Predicting-Food-Delivery-Time
MachineHack Hackathon by IMS Proschool
Analytics India Magazine and IMS Proschool bring to you ‘Predicting Predicting Food Delivery Time Hackathon’.
Size of training set: 11,094 records
Size of test set: 2,774 records
FEATURES:
Restaurant: A unique ID that represents a restaurant.
Location: The location of the restaurant.
Cuisines: The cuisines offered by the restaurant.
Average_Cost: The average cost for one person/order.
Minimum_Order: The minimum order amount.
Rating: Customer rating for the restaurant.
Votes: The total number of customer votes for the restaurant.
Reviews: The number of customer reviews for the restaurant.
Delivery_Time: The order delivery time of the restaurant. (Target Classes)
Data Observations and Processing
- From the above features we dropped the Restaurant and Location columns.
- There were a total of 101 cuisines with mutli-labelled values.
- We noticed that the 8 cuisines was the maximum number used to describe few of the rows, hence we introduced 8 columns to represent the cuisines.
- For rows which which had less than 8 cuisines the remaining values were stored as nan which we labelled as None.
- For newly opened resturants and resturants with blank we converted the value to 0.
- The data was also scaled.
Training and Testing
In Final Predctions, we tired XGBoostClassifier and RandomForestClassifier but the accuracy wasn't very good on the test data.
Therefore, in Deep Learning, we used a neural network to predict the time taken for delivery.