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A Complete Guide to Cracking The Predicting Restaurant Food Cost Hackathon By MachineHack

A Complete Guide to Cracking The Predicting Restaurant Food Cost Hackathon By MachineHack

We all love food. And it is only normal to have a craving for one of your favourite foods from a special restaurant that we all love to have at least once in a month. However, there is a strong factor that will make us reconsider going back to that special restaurant cost.  

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Machinehack’s Predicting Restaurant Food Cost Hackathon lets all Data Science enthusiasts to play with data collected from various sources, which includes the price information of thousands of restaurants across India. The contestants will predict the cost of a meal for different restaurants across the country based on various features.

About the Data Set

The hackathon is about predicting the average price for a meal. The data consists of the following features.

Size of training set: 12,690 records

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Size of test set: 4,231 records

Columns/Features :

TITLE: The feature of the restaurant which can help identify what and for whom it is suitable for.

RESTAURANT_ID: A unique ID for each restaurant.

CUISINES: The variety of cuisines that the restaurant offers.

TIME: The open hours of the restaurant.

CITY: The city in which the restaurant is located.

LOCALITY: The locality of the restaurant.

RATING: The average rating of the restaurant by customers.

VOTES: The overall votes received by the restaurant.

COST: The average cost of a two-person meal.

Click here to participate in the hackathon.

Solving Predicting Restaurant Food Cost Hackathon By Machnehack

Use the following links to our top tutorials to help you with this challenge:

  1. Flight Ticket Price Prediction Hackathon: Use These Resources To Crack Our MachineHack Data Science Challenge
  2. Hands-on Tutorial On Data Pre-processing In Python
  3. Data Preprocessing With R: Hands-On Tutorial
  4. Getting started with Linear regression Models in R
  5. How To Create Your first Artificial Neural Network In Python
  6. Getting started with Non Linear regression Models in R
  7. Beginners Guide To Creating Artificial Neural Networks In R


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