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This dataset offers a fascinating glimpse into the culinary landscape of the world, featuring a wide array of recipes sourced from different cultures and traditions. It provides a structured collection of information, including recipe names, their originating cuisines, a detailed list of ingredients, preparation and cooking times, serving sizes, estimated calories per serving, and associated dietary restrictions.

• recipe_name: The name of the recipe. (Text/String)
• cuisine: The geographical or cultural origin of the recipe. (Categorical/String)
• ingredients: A list of ingredients required for the recipe. (List/String - often needs parsing)
• cooking_time_minutes: The estimated time required for cooking the dish in minutes. (Numerical/Integer)
• prep_time_minutes: The estimated time required for preparing the ingredients in minutes. (Numerical/Integer)
• servings: The number of people the recipe is intended to serve. (Numerical/Float)
• calories_per_serving: The estimated number of calories in one serving of the dish. (Numerical/Float)
• dietary_restrictions: Any specific dietary categories the recipe falls under (e.g., vegetarian, vegan, gluten-free, dairy-free, nut-free). A single recipe can have multiple restrictions, often listed as a comma-separated string. (Categorical/String)

Context

Cheese is nutritious food made mostly from the milk of cows but also other mammals, including sheep, goats, buffalo, reindeer, camels and yaks. Around 4000 years ago people have started to breed animals and process their milk. That's when the cheese was born.

Content

Geography: Global

Time period: 2024

Unit of analysis: Global Cheese Dataset

Explore this site to find out about different kinds of cheese from all around the world.

248 cheeses have listed fat content. Is there a relationship between fat content and cheese type? What about texture, flavor, or aroma?

Variables

Acknowledgements

Datasource: Cheese.com

Inspiration: Cheese Blog

The dataset is designed to assist in predicting recommended medications for patients based on their fever condition, symptoms, medical history, and other relevant factors. It incorporates a mix of patient health data, environmental variables, and lifestyle choices to improve model accuracy and better simulate real-world scenarios.

Dataset Characteristics: Total Samples: 1000 (modifiable based on user needs). Number of Features: 19 features + 1 target column. File Format: CSV (enhanced_fever_medicine_recommendation.csv). Features Description: Column Name Description Data Type Temperature Body temperature of the patient in Celsius (e.g., 36.5 - 40.0). Float Fever_Severity Categorized fever severity: Normal, Mild Fever, High Fever. Categorical Age Age of the patient (1-100 years). Integer Gender Gender of the patient: Male or Female. Categorical BMI Body Mass Index of the patient (e.g., 18.0 - 35.0). Float Headache Whether the patient has a headache: Yes or No. Categorical Body_Ache Whether the patient has body aches: Yes or No. Categorical Fatigue Whether the patient feels fatigued: Yes or No. Categorical Chronic_Conditions If the patient has any chronic conditions (e.g., diabetes, asthma): Yes or No. Categorical Allergies If the patient has any allergies to medications: Yes or No. Categorical Smoking_History If the patient has a history of smoking: Yes or No. Categorical Alcohol_Consumption If the patient consumes alcohol: Yes or No. Categorical Humidity Current humidity level in the patient’s area (e.g., 30-90%). Float AQI Current Air Quality Index in the patient’s area (e.g., 0-500). Integer Physical_Activity Daily physical activity level: Sedentary, Moderate, Active. Categorical Diet_Type Diet preference: Vegetarian, Non-Vegetarian, or Vegan. Categorical Heart_Rate Resting heart rate of the patient in beats per minute (e.g., 60-100). Integer Blood_Pressure Blood pressure category: Normal, High, or Low. Categorical Previous_Medication Medication previously taken by the patient: Paracetamol, Ibuprofen, Aspirin, or None. Categorical Recommended_Medication Target variable indicating the recommended medicine: Paracetamol or Ibuprofen. Categorical

Proportion of the 2020 US human population who could be fed with food energy savings associated with vegan diets.

Context I was always fascinated by the food culture of Bengaluru. Restaurants from all over the world can be found here in Bengaluru. From United States to Japan, Russia to Antarctica, you get all type of cuisines here. Delivery, Dine-out, Pubs, Bars, Drinks,Buffet, Desserts you name it and Bengaluru has it. Bengaluru is best place for foodies. The number of restaurant are increasing day by day. Currently which stands at approximately 12,000 restaurants. With such an high number of restaurants. This industry hasn't been saturated yet. And new restaurants are opening every day. However it has become difficult for them to compete with already established restaurants. The key issues that continue to pose a challenge to them include high real estate costs, rising food costs, shortage of quality manpower, fragmented supply chain and over-licensing. This Zomato data aims at analysing demography of the location. Most importantly it will help new restaurants in deciding their theme, menus, cuisine, cost etc for a particular location. It also aims at finding similarity between neighborhoods of Bengaluru on the basis of food. The dataset also contains reviews for each of the restaurant which will help in finding overall rating for the place.

Content The basic idea of analyzing the Zomato dataset is to get a fair idea about the factors affecting the establishment of different types of restaurant at different places in Bengaluru, aggregate rating of each restaurant, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry has’nt been saturated yet and the demand is increasing day by day. Inspite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don’t have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. What kind of a food is more popular in a locality. Do the entire locality loves vegetarian food. If yes then is that locality populated by a particular sect of people for eg. Jain, Marwaris, Gujaratis who are mostly vegetarian. These kind of analysis can be done using the data, by studying the factors such as • Location of the restaurant • Approx Price of food • Theme based restaurant or not • Which locality of that city serves that cuisines with maximum number of restaurants • The needs of people who are striving to get the best cuisine of the neighborhood • Is a particular neighborhood famous for its own kind of food.

“Just so that you have a good meal the next time you step out”