Showing results for Create Vector Recipe
GitHub Repo
https://github.com/smburgoyne/Recipe-Book-Console-Application
smburgoyne/Recipe-Book-Console-Application
Program read user input to create and search for recipes stored in vectors, and wrote them to a file.
GitHub Repo
https://github.com/Balorum/Cocktail_Assistant
Balorum/Cocktail_Assistant
This project is a Python-based chat app integrating a Large Language Model (LLM) with a vector database to create a Retrieval-Augmented Generation (RAG) system for cocktail recommendations and information. It offers a simple web interface where users can ask cocktail-related questions, get personalized suggestions, and explore drink recipes.
GitHub Repo
https://github.com/KarinaVanyaWardoyo/SmartRecipeRecommender
KarinaVanyaWardoyo/SmartRecipeRecommender
Creating a website that can generate recipe ideas based on available ingredients based on user's input. This project implements Content-Based Filtering (CBF) to suggest recipes by using TF-IDF to convert ingredients into numerical vectors, followed by Cosine similarity to measure how similar the vectors of the ingredients are
GitHub Repo
https://github.com/Aryia-Behroziuan/pandas
Aryia-Behroziuan/pandas
Tutorials This is a guide to many pandas tutorials, geared mainly for new users. Internal Guides pandas own 10 Minutes to pandas More complex recipes are in the Cookbook pandas Cookbook The goal of this cookbook (by Julia Evans) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that that entails. Here are links to the v0.1 release. For an up-to-date table of contents, see the pandas-cookbook GitHub repository. To run the examples in this tutorial, you’ll need to clone the GitHub repository and get IPython Notebook running. See How to use this cookbook. A quick tour of the IPython Notebook: Shows off IPython’s awesome tab completion and magic functions. Chapter 1: Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong! Chapter 2: It’s not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns) Chapter 3: Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast. Chapter 4: Groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this. Chapter 5: Here you get to find out if it’s cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. Chapter 6: Strings with pandas are great. It has all these vectorized string operations and they’re the best. We will turn a bunch of strings containing “Snow” into vectors of numbers in a trice. Chapter 7: Cleaning up messy data is never a joy, but with pandas it’s easier. Chapter 8: Parsing Unix timestamps is confusing at first but it turns out to be really easy. Lessons for New pandas Users For more resources, please visit the main repository. 01 - Lesson: - Importing libraries - Creating data sets - Creating data frames - Reading from CSV - Exporting to CSV - Finding maximums - Plotting data 02 - Lesson: - Reading from TXT - Exporting to TXT - Selecting top/bottom records - Descriptive statistics - Grouping/sorting data 03 - Lesson: - Creating functions - Reading from EXCEL - Exporting to EXCEL - Outliers - Lambda functions - Slice and dice data 04 - Lesson: - Adding/deleting columns - Index operations 05 - Lesson: - Stack/Unstack/Transpose functions 06 - Lesson: - GroupBy function 07 - Lesson: - Ways to calculate outliers 08 - Lesson: - Read from Microsoft SQL databases 09 - Lesson: - Export to CSV/EXCEL/TXT 10 - Lesson: - Converting between different kinds of formats 11 - Lesson: - Combining data from various sources Practical data analysis with Python This guide is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset. There are four sections covering selected topics as follows: Munging Data Aggregating Data Visualizing Data Time Series Excel charts with pandas, vincent and xlsxwriter Using Pandas and XlsxWriter to create Excel charts Various Tutorials Wes McKinney’s (pandas BDFL) blog Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 Financial analysis in python, by Thomas Wiecki Intro to pandas data structures, by Greg Reda Pandas and Python: Top 10, by Manish Amde Pandas Tutorial, by Mikhail Semeniuk indexmodules |next |previous |pandas 0.15.2 documentation » © Copyright 2008-2014, the pandas development team
GitHub Repo
https://github.com/http406/Vect-Field-Remastered.py
http406/Vect-Field-Remastered.py
From the previous code of Vector Field 2D, using arrows to represent a vector field works fairly well. But matplotlib can do better than this—it can show the streamlines of a vector field. A streamline shows how the vector field flows. In this recipe, we will show you how to create streamlines. Let's use the fluid flow example of the previous recipe. You will simply replace the arrows ( the first code of vector field ) with streamlines, as shown in the following code.
GitHub Repo
https://github.com/asojan6699/Recipe-Recommender-System
asojan6699/Recipe-Recommender-System
Created a content-based recipe recommender system using TF-IDF vectorization and cosine similarity. The system suggests recipes based on ingredients or user-selected preferences. Demonstrates skills in natural language processing, text cleaning, and recommendation algorithms.
GitHub Repo
https://github.com/MikeDuran-git/simple-local-rag
MikeDuran-git/simple-local-rag
This project aims to create a simple Retrieval-Augmented Generation (RAG) application that generates recipes based on available ingredients. By leveraging advanced language models (such as GPT-4) and a vector database, this application can retrieve relevant information and generate personalized recipes based on user criteria.
GitHub Repo
https://github.com/arunprabu-bot/AI-RECIPE-GENERATOR
arunprabu-bot/AI-RECIPE-GENERATOR
An AI-powered recipe generator built with Streamlit and Google Gemini that creates personalized recipes based on user ingredients and dietary preferences, with vector search for finding similar recipes.
GitHub Repo
https://github.com/Seba-Toso/Meals-Recipes
Seba-Toso/Meals-Recipes
Finished|http://imgfz.com/i/ltyvD8a.png|http://imgfz.com/i/GRIT4s1.png|http://imgfz.com/i/mbqzows.png|The task was to create an app that allows access to recipe categories. On each category, all its recipes must be displayed and in each one of these will see its preparation. Each recipe must be able to be marked as a favorite and this must be recorded. All recipes can be filtered and checked as favorite.|This app was made using React-Native, expo as skeleton, expo vector icons, React Stack Navigation, Bottom tab Navigation and Drawer Navigation. The global state management of the app was done with Redux. The app data this time was provided by hardcode according to the meal and category models.|La tarea era crear una aplicación que permita el acceso a las categorías de recetas. En cada categoría se deben mostrar todas sus recetas y en cada una de estas se verá su preparación. Cada receta debe poder marcarse como favorita y esto debe quedar registrado. Todas las recetas se pueden filtrar y marcar como favoritas.| Esta aplicación se creó con React-Native, expo como esqueleto, iconos de vector de exposición, React Stack Navigation, navegación con pestañas inferiores y navegación con cajones. La gestión del estado global de la aplicación se realizó con Redux. Los datos de la aplicación esta vez fueron proporcionados por hardcode de acuerdo con los modelos de comida y categoría.
GitHub Repo
https://github.com/rida05432/Data-Analytics-Project