I'm Pablo Gonzalez, a Computer Science student at the University of Valle residing in Guatemala City. My passion lies in the field of Artificial Intelligence and Deep Learning. I've undertaken projects in various programming languages such as Java, Python, and JavaScript. This repository serves as a portfolio of my work, reflecting my dedication to technological innovation and problem-solving. I'm eager to collaborate and engage with fellow enthusiasts in the ever-evolving world of AI and Deep Learning. Thank you for visiting, and let's explore the exciting possibilities together.
-
🔭 I’m currently working on Data Science
-
🌱 I’m currently learning Data Science
|
|
|
| Prediction of the type of vehicle involved in accidents | Prediction of rental prices for houses in Brazil | Music recommendation system |
|---|---|---|
 |
 |
 |
| This project focuses on the development and implementation of artificial intelligence models to predict the type of vehicle involved in traffic accidents in the Republic of Guatemala. Using a dataset spanning from 2017 to 2020, exploratory analysis and preprocessing techniques have been applied to understand the complexities of traffic accidents. The methodology is based on the implementation of neural networks with the TensorFlow library, progressing towards more complex models to enhance predictive capability. The results reveal a significant improvement in accuracy, with a model achieving a rate of 76.44%. This project aims to contribute to road safety by identifying patterns and risk factors, enabling preventive measures and improvements in traffic management in Guatemala. | This project addresses the analysis and prediction of housing rental prices in Brazil using advanced artificial intelligence techniques. After exploring and preprocessing an extensive dataset, a random forest regression model, optimized through hyperparameter tuning, is used to forecast rental costs. Model evaluation reveals its predictive ability, measured by Mean Absolute Error (MAE) and Mean Squared Error (MSE). An interface is included for making personalized predictions, exemplified with specific input data. This project provides insights into the factors influencing rental prices in the Brazilian real estate market and offers a practical tool for personalized estimates based on user preferences. | This project focuses on building a song recommendation system using collaborative filtering and cosine similarity techniques. The implementation is based on the analysis of an extensive music dataset, encompassing information on genres, acoustic features, and release years. Through feature normalization and similarity calculation, the model can suggest up to 10 songs similar to a given one. The scalability and effectiveness of the system are achieved through the use of the scikit-learn library for preprocessing and similarity comparison. This project enhances the user experience by discovering new songs aligned with user preferences, thus contributing to musical exploration and personalized recommendation. |