Diet Recommendation System
Submitted in partial fulfillment of the requirements of
Summer Internship Program 2022-23
By
Akansh Jatav 21CE2050
Aditya Wani 21CE1377
Prachiti Chavan 21CE1344
Shreya Kumar 21CE1322
Supervisor
Mrs. Sumithra T. V.
Department of Computer Engineering
Ramrao Adik Institute of Technology,
Sector 7, Nerul, Navi Mumbai
(Under the ambit of D. Y. Patil Deemed to be University)
July 2023
Ramrao Adik Institute of Technology
(Under the ambit of D. Y. Patil Deemed to be University)
Dr. D. Y. Patil Vidyanagar, Sector 7, Nerul, Navi Mumbai 400 706.
Certificate
This is to certify that, the Internship project entitled
“Diet Recommendation System”
is a bonafide work done by
Akansh Jatav 21CE2050
Aditya Wani 21CE1377
Prachiti Chavan 21CE1344
Shreya Kumar 21CE1322
and is submitted in the partial fulfillment of the requirement for the Summer
Internship Program 2022-23
Supervisor
Mrs. Sumithra T. V.
Dr. Dhananjay Dakhane
Internship Coordinator
Dr. Amarsinh Vidhate
Head of Department
Dr. Mukesh D. Patil
Principal
Abstract
People from all around the world are getting more concerned in their health and
way of life in today's modern environment. However, avoiding junk food and
exercising alone are insufficient; we also need to eat a balanced diet. We can
live a healthy life with a balanced diet based on our height, weight, and age. A
food recommendation engine using a content-based approach is an important
tool for promoting healthy eating habits. This type of engine uses information
about the nutritional content and ingredients of foods to make personalized
recommendations to users. One of the key advantages of a content-based
approach is that it considers an individual's dietary restrictions and preferences,
such as allergies or food preferences. By providing users with tailored
recommendations, a content-based food recommendation engine can help them
make better choices about what to eat and improve their overall health.
Contents
1.
Introduction
1.1 Overview
1.2 Motivation
1.3 Problem Statement and Objectives
2.
Literature Survey
2.1 Survey of Existing System
2.2 Limitations of Existing System
3.
Proposed System
3.1 Problem Statement
3.2 Proposed Methodology / Techniques
3.3 System Design
3.4 Details of Hardware and Software Requirements
4.
Results and Discussion
4.1 Implementation Details
4.2 Project Outcomes
5.
Conclusion
References
Chapter 1
Introduction
1.1 Overview
There are many factors known that influence an individual’s health. Physical exercise,
sleeping, nutrition, heredity, pollution, among other external factors. Being nutrition one
of the biggest modifiable factors in our lives, it is not surprising that small changes can
induce significant outcomes. Our diet system is based on Indian cuisine. Indian cuisine
consists of a variety of regional and traditional cuisines native to India. Given the
diversity in soil, climate, culture, ethnic groups, and occupations, these cuisines vary
substantially and use locally available spices, herbs, vegetables, and fruits. Therefore, it
is essential that we accommodate this dietary diversity into our diet system. It is highly
likely that affordability is a major barrier to improving diets in India. However, our diet
system mainly focuses on the most used ingredients in India which overcomes this
problem. Some molecules are known to have a positive effect on health, namely, in
fighting diseases. Being able to identify which ingredients contain the higher
concentrations may help us treating and preventing them. Moreover, by including these
ingredients in tasty and affordable meals, it can promote a shift in the nutritional habits
of the population.
1.2 Motivation
Dietary guidelines are a translation of scientific knowledge on nutrients into specific
dietary advice. They represent the recommended dietary allowances of nutrients in terms
of diets that should be consumed by the population. The guidelines promote the concept
of nutritionally adequate diets and healthy lifestyles from the time of conception to old
age. Formulation of dietary goals and specific guidelines would help in providing
required guidance to people in ensuring nutritional adequacy. The dietary guidelines
could be directly applied for general population or specific physiological or high risk
groups to derive health benefits. They may also be used by medical and health personnel,
nutritionists and dietitians.
1.3 Problem Statement and Objectives
Dietary quality is recognized as a key factor affecting human nutritional status, and
empirical studies have linked dietary diversity to micro-nutrient adequacy and dietary
risk factors to morbidity and mortality from non-communicable diseases. The major
food issues of concern are insufficient/ imbalanced intake of foods/nutrients. The
common nutritional problems of public health importance in India are low birth weight,
protein energy malnutrition in children, chronic energy deficiency in adults, micronutrient malnutrition and diet-relate noncommunicable diseases. However, diseases at
the either end of the spectrum of malnutrition (under and over-nutrition) are important.
