For this project, I have created a tool to get an estimated rent price for certain neighborhoods of Madrid, based on the apartment parameters selected by the user. The web app has been built using Streamlit and I have used a linear regression model in order to predict the rent price.

I have used Idealista API to get a list of apartments, including their characteristics and rent price. The data has being downloaded into a .csv file.

Understanding of the dataset: columns, types of data.

Deleting unnecessary info and transforming data to create the regression model.

Choosing the variables to be used in the model and encoding the categorical ones.

The model has 7 variables (size, exterior, hasLift, hasParkingSpace, isFloorZero, propertyType, neighborhood) and has been trained using 30% of the data. An R-squared of 0.72 was obtained.

Using Streamlit, I built an interface where the user can use the different parameters (the variables used in the model) to get an estimated rent price based on these inputs.