In this ML project we will create a model that predicts the day-ahead price of power in Spain with Python. As in previous projects, we will cover some of the most interesting steps and findings but not all of them. Personally, I would recommend that you download the code (Jupyter notebook) and the datasets (CSV files) for a better understanding. Our dataset (power_market.csv) is composed of: date: date of the observation ”%Y-%m-%d” hour: hour of the observation, [0 - 23] fc demand: forecast of demand in MWh fc nuclear: forecast of nuclear power production in MWh import FR: forecast of the importing capacity from France to Spain in MWh export FR: forecast of the exporting capacity from Spain to France in MWh fc wind: forecast of wind power production in MWh fc solar pv: forecast of PV solar (solar panels) power production in MWh fc solar th: forecast of thermal solar power production in MWh price: power price for each hour in €/MWh.

Has popular music truly changed over the last 50 years? We bring something different this time. Would it be possible to show the evolution of popular music over the last five decades using visualizations? We examined a dataset from Kaggle that contains more than 175.000 songs from the Spotify Website API. Find the link to the dataset below. Some of the most relevant variables: artists (List of artists mentioned) release_date (Date of release) name (Name of the song) acousticness (Ranges from 0 to 1) danceability (Ranges from 0 to 1) energy (Ranges from 0 to 1) duration_ms (Integer typically ranging from 200k to 300k) valence (Ranges from 0 to 1) popularity (Ranges from 0 to 100) loudness (Float typically ranging from -60 to 0) year (Ranges from 1921 to 2020) After understanding the dataset with an Exploratory Data Analysis and applying some transformations, we created our figures.