| firmai/industry-machine-learning |
6,077 |
|
0 |
0 |
over 4 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai) |
| Murgio/Food-Recipe-CNN |
502 |
|
0 |
0 |
about 4 years ago |
0 |
|
20 |
|
Jupyter Notebook |
| food image to recipe with deep convolutional neural networks. |
| Chicago/food-inspections-evaluation |
281 |
|
0 |
0 |
almost 7 years ago |
0 |
|
7 |
other |
HTML |
| This repository contains the code to generate predictions of critical violations at food establishments in Chicago. It also contains the results of an evaluation of the effectiveness of those predictions. |
| IBM/visualize-food-insecurity |
24 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Use Watson Analytics and Pixie Dust to visualize US Food