Recent evidence indicate that undernutrition in utero may set the pace for diet-related
chronic diseases in later life.
With undernutrition, overnutrition, and micronutrient deficiencies afflicting the
country, India experiences a triple burden of malnutrition. Recent decades have seen
modest progress when it comes to health in India, but progress has been uneven and
inequitable. Therefore this Project’s aim is to recommend recipes to the user based on
their nutrient levels which is a suitable solution for the malnutrition cases in India.
Chapter 2
Literature Survey
Nutrition is a basic human need and a prerequisite to a healthy life. A proper diet is
essential from the very early stages of life for proper growth, development and to
remain active. Food consumption, which largely depends on production and
distribution, determines the health and nutritional status of the population. The
recommended dietary allowances (RDA) are nutrient-centered and technical in nature.
Apart from supplying nutrients, foods provide a host of other components (non-nutrient
phytochemicals) which have a positive impact on health. Since people consume food, it
is essential to advocate nutrition in terms of foods, rather than nutrients. Emphasis has,
therefore, been shifted from a nutrient orientation to the food-based approach for
attaining optimal nutritional status. Dietary guidelines are a translation of scientific
knowledge on nutrients into specific dietary advice.
They represent the recommended dietary allowances of nutrients in terms of diets that
should be consumed by the population. The guidelines promote the concept of
nutritionally adequate diets and healthy lifestyles from the time of conception to old
age.
2.1 Survey of Existing System
As observed in many online websites, diet plans are being recommended on the basis of
BMI. Body mass index (BMI) is a measure of body fat based on height and weight that
applies to adult men and women. However, according to an October 24 2022 article in
the Montreal Gazette, criticism is growing that BMI—a formula that uses height and
weight to measure body fat—is a “flawed, crude, archaic and overrated proxy for
health.” Those opposed to its continued use argue that it was developed based on white
males and has little validity for other racial and ethnic groups, and that it is sometimes
used to deny certain people joint replacements and other surgeries. Experts have also
pointed out that BMI fails to take into account factors such as how much fat versus
muscle a patient has, the distribution of fat in their body (typically, fat around the waist
increases disease risk more than fat in other places), and their metabolic health.
Some research suggests that other measures of body fat, such as skinfold thicknesses,
bioelectrical impedance, underwater weighing, and dual energy x-ray absorption, may
be more accurate than BMI. Yet, BMI is still being used as a diagnostic tool for body
fat measurement.
fig 2.1 BMI Calculators
fig 2.2 BMR calculator
A recent study published in the journal found out that changes in a person’s basal
metabolic rate (BMR) — essentially, the number of calories they burn at rest each day —
has quite a bit to do with both how well people lose weight and how easily it is to keep
the pounds from returning. Whether an individual aims to lose or gain weight, knowing
how many calories are required every day while at rest, and also once they factor in
physical activity is very important. A 2005 meta-analysis study on BMR* showed that
when controlling all factors of metabolic rate, there is still a 26% unknown variance
between people. Essentially, an average person eating an average diet will likely have
expected BMR values, but there are factors that are still not understood that determines
BMR precisely.
Therefore, all BMR calculations, even using the most precise methods through
specialists, will not be perfectly accurate in their measurements. Not all human bodily
functions are well understood just yet, so calculating total daily energy expenditure
(TDEE) derived from BMR estimates are just that, estimates.
Recipe Recommender Limitations:
•
Limited Personalization: Recipe recommenders typically rely on user inputs such
as dietary restrictions, preferences, and health goals. However, the
recommendations may not account for individual variations in nutritional needs,
food tolerances, or allergies.
•
Lack of Contextual Understanding: Recipe recommenders may not consider
cultural, regional, or seasonal factors that influence food choices and availability.
Additionally, they may not account for specific dietary approaches or ethical
considerations (e.g., vegetarianism, veganism) unless explicitly specified.
It's essential to consider these limitations when using BMI, BMR, or recipe
recommenders, that a diet recommendation system should be used as a supportive tool
and not as a substitute for professional medical advice or guidance from a registered
dietitian or nutritionist. Individual health conditions, allergies, and specific dietary
requirements should also be considered when implementing personalized dietary
recommendations.
