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7- Data Mining / Regression Techniques with Data Integration

Institution: Pontifical Catholic University of São Paulo (PUC-SP)
School: Faculty of Interdisciplinary Studies
Program: Humanistic AI and Data Science Semester: 2nd Semester 2025
Professor: Professor Doctor in Mathematics Daniel Rodrigues da Silva

Important

⚠️ Heads Up

• Projects and deliverables may be made publicly available whenever possible.
• The course emphasizes practical, hands-on experience with real datasets to simulate professional consulting scenarios in the fields of Data Analysis and Data Mining for partner organizations and institutions affiliated with the university.
• All activities comply with the academic and ethical guidelines of PUC-SP.
• Any content not authorized for public disclosure will remain confidential and securely stored in private repositories.

🎶 Prelude Suite no.1 (J. S. Bach) - Sound Design Remix

Statistical.Measures.and.Banking.Sector.Analysis.at.Bovespa.mp4

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Tip

This repository is a review of the Statistics course from the undergraduate program Humanities, AI and Data Science at PUC-SP.

☞ Access Data Mining Main Repository

Overview

This repository covers fundamental concepts and practical techniques in Data Mining focused on clustering (grouping by similarity), various types of regression for modeling data trends, and the crucial steps for data integration and preprocessing. Each section includes theoretical explanations, use case examples, mathematical formulations using LaTeX, and Python code snippets to assist practical understanding.

Table of Contents

• Clustering
• Regression Types
• Data Integration
  • Data Redundancy and Duplicates
  • Data Conflicts
  • Data Compression
  • PCA - Principal Component Analysis
  • Data Standardization
  • Data Normalization

Bibliography

1. Castro, L. N. & Ferrari, D. G. (2016). Introduction to Data Mining: Basic Concepts, Algorithms, and Applications. Saraiva.

2. Ferreira, A. C. P. L. et al. (2024). Artificial Intelligence – A Machine Learning Approach. 2nd Ed. LTC.

3. Larson & Farber (2015). Applied Statistics. Pearson.

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