food recommendation sytem using python and streamlit

my thesis work done

1. Indian Recipe Recommendation System A Content-Based Food Recommendation System for Diverse Indian Culinary Tastes Prepared By Neha Tyagi M. Tech (CE) 4 th Sem. Roll no- MRT22PGMCS001

2. Agenda Introduction Motivation & Objectives Background Literature Survey Methodology Used Evaluation Principles Implementation Conclusion and Future Directions

3. What are Recommendation Systems? It is an information filtering tool designed to predict a user's preferences or ratings for specific items. These are filtering tools that provide recommendations that might interest the users. Recommendations provided are based on their likes or the likes of similar users. Some Popular Websites that use Recommendation Systems Fig 1 . Recommendation System

4. Popular Websites Websites What is recommended? Facebook Friend Suggestions YouTube Videos Amazon Consumer Products Netflix Movies and Web series

5. Motivation & Objectives Motivation Technological Advancements and AI Utilization Economic Motivation Enhancing Culinary Skills and Creativity Social and Cultural Connectivity Objectives Business and Marketing Enhancing User Experience Cultural and Culinary Exploration Academic and Research Purposes

6. Types of Recommendation Systems Content-based Recommendation Systems Collaborative Filtering (CF) based Recommendation System Hybrid Recommendation System Basic Models of Recommendation System User-item interactions Attribute information about the users and items

7. Content-based Recommendation Systems Content filtering uses the attributes or features of an item to recommend other items similar to the user’s preferences. Fig 2. Content Based Recommendation Systems

8. Collaborative Filtering based Recommendation Systems Collaborative filtering algorithms recommend items (this is the filtering part) based on preference information from many users (this is the collaborative part). Fig 3. Collaborative Filtering Based Recommendation Systems

9. Hybrid Recommendation Systems Hybrid recommendation Systems combine the advantages of both types to create a more comprehensive recommendation system. Fig 4. Hybrid Recommendation Systems

10. Literature Survey Researchers Method Used Contributions Limitations G. Sekhar Babu* et al. (2024) Graph Clustering, Machine Learning Novel hybrid food recommender-system addresses key issues for user satisfaction. cold start users, food items, and community aspects. G Prabhakar* et al. (2024) Ensemble model with DTC, SVM, and ANN for diabetes prediction Bagging ensemble classifier for type-2 diabetes prediction Data privacy concerns and unclear real-time capabilities in healthcare monitoring Kudang Boro* et al. (2023) Genetic Algorithm with elitism for food recommendation model Introduces a novel food recommender system using genetic algorithm, Elitism in Genetic Algorithm leads to slower convergence rate.

11. Methodology Used Dataset sourced from https://www.kaggle.com/datasets/sooryaprakash12/cleaned-indian-recipes-dataset The Term Frequency-Inverse Document Frequency (TF-IDF) is used to convert text into numerical representations. By computing cosine similarity between pairs of recipes For a given query, the recommendation engine utilized the computed cosine similarity matrix Recipes with the highest similarity scores are recommended Data Collection and Pre-processing Text Vectorization Similarity Computation Generate Recommendations Implement and Deploy Evaluation

12. Dataset Fig 5.Dataframe Info Fig 6. CSV View

13. Evaluation Principles Evaluating a recommendation system involves two main aspects : Relevance of Recommendations Ranking Quality Metrics for Explicit Feedback Recommendation Systems Mean Absolute Error, Root Mean Square Error, etc. Metrics for Implicit Feedback Recommendation Systems Precision@K , Recall@K , Mean Average Precision (MAP), Mean Average Recall (MAR) Some Popular Websites that use Recommendation Systems

14. Software Requirements PyCharm : An Integrated Development Environment (IDE) for Python programming. Streamlit : A powerful library for building interactive web applications with Python, ideal for creating user-friendly interfaces and visualizing data-driven insights effortlessly. Pandas : Enables data analysis and manipulation, offering efficient data structures and operations for large files like CSV or TSV. Scikit - learn : Provides various machine learning algorithms for data analysis and modeling tasks. Numpy : Facilitates handling large, multi-dimensional arrays and matrices and supports high-level mathematical functions.

15. UI Screens Some Popular Websites that use Recommendation Systems Fig 8. Search Screen

16. UI Screens Some Popular Websites that use Recommendation Systems Fig 9. Results Screen

17. Conclusion and Future Directions To conclude, our system has shown promising results, but there are several avenues for future research and development:- Enhanced Recommendation Algorithms Integration of User Feedback Expansion to Other Cuisines Integration with Cooking Platforms Personalized Nutritional Recommendations

18. Thank you Neha Tyagi M. Tech (CE) Roll no- MRT22PGMCS001