Restaurant Revenue Prediction

Restaurant Revenue Prediction에 대해서 아봅니다.

 

각 데이터가 의미하는 것은 아래와 같습니다.

 

File descriptions

• train.csv - the training set. Use this dataset for training your model. 
• test.csv - the test set. To deter manual "guess" predictions, Kaggle has supplemented the test set with additional "ignored" data. These are not counted in the scoring.
• sampleSubmission.csv - a sample submission file in the correct format

Data fields

• Id : Restaurant id. 
• Open Date : opening date for a restaurant
• City : City that the restaurant is in. Note that there are unicode in the names. 
• City Group: Type of the city. Big cities, or Other. 
• Type: Type of the restaurant. FC: Food Court, IL: Inline, DT: Drive Thru, MB: Mobile
• P1, P2 - P37: There are three categories of these obfuscated data. Demographic data are gathered from third party providers with GIS systems. These include population in any given area, age and gender distribution, development scales. Real estate data mainly relate to the m2 of the location, front facade of the location, car park availability. Commercial data mainly include the existence of points of interest including schools, banks, other QSR operators.
• Revenue: The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis. Please note that the values are transformed so they don't mean real dollar values.

 

 

 

 

 

 

 

 

 

참고

[1] - https://github.com/cosmicudemy/ML_Casestudies