The objective is to predict food preparation time from ordered food items and quantity. This is a data challenge arranged by SkipTheDishes, Canada’s leading and largest food delivery company. The data includes upto 10 ordered food items along with the quantity. There are 20 features and 80,000 samples in the data. The goal is to predict food preparation time.

• Doc2Vec has been applied to vectorize food item names and feature engineering.
• RandomForest is used to predict food preparation time.
• Grid search is used for hyperparameter optimization.
• The baseline performance is R2: 0.61, MAE: 5.45, RMSE: 6.98.
• The performance achieved is R2:0.75, MAE: 4.41, RMSE: 5.56.

This Project’s GitHub Repository

• Doc2Vec
• Regression