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Restaurant Recommender ( CS-F407 Artificial Intelligence Lab Project )

Submitted to Dr. Aneesh Chivukula

Group details

Aman Agarwal : 2020B4AA2328H
Gauri Tewari : 2020B4A32314H
Kartik CHitoor : 2020B4A81617H

The project makes use of Machine Learning techniques to recommend ten restaurants to a user living in Hyderabad, India, based on the input. It takes input from the user for the following attributes:

Restaurant Type ( Casual Dining, Cafe, Quick Bites etc.)

Cuisines (North Indian, Italian, Continental etc.)

Approximate Cost for two people (Cheap, Affordable, Expensive etc.)

Location (Multiple locations of Hyderabad like Jubilee Hills, BITS Hyderabad, Secunderabad etc.)

It then applies TF-IDF (Term Frequency-Inverse Document Frequency) and Cosine Similarity to find similarity scores for all the restaurants present in the dataset, i.e. 51,717 spread across the city of Hyderabad.

Then the output is given in the form of the top 10 restaurants with multiple other fields like

Address

Online orders available

Rating (out of 5)

Dishes liked

The report for the same is made in CRISP-DM Methodology and is available in both .docx and .pdf format.

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