Python

Introduction How do we define what makes two cyclists similar? In this project, I analyzed professional peloton data to build a cyclist recommendation system. Using data cleaning techniques and vector distance metrics, I managed to identify which riders share similar performance profiles, regardless of their team or nationality. Development Process The project focused on transforming raw tabular data into comparable performance profiles. 1. Data Collection The data was obtained from public sources, specifically after an exhaustive search, I decided to use those provided at Cycling Oracle ...

Introduction In this project, the goal was to create an integral solution for the management and visualization of geospatial data. For this, it was necessary to develop a viewer capable of monitoring the position of livestock in relation to the boundaries of different farms, allowing for the dynamic filtering and visualization of data. System Architecture The system is divided into three fundamental layers that ensure an efficient data flow: 1. Preprocessing (Data Engineering) We use a Jupyter Notebooks environment (preprocess.ipynb) to clean and transform raw data (cows_pos.csv and fincas.json). Here we normalize coordinates, perform spatial joins, and prepare the dataset for consumption. ...

Objective To develop a computational method that allows: Segmenting existing roads in an input image. That is, given an aerial image, provide a binary mask with road pixels set to 1 and the rest to 0. For a set of test images, provide a set of quantitative evaluation metrics for the segmentation appropriate for the target application, using the corresponding ground truth. Workflow Load the input images and each ground truth. ...