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anomalies_detection_time_series

Develop a model which, upon being fed with an unlabeled time series (which you can assume to also have an hourly frequency with no missing values), is able to classify each data point as normal or anomalous. Discuss whether your model could be used to detect outliers for future (previously unseen) data points of the series.

data-analysis-for-a-telecom-company-client

This repository showcases a comprehensive data analysis and business intelligence project for a telecom company client. Leveraging the power of data science techniques and Power BI, the project aims to provide valuable insights and actionable information to enhance decision-making and drive business growth.

data-engineering-project-1

deep-vision-cifar10

Mediante Deep Vision, tratamos de diseñar y comparar dos estrategias para la clasificación de imágenes en el dataset CIFAR100 de Keras.

electricity-consumption-timeseries-complete-analysis

We firstly decompose a time series using Python language and some of the most relevant libraries for time series analysis and machine learning. We then perform data transformation processes, obtain different Machine Learning models and evaluate the goodness of each model. Our dataset features the consumption of electricity recorded by a counter.

fintech-marketing-campaign

This project is based on the work as a Data Scientist in a financial products trading company. The intention is to face a scenario very close to the reality of the profession, in which the requirements are scarcely defined, and we must make use of our creativity to provide solutions in an area of analytical marketing and development of business.

java_avanzado

Código y ejemplos del curso de Java Avanzado

manipulating-files-and-folders

Write a bash script called unpack which can unpack multiple packed files (a.k.a “archives”), and even traverse folders recursively and unpack all archives in them - regardless of the specific algorithm that was used when packing them.

microsoft-malware-prediction

The objective of this exercise is to estimate the probability that a machine with Windows Operating System will be infected by some type of malware, based on the different properties of the machine.

ml_classification_pyspark_and_docker

The application aims to analyze a cardiovascular disease dataset, comprising 70,000 patient records with 11 features. By leveraging both Scikit Learn and PySpark ML libraries, the app provides users with a seamless comparison and selection of Machine Learning models for predicting cardiovascular disease risk.

ml_regression_maximizing_forecast_accuracy

Your task is to support business planning process by creating monthly predictions of Sales (y) for 119 products, for the next 12 months. You must perform appropriate data transformations & feature engineering/selection, build a predictive model(s) that will guarantee best performance on test dataset and make prediction on test dataset.

monitoring-processes

Write a bash script called psping which checks periodically if a specific executable has a live process.

numerical-methods-in-matlab

Numerical methods are very powerful problem-solving tools. They are proficient of handling large systems of equations, nonlinearities, and complicated geometries that are not uncommon in engineering practice and that are often impossible or hard to solve analytically. As such, they greatly enhance your problem-solving skills.

portfolio-risk-management-in-python

This notebook displays how to evaluate basic portfolio risk and returns. It shows what factors are driving the portfolio returns, constructs market-cap weighted equity portfolios, and forecasts and hedges market risk via scenario generation.

prediccion-de-ventas-con-series-temporales-multivariantes

El dataset con el que vamos a trabajar en este notebook contiene alrededor de 300 Series Temporales que abarcan el periodo Enero'2013 - Septiembre'2015. Cada una de estas Series Temporales representan las ventas diarias de un artículo en un tienda y ciudad. El objetivo es predecir las ventas totales del mes de octubre de 2015.

prueba-algoritmica

Three Python exercises attached.

quantum-periodic-potentials

It is of our interest to compute the following potentials: single barrier (Kronig-Penney model), Dirac delta, harmonic, double barrier and multibarrier. Main functions are to generate the potentials, create the Hamiltonian, calculate the associated energies, the bands and the generated states.

reinforcement-learning---cliff-walking

This is a standard un-discounted, episodic task, with start and goal states, and the usual actions causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked Cliff. Stepping into this region incurs a reward of optimal path -100 and sends the agent instantly back to the start.

schrodinger_hydrogen_atom

Analytical resolution of 3D Schrödinger equation, making it possible for us to study orbitals, energies and probability distributions of electron position.

song_generator

The program allows you to create songs by generating random sentences, the words of which will be associated with a feeling, from the sentences of a text file. Then allow the user to save the songs in a new text file, identified by its title and average feeling. The user will have two forms of creation: the generation of as many sentences as you want one by one or the direct creation of complete songs from words with the same feeling.

variational_principle_infinite_well

We apply the variational principle for the infinite well energies, analytically described by the Schrödinger equation. An approximate solution can be calculated using the variational principle, minimizing the expected value of the energy of a test wave function. The functions will have a symmetrized polynomial form.

web-scraper

Through web scraping, not only can you extract information, but also simulate navigation: such as clicking on links, logging into a page, saving images from the web, among other things. In Python there are a series of libraries/frameworks that allow web scraping to be carried out. In this case, we are using BeautifulSoup and Selenium.