2.2 Limitations of Existing System
Challenges regarding recommendation algorithms
In order to calculate nutritional recommendations for users, any algorithm needs the
following information:
1) User information (e.g., likes, dislikes, food consumption, or nutritional
needs): Similar to recommendation systems in other domains, food
recommendation systems also face with the cold-start problem when the system is
used in the first time .
This problem can be surmounted by using information about users’ previous
meals to calculate similarity and then recommend new recipes to users . However,
this solution requires many user efforts and abates the desire of system usage.
How many recipes the system should have? The quantity of gathered recipes
should be large enough to accommodate the preferences of many users and vary
the recommended recipes while still minimize the time for making
recommendations. This is a tricky problem when the system tries to balance
between the variety of recommendations and system response time.
2) A set of constraints or rules: Considering more constraints and rules in the
recommendation process will improve the quality of recommendations. For
instance, with a user who has heart disease, the system should recommend menus
with less fat and salt. Moreover, it is very necessary to detect the conflicts among
the constraints or rules which prevent the recommendation algorithms from
finding a solution. However, with the large database (e.g., thousands of
foods/recipes), checking constraints/rules in the database brings negative effects
for system performance . In addition, food recommendation systems should take
into account constraints with regard to the availability of ingredients in the
households for the purpose of helping users to save money and prevent the food
waste behavior. The challenge here is how to propose food which meets health
situations and nutritional needs of users, as well as taking advantages of the
ingredients that are currently in the fridge. In this scenario, recommendation
systems seems to require many efforts from users because users have to report the
consumption of all ingredients regularly and this can prevent users from using the
system permanently.
Challenges regarding changing eating behavior of users:
Nowadays, many people are suffering health problems because of inappropriate eating
habits. For instance, some people eat too much food compared to their physical activity
level and gradually become obese. Whereas, others (e.g., the elderly, the dieters) restrict
extremely nutrition intake and this leads to malnutrition.
Therefore, one of the main functions of food recommendation systems is understanding
users’ eating behaviors and persuading them to change eating behaviors in positive ways.
However, this is a big challenge for food recommendation systems because eating is a
lifelong behavior which is influenced by many factors, especially psychological factors.
Hence, food recommendation systems should integrate health psychology theory in order
to stimulate users to comply healthy eating behaviors. The first approach can be used by
applying one simple change at a specific time until the user behavior becomes habitual.
Another approach can be applied for food recommendation systems by comparing to the
ideal nutrient. Users can find the structure of ideal diet according to the age and physical
activity level from reliable resources (e.g., USDA, DACH) and then compare what food
they ate to what is recommended .
Chapter 3
Proposed System
The designed diet recommendation system provides a suitable Recipe containing a key
ingredient recommended by the system based on their nutritional requirements put by the user.
By providing users with tailored recommendations, a content-based food recommendation
engine can help them make better choices about what to eat and improve their overall health.
The web application uses content-based approach with Scikit-Learn, FastAPI and Streamlit.
3.1 Problem Statement
With under-nutrition, over-nutrition, and micro-nutrient deficiencies afflicting the country,
India experiences a triple burden of malnutrition. Recent decades have seen modest progress
when it comes to health in India, but progress has been uneven and inequitable. Hence the need
of the hour is to create a diet recommendation system that is based on the nutrient levels so that
the recipes suit every body’s nutritional needs.
3.2 Proposed methodology / Techniques
The nutrients considered by the system to recommend a diet are Calcium, Fats, Fiber, Carbon,
Sodium, Thymine, Riboflavin, Niacin, Pantothenic acid, Biotin, Vitamin B6, Vitamin C and
Protein. Our model is a content-based recommendation engine. A content-based
recommendation engine is a type of recommendation system that uses the characteristics or
content of an item to recommend similar items to users. It works by analyzing the content of
items, such as text, images, or audio, and identifying patterns or features that are associated
with certain items. These patterns or features are then used to compare items and recommend
similar ones to users.
Why content-based approach?
•
No data from other users is required to start making recommendations.
•
Recommendations are highly relevant to the user.
•
Recommendations are transparent to the user.
•
You avoid the “cold start” problem.
•
Content-based filtering systems are generally easier to create.
There were 2 data sets used:
1. ifct2017: The Indian Food Composition tables provides nutritional values for 528 key
ingredients.
