This repository contains my solutions to the technical challenges proposed during the PUC Tech selection process.
The challenges were designed to evaluate both technical skills and problem-solving abilities in real-world scenarios. The focus of this project is not only on delivering working solutions, but also on demonstrating clear reasoning, code organization, and data-driven decision-making.
Throughout the challenges, the following topics and skills were explored:
- Exploratory Data Analysis (EDA) and data visualization
- Data cleaning and preprocessing
- Statistical analysis and interpretation of results
- Machine learning models for classification problems
- Model evaluation using appropriate metrics
- Backend logic involving data persistence and manipulation
- Implementation of basic system operations for managing structured data
For each challenge, the solution was developed with attention to:
- Understanding the problem and its business context
- Writing clear, readable, and well-structured code
- Explaining assumptions, decisions, and trade-offs
- Applying best practices whenever possible within the scope of the challenge
- Python
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn
- Jupyter Notebook
This repository is intended for educational and portfolio purposes.
The datasets used in the challenges are either publicly available or adapted to avoid sharing sensitive or proprietary information.