Restaurant-Recommander-System

Implémentation d'un système de recommandation de restaurants à partir de jeux de données d'une application de livraison de plats cuisinés. Ce projet s'inscris dans le cours de Systèmes de recommandation encadré par Oscar de Felice

Les données sont disponibles ici

Assignement

The objective of this project is to build a recommendation engine to predict what restaurants customers are most likely to order from given the customer location, restaurant information, and the customer order history.

You should imagine that this is a client project and that you are working with a team of data scientists to build a recommendation engine. You will be expected to present your work to the client and to the data science team.

This solution will allow your client, an app-based food delivery service, to customise restaurant recommendations for each of their customers and ensure a more positive overall user experience.

Data

There are ~$10,000$ customers in the test set. These are the customers you will need to recommend a vendor to. Each customer can order from multiple locations, identified by the variable LOC_NUM.

There are ~$35,000$ customers in the train set. Some of these customers have made orders at at least one of $100$ vendors.

As said, the aim of this project is to build a recommendation engine to predict what restaurants customers are most likely to order from, given the customer location, the restaurant, and the customer order history.

Data files are available at this link and are structured as follows:

• test_customers.csv- customer id’s in the test set.
• test_locations.csv - latitude and longitude for the different locations of each customer.
• train_locations.csv - customer id’s in the test set.
• train_customers.csv - latitude and longitude for the different locations of each customer.
• orders.csv - orders that the customers train_customers.csv from made.
• vendors.csv - vendors that customers can order from.
• VariableDefinitions.txt - Variable definitions for the datasets
• SampleSubmission.csv - is an example of what your submission file should look like. The order of the rows does not matter, but the names of CID X LOC_NUM X VENDOR must be correct. The column "target" is your prediction.

Evaluation

The error metric for this competition is the $F_1$ score, which ranges from 0 (total failure) to 1 (perfect score). Hence, the closer your score is to $1$, the better your model.

• F1 Score: A performance score that combines both precision and recall. It is a harmonic mean of these two variables. The formula is given as:

$$F_1 = 2 \frac{PR}{P + R}, .$$

• Precision: This is an indicator of the number of items correctly identified as positive out of total items identified as positive. The formula is given as:

$$P = \frac{TP}{TP+FP}, .$$

• Recall / Sensitivity / True Positive Rate (TPR): This is an indicator of the number of items correctly identified as positive out of total actual positives. The formula is given as:

$$R = \frac{TP}{TP+FN}, .$$

Where:

TP=True Positive FP=False Positive TN=True Negative FN=False Negative

Your submission file should look like:

CID X LOC_NUM X VENDOR     target
A7B8IGM X 0 X 105            0
NS70FA9 X 0 X 105            1
WTWOE69 X 0 X 105            0

Where $1$ indicates that a customer will order from that restaurant and $0$ that they will not order from that restaurant. e e e e e