2. Cleaned_Indian_Food_Dataset – data set of all Indian food items with their recipes.
The web application is built using HTML, CSS and JavaScript.
To translate the paper working to our website we used the unsupervised implementation
of Nearest Neighbors algorithm as it does exactly what our requirement is for the
website. The unsupervised implementation of nearest neighbor search refers to finding the
nearest neighbors of a given data point in a dataset without utilizing any labeled target
information.
It is primarily used for tasks such as clustering, anomaly detection, and data exploration.
The algorithm stores the entire training dataset in memory and makes predictions based
on the similarity of new data points to the existing training data. The k-nearest neighbors
(KNN) algorithm is a simple and popular machine learning algorithm used for both classification
and regression tasks. It is a type of instance-based or memory-based learning, where the
algorithm stores the entire training dataset in memory and makes predictions based on the
similarity of new data points to the existing training data.
Here's an overview of the KNN algorithm:
1. Training Phase:
•
The KNN algorithm starts by storing the feature vectors and corresponding
labels of the training dataset.
•
The algorithm does not learn explicit parameters or construct a model during the
training phase.
2. Prediction Phase:
•
Given a new, unseen data point, the KNN algorithm calculates the distance
between this point and all points in the training dataset.
•
The distance metric can be Euclidean distance, Manhattan distance, or any other
appropriate distance measure.
•
The algorithm selects the k nearest neighbors (data points) to the new point
based on the calculated distances.
•
For classification, the algorithm assigns the class label that is most frequent
among the k nearest neighbors.
•
For regression, the algorithm calculates the average (or weighted average) of the
labels of the k nearest neighbors.
3. Choosing the Value of k:
•
The choice of the parameter k, the number of neighbors to consider, is important
in the KNN algorithm.
•
A small value of k can be sensitive to noise or outliers in the data, potentially
resulting in overfitting.
•
A large value of k may smooth out the decision boundaries, potentially leading to
underfitting.
•
The optimal value of k depends on the dataset and should be determined through
experimentation and validation.
4. Scaling Features:
•
Before applying KNN, it is often necessary to scale or normalize the features of
the dataset. This is important because KNN makes predictions based on the
distances between data points, and if the features have different scales, some
features may dominate the distance calculation.
The KNN algorithm is relatively easy to understand and implement, however, it can be
computationally expensive for large datasets since it requires calculating distances
between the new point and all training points. Additionally, careful consideration of
feature scaling and choosing the appropriate value of k is crucial for optimal performance.
In scikit-learn (sklearn), the sklearn.neighbors module provides various algorithms and
tools for nearest neighbor-based learning tasks. Within this module, the sklearn.neighbors
class implements the k-nearest neighbors algorithm, which is a popular method for
classification and regression tasks. The main purpose of sklearn.neighbors is to perform
nearest neighbor-based computations, such as finding the k-nearest neighbors to a given
data point or making predictions based on the neighbors' labels or values.
In our case we return the ingredients which match the closest nutritional requirements.
The fit() function is used to train the model by storing the training dataset in memory. It
takes the feature vectors (X) and corresponding labels (y) as input. It stores this
information in the internal memory of the KNN model.
Then, we use the kneighbors() function. The kneighbors() method is a function provided
by scikit-learn's KNeighborsClassifier and KNeighborsRegressor classes. It is used to find
the k nearest neighbors of a given query data point(s) in a trained KNN model. The
method returns the indices and distances of the k nearest neighbors.
User
Inputs
•
•
Physical Metrics
Nutritional deficiency
Metrics
Processing
Using the KNN algorithm to
find 5 nearest matching
recipe ingredients
Word processing
Tokenising the ingredient and
searching the ingredient in the
cleaned Indian recipes database
Output
List of ingredients and recipes
3.3 System Design
Our model extracts ingredients from the data set based on the nutritional deficiencies stated in
the input.
User flow: User's will request to system by providing their physical information and after
analyzing the data as a response the system (ML model) will recommend a diet based on the
user information accordingly.
System Architecture
1. User's will enter their nutritional deficiency label on the website.
2. The information will then go through the ML model in following manner:
2.1 KNN is used to find the ingredients fitting the nutritional requirements of the user
3. The System will then recommend diet to the users.
‘
The Indian Diet recommendation system successfully recommends Indian ingredients to the users based
on the nutirient values given to it as the inputs.
