Database of gastronomic establishments in Barcelona (clean)

Below is a dictionary that clearly distinguishes between facts (quantitative variables to be analyzed) and dimensions (variables that allow organizing, segmenting, or contextualizing the data). Facts price_min: Quantitative variable derived from the price_range variable of the original dataset. It indicates the approximate minimum price of the restaurant. price_max: Quantitative variable derived from the price_range variable of the original dataset. It indicates the approximate maximum price of the restaurant. score: Continuous quantitative variable representing the average rating obtained from customer reviews, with a value range between 0 and 10. opinions_count: Discrete quantitative variable indicating the total number of customer reviews. days_open_count: Discrete quantitative variable indicating the number of days per week the restaurant is open. It is derived from the time_{day}_{moment} variables in the original dataset. Dimensions price_category: An ordinal categorical variable calculated from the price_range variable of the original dataset. It indicates the price range based on: [1] Budget (€0–25) [2] Moderate (€25–35) [3] Expensive (€35–50) [4] Luxury (>€50) zone: Categorical variable indicating the areas or neighborhoods where the restaurant is located, derived from the zone_source variable in the original dataset. After cleaning, the dataset includes a total of 22 different zones. It is split into multiple fields because a restaurant may border several zones. latitude and longitude: Continuous quantitative variables derived from address data, enabling precise geolocation of establishments. food: Categorical variable indicating the type of cuisine (homemade, pizzeria, rice dishes, etc.). It has high cardinality (99 categories after cleaning) and may require additional transformation processes (e.g., one-hot encoding). ambient: Categorical variable describing the atmosphere or style of the venue (romantic, traditional, iconic, etc.), with a total of 17 categories. target: Categorical variable indicating the main target audience of the restaurant, with values such as families, young people, groups, or business. open_lunch: Binary categorical variable indicating whether the restaurant offers lunch service. open_dinner: Binary categorical variable indicating whether the restaurant offers dinner service. open_weekend: Binary categorical variable indicating whether the restaurant is open on weekends. Other supporting variables (high cardinality) These variables may be useful for displaying detailed or contextual information, but they are not particularly suitable as main metrics or global filters: name: Name of the establishment. description: Short description of the restaurant. phone: Contact phone number. address, city, postal_code: Location information. food_sample: Categorical variable with examples of typical dishes from the restaurant. menu: Categorical variable with types of menus available (126 different values), which could be leveraged after a cleaning process. keywords: Categorical variable with descriptive keywords (279 values), requiring transformation for analytical use. comments: Free text containing user comments. It has high semantic value but requires more advanced techniques (e.g., text analysis) for exploitation.