IAAC’s Master in City & Technology (1 or 2-year program) is a unique program oriented towards redefining the analysis, planning, and design of twenty-first-century cities and beyond. The program offers expertise in the design of digitally enhanced, ecological and human-centered urban environments by intersecting the disciplines of urbanism and data science. Taking place in Barcelona, the capital of urbanism, the Master in City & Technology is training the professionals that city administrations, governments, industries, and communities need, to transform the urban environment in the era of big data.
Introduction The objective of this Data Science project is to investigate the possible evolution that the value of a chess piece may experience when they’re allowed to move on a board of higher dimensions. The project will analyse the movement of the chess pieces beyond the 2d plane and onto the 3d cube, 4d hypercube … Read more
As the Submission about Data Digital Tools & Big Data II, we analyzed the new york taxi data. We were given the data about Taxi Infomation in New York, in 2016.In this data, there is information, from the data, I made new information, Erased some columns, and fix the data. Reading the data the data … Read more
Clustering advanced industries to facilitate technology transfer in the Barcelona metropolitan region Spain has been at the forefront of pioneering research and innovation that have led to significant contributions to the world of technology. Technology Transfer Technology transfer from research to market seems like a straight-forward and linear process, but such is not the case. … Read more
The New York City Taxi Trip Duration competition is a challenge to develop a model that predicts the total ride time of taxi trips in New York City. Yellow medallion taxicabs, which number 12,779 in New York City, generate a substantial revenue of $1.8 billion per year by providing transportation services to around 240 million … Read more
Machine learning has been applied to a wide range of domains, including transportation, to improve the accuracy of predictions and optimize systems. In the context of taxi services, predicting the trip duration is an essential task to optimize route planning and estimate arrival times. In this post, we present a machine learning approach using Python … Read more
Goal: to predict trip duration of NYC cabs using machine learning models. Tools: Python + Nympy + Pandas + Datetime + Plotly.express + Matplotlib + Math + Seaborn + Bokeh + Sklearn Stages of project: data cleaning, data analysis, data preparation, data testing, evaluating prediction accuracy. Data cleaning The first dataset visualization with splitting datasets … Read more
Machine Learning-based Analysis of NYC Cab Trip Duration “Data-Driven Rides” is an entry for the first MaCT Machine Learning Competition that hosted on Kaggle which involves predicting the duration of taxi rides in New York City. The dataset provided for this competition is based on the 2016 NYC Yellow Cab trip record dataset and the … Read more
The objective of this exercise is to build a machine learning model that will predict taxicab trip durations based on 2016 NYC Yellow Cab trip record data. Data Fields Based on this, this project will develop a machine learning regression algorithm capable of predicting the duration of a trip based on the variables provided by … Read more
The MaCT01 students were tasked with training a model that would be used to predict NYC yellow taxi ride durations using machine learning. The dataset included pickup and drop-off datetimes, location coordinates and passenger count. Visualizing the data helped to understand the correlation between the columns and remove the highly correlated values Understanding the distribution … Read more
#Objective #A Kaggle Competition to MaCT01 students to show their knowledge, designing an end-to-end machine learning project to predict the “Trip Duration” of NYC Taxi trips. #Workflow #First of all a workflow hast do be developed, which represents a classic approach for training machine learning models, analysing the provided training data provided by the submission, … Read more
INTRODUCTION Urban planning decisions have a significant impact on the development of cities, and using machine learning can provide decision-makers with valuable insights to make informed decisions. By clustering urban areas based on various factors such as population density, built density, POI density, green cover, and build diversity, we can reveal spatial patterns that can … Read more
a story about Spanish festivals Does it possible to party all year long from festival to festival throughout Spain? The story starts from the New Year holiday at the 1st of January For the better clusterisation we will use administrative division of Spain in autonomous communities (comunidades) and provinces. Each province of Spain has its … Read more
An Exploration of the Epidemic of Femicide “How many more women must fall victims before we recognize that femicide is a real and pressing social problem that demands our attention? Despite its significance, many people are still unfamiliar with the term. Femicide is the ‘the intentional killing of a woman because of her gender‘. It … Read more
A Visual Story by: According to World Health Organization, 1/5 adolescents engage in some form of substance use. The Question of 100 School Phycologists: How planning could contribute towards the understanding of geographic patterns of substance use in order to distribute human resources all around the country? This visual story, seeks to explore the geographical … Read more
Objective The objective of the project is to create a database of stickers located in the district of Gotico, Barcelona by collecting geolocated photos, analyzing & sorting them by machine learning and further research to then create a framework for evaluating and improving local economy, artists and political movements marketing strategies. Strategy By collecting geolocated … Read more
