Joel Pires's repositories
Bachelor_CS_Projects
Collection of some of the most relevant projects developed during my Bachelor in Computer Science.
CDRsDataAnalysis
Inferring Commuting Routes And Respectives Modes Of Transport Through Call Detail Records (Python, PostgreSQL, Google API, ArcGIS)
computer-vision-elements
Multiple scripts developed using python and openCV. From basic concepts like indentification of edges and contours to slight more advanced ones like object detection and face recognition.
tensorflow-elements
Multiple scripts developed using python and tensorflow. From basic concepts like variables and constants, to slight more advanced ones like encoders and GANs.
GPS_clustering
Identification of activity stop locations in GPS trajectories by density-based clustering method (with some constraints - C DBSCAN) combined with SVM and using WEKA's libraries
Master_AI_Projects
Collection of some of the most relevant projects developed during the MSc. in Artificial Intelligence.
praxxis
A task interface for Jupyter notebooks built on machine learning and big data
predictML
A Flask powered web app hosted at https://predictML.herokuapp.com. It classifies the income based on socio-economic information of US citizens and also predicts house prices of their houses based on their conditions.
landingPage
Code behind the website joelpires.com
ml-challenges
A collection of python notebooks with exploratory data analysis and actual solutions to kaggle challenges and competitions (ongoing updates)
MovieTaster-Open
A practical movie recommend project based on Item2vec.
name_counter
It counts the number of occurrences of a name (in a names database) on a document.
OCR_reader
Tool to extract text from a given PDF, JPEG, or PNG file
pwc
Papers with code. Sorted by stars. Updated weekly.
tensorscript
Deep Learning Classification, Clustering, LSTM Time Series and Regression with Tensorflow
twitter_sentiment_analysis
Development of a model that is able to determine with reasonable accuracy the sentiment expressed by travelers in Twitter regarding their experience with US airline companies.