Modules that have been used in Python are:
1) Scipy and Pylab from NumPy: NumPy is a library for the Python programming language
that adds support for big, multidimensional arrays and matrices as well as a wealth of high
level mathematical operations that can be performed on these arrays. Pylab is a module in
the matplotlib library that provides a MATLAB-like interface to create plots and visualizations in Python
2) Pandas: pandas is a software library written for the Python programming language for data
manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
3) Matplotlib: Matplotlib is a cross-platform, data visualization and graphical plotting library
for Python and its numerical extension NumPy. It provides an object-oriented API for embedding plots into applications.
4) StandardScaler from sklearn.preprocessing: An open-source Python library called scikitlearn uses a unified interface to implement a variety of machine learning, pre-processing,
cross-validation, and visualization algorithms. The data values are standardized into a standard format using the StandardScaler() function.
scaler=StandardScaler()
prep_data=scaler.fit_transform(extracted_data.iloc[:,3:17].to_numpy())
Where,
•
‘scaler’ is the StandardScaler() object initialized
•
‘fit_transform()’ combines the fit() and transform() methods into a single step, allowing you to fit the transformer on the data and transform the data in one call.
•
‘to_numpy()’ converts a pandas DataFrame or Series object into a NumPy array.
5) NearestNeighbors from sklearn.neighbors: the NearestNeighbors class provides an unsupervised implementation of nearest neighbor search. It allows you to find the k-nearest neighbors or neighbors within a specified radius for a given data point or set of data points.
neigh = NearestNeighbors(metric='cosine',algorithm='brute')
neigh.fit(prep_data)
where,
•
‘neigh’ is an object of the NearestNeighbors class using the cosine metric. Metric to
use for distance computation.
cos(theta) = (A * B) / (||A|| * ||B||)
•
The fit() function is used to train the model by storing the training dataset in
memory. It takes the feature vectors (X), ‘prep_data’ in this case and stores
data in internal memory of the knn model.
6) From sklearn pipeline() and sklearn.preprocessing FunctionTrasnformer: The Pipeline function in scikit-learn is a utility class that allows you to chain multiple transformers and an estimator into a single object.
It simplifies the process of creating and applying a sequence of data transformations followed by a final estimator, especially in machine learning pipelines with multiple preprocessing steps.
transformer = FunctionTransformer(neigh.kneighbors,kw_args={'return_distance':False})
pipeline=Pipeline([('std_scaler',scaler),('NN',transformer)])
•
Where ‘transformer’ is an object of the FunctionTransformer class taking in the
kneighbors function as a parameter
•
And the pipeline taking the scaler and the transformer as paremeters.
7) train_test_split from sklearn.model_selection: train_test_split has been imported. The
train_test_split() method is used to split our data into train and test sets.
from sklearn.model_selection import train_test_split
TrainSet, TestSet = train_test_split(extracted_data,test_size=0.2, random_state=42)
Where, ‘train_test_split()’ is the splitter, test_size is the size of the test set, random_state is the
seed for the randomizer.
3.4 Details of Hardware and Software Requirements
The project is created with:
•
•
•
•
•
•
•
•
•
Python: 3.10.8
scikit-learn 1.1.3
Pandas: 1.5.1
Streamlit: 1.16.0
NumPy: 1.21.5
beautifulsoup4 4.11.1
Tensorflow 2.13
NLTK: Natural Language Toolkit
Matplotlib
Software used:
Visual Studio Code: Microsoft created Visual Studio Code, popularly known as VS Code, a C#
editor for Windows, Linux, and macOS that uses the Electron Framework. Debugging support,
syntax highlighting, intelligent code completion, snippets, code refactoring, and integrated Git
are among the features. The theme, keyboard shortcuts, options, and extensions that offer more
functionality may all be changed by users.
Python (Version-3.10.9): A high-level, all-purpose programming language is Python. Code
readability is prioritized in its design philosophy, which makes heavy use of indentation. Python
has garbage collection and dynamic typing. It supports a variety of programming paradigms,
including procedural, object-oriented, and functional programming as well as structured
programming (especially this).
HyperText Markup Language (HTML): The preferred markup language for texts intended to be
viewed via a web browser is HTML. Technologies like Cascading Style Sheets (CSS) and
programming languages like JavaScript can help. HTML documents are downloaded from a
web server or local storage by web browsers, who then turn them into multimedia web pages.
HTML initially featured cues for the document's design and semantically explains the structure
of a web page.
