the later modeling notebooks.

The page therefore works as a bridge between raw data and model evaluation: it explains the decisions that make the later clustering, PCA and classification pages easier to trust.

As related reading, the same workflow connects naturally with data quality in scraping projects, R data preprocessing and search index construction, because all of them depend on clean input data before any model or ranking method can be trusted.