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Food-Recmmendation-System-Python

This is the Recommendation Engine that will be used in building the Lunchbox App, a platform for ordering food and keeping track of user expenditure and canteen sales. Regardless of whether or not this is actually implemented in all the canteens of IIT Kanpur (given the potential for frauds & cyber-attacks) I will still complete the platform.

Also, I would be open-sourcing the app so that any campus can implement a cash-less & integrated system of ordering food across their whole campus. After all, what good are IITs for if our canteens still keep track of student accounts on paper registers!

Build instructions

  • git clone https://github.com/gsunit/Food-Recommendation-System-Pyhton.git
  • Run the Jupyter Notebook src.ipynb

Demographic Filtering

Suggesting the items that are well-received and popular among the users. Most trending items and items with the best rating rise to the top and get shortlisted for recommendation.

import pandas as pd 
import numpy as np

# Importing db of food items across all canteens registered on the platform
df1=pd.read_csv('./db/food.csv')
df1.columns = ['food_id','title','canteen_id','price', 'num_orders', 'category', 'avg_rating', 'num_rating', 'tags']

df1
food_id title canteen_id price num_orders category avg_rating num_rating tags
0 1 Lala Maggi 1 30 35 maggi 3.9 10 veg, spicy
1 2 Cheese Maggi 1 25 40 maggi 3.8 15 veg
2 3 Masala Maggi 1 25 10 maggi 3.0 10 veg, spicy
3 4 Veg Maggi 1 30 25 maggi 2.5 5 veg, healthy
4 5 Paneer Tikka 1 60 50 Punjabi 4.6 30 veg, healthy
5 6 Chicken Tikka 1 80 40 Punjabi 4.2 28 nonveg, healthy, spicy

Results of demographic filtering

top_rated_items[['title', 'num_rating', 'avg_rating', 'score']].head()
pop_items[['title', 'num_orders']].head()
title num_rating avg_rating score
4 Paneer Tikka 30 4.6 4.288889
5 Chicken Tikka 28 4.2 4.013953
1 Cheese Maggi 15 3.8 3.733333
title num_orders
4 Paneer Tikka 50
1 Cheese Maggi 40
5 Chicken Tikka 40
0 Lala Maggi 35
3 Veg Maggi 25

Content Based Filtering

A bit more personalised recommendation. We will analyse the past orders of the user and suggest back those items which are similar.

Also, since each person has a "home canteen", the user should be notified of any new items included in the menu by the vendor.

We will be use Count Vectorizer from Scikit-Learn to find similarity between items based on their title, category and tags. To bring all these properties of each item together, we create a "soup" of tags. "Soup" is a processed string correspnding to each item, formed using the constituents of tags, tile and category.

food_id title canteen_id price num_orders category avg_rating num_rating tags soup
0 1 Lala Maggi 1 30 35 maggi 3.9 10 veg, spicy veg spicy lala maggi
1 2 Cheese Maggi 1 25 40 maggi 3.8 15 veg veg cheese maggi
2 3 Masala Maggi 1 25 10 maggi 3.0 10 veg, spicy veg spicy masala maggi

Using CountVectorizer from Scikit-Learn

# Import CountVectorizer and create the count matrix
from sklearn.feature_extraction.text import CountVectorizer
count = CountVectorizer(stop_words='english')

# df1['soup']
count_matrix = count.fit_transform(df1['soup'])

# Compute the Cosine Similarity matrix based on the count_matrix
from sklearn.metrics.pairwise import cosine_similarity
cosine_sim = cosine_similarity(count_matrix, count_matrix)

Sample Recommendation

df1.loc[get_recommendations(title="Paneer Tikka")]
food_id title canteen_id price num_orders category avg_rating num_rating tags soup
5 6 Chicken Tikka 1 80 40 Punjabi 4.2 28 nonveg, healthy, spicy nonveg healthy spicy chicken tikka punjabi
3 4 Veg Maggi 1 30 25 maggi 2.5 5 veg, healthy veg healthy maggi

After all the hard work, we finally get the recommendations

personalised_recomms(orders, df1, current_user, columns)
get_new_and_specials_recomms(new_and_specials, users, df1, current_canteen, columns)
get_top_rated_items(top_rated_items, df1, columns)
get_popular_items(pop_items, df1, columns).head(3)
title canteen_id price comment
0 Veg Maggi 1 30 based on your past orders
1 Paneer Tikka 1 60 based on your past orders
2 Chicken Tikka 1 80 based on your past orders
title canteen_id price comment
0 Cheese Maggi 1 25 new/today's special item in your home canteen
title canteen_id price comment
0 Paneer Tikka 1 60 top rated items across canteens
1 Chicken Tikka 1 80 top rated items across canteens
2 Cheese Maggi 1 25 top rated items across canteens
title canteen_id price comment
0 Paneer Tikka 1 60 most popular items across canteens
1 Cheese Maggi 1 25 most popular items across canteens
2 Chicken Tikka 1 80 most popular items across canteens

These are just simple algorithms to make personalised & general recommendations to users. We can easily use collaborative filtering or incorporate neural networks to make our prediction even better. However, these are more computationally intensive methods. Kinda overkill, IMO! Let's build that app first, then move on to other features!

Star the repository and send in your PRs if you think the engine needs any improvement or help me implement some more advanced features.