Cascading Style Sheets (CSS): A style sheet language called CSS is used to describe how a page
presented whether written in a markup language like HTML or XML (including XML dialects
like SVG, MathML, or XHTML). The World Wide Web's foundational technologies, along with
HTML and JavaScript, include CSS. The purpose of CSS is to make it possible to separate
content from presentation, including layout, colors, and fonts.
Hardware used:
Laptop: A laptop or notebook is a compact, transportable personal computer (PC), usually
referred to as a laptop or notebook for short. Although 2-in-1 PCs with a detachable keyboard
are frequently marketed as laptops or as having a "laptop mode," most laptops have a clamshell
design with a flat panel screen (typically 11-17 in or 280-430 mm in diagonal size) on the inside
of the upper lid and an alphanumeric keyboard (typically QWERTY) on the inside of the lower
lid.
Wi-Fi: The IEEE 802.11 family of standards, which is frequently used for wireless networking
and enables neighboring devices to exchange data through radio waves, provides the foundation
for the Wi-Fi family of wireless network protocols. In public spaces like coffee shops, hotels,
libraries, and airports, wireless local area networks (WLAN) are frequently used to link laptop
computers, tablet computers, smartphones, smart televisions (smart TVs), printers, and smart
speakers together as well as to a wireless router to connect them to the Internet.
Chapter 4
Results and Discussion
There are many different ways to build recommending systems, some use algorithmic and
formulaic approaches like Page Rank while others use more model-centered approaches like
collaborative filtering, content based, link prediction, etc. We have used model-centered
approaches to build our project. Our website can be referred only after the user gets a certified
medical report.
The model accepts complete as well as partial nutrient requirement ranges.
Input: complete range from a randomly selected test input from train_test_split
Output:
input: [.01,.03], For thiamin and riboflavin the range is 10: 12
Output:
As stated before, we have built a user friendly website using Bootstrap and JavaScript.
Fig 4.1 Home page of website
We present the user with 2 options, a daily calorie calculator and a form to customize a diet
plan. The daily calorie calculator is a BMR takes the age, gender, height and weight as input and
returns back the number of calories the user needs to consume for the basic bodily functions.
Fig 4.2 Daily Calories Calculator
Our diet plan form takes an input from the user about the levels of various nutrients mentioned
in their report. Based on the input we recommend a diet plan.
Fig 4.3 Customize your diet plan form
Chapter 5
Conclusion
The goal of this project was to find data of all the Indian recipes and ingredients with their
respective nutrient compositions to build a diet recommendation system for India that is for the
entire diverse population; specifically the Rural population. Train, evaluate and test a model to be
able to predict food items on the basis of the nutrient deficiencies of an individual. Finally, to
build a user- friendly web application as a step forward in building a recommendation system.
Our next step would be to make the model capable of recommending region-specific diets and
also to consider the user’s taste preferences.
References
•
End to End Recipe Cuisine Classification , AWS Lambda functions, BeautifulSoup,
Python, Sci-Kit Learn
https://towardsdatascience.com/https-towardsdatascience-com-end-to-end-recipecuisine-classification-e97f4ac22104
•
Building a Website Starter with FastAPI
https://levelup.gitconnected.com/building-a-website-starter-with-fastapi-92d077092864
•
A Simple Example of Pipeline in Machine Learning with Scikit-learn
https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-withscikit-learn-e726ffbb6976
•
Reference code
https://github.com/vishalvermaCred/ML_project
•
Report of a diet recommendation Website using machine learning
https://www.irjet.net/archives/V8/i4/IRJET-V8I4702.pdf
•
A Hybrid Approach Based Diet Recommendation System using ML and Big Data
Analytics
https://assets.researchsquare.com/files/rs-1044422/v1_covered.pdf?c=1664358567
•
Explaining & Implementing Content Based, Collaborative Filtering & Hybrid
Recommendation Systems in Python
https://towardsdatascience.com/recommendation-systems-explained-a42fc60591ed
•
An Introduction to Recommender Systems
https://www.iteratorshq.com/blog/an-introduction-recommender-systems-9-easyexamples/
•
Recommender Systems: Behind the Scenes of Machine Learning-Based Personalization
https://www.altexsoft.com/blog/recommender-system-personalization/
•
Dietary guidelines for Indians
https://www.nin.res.in/downloads/DietaryGuidelinesforNINwebsite.pdf
•
Building a Food recommendation system
https://towardsdatascience.com/building-a-food-recommendation-system-90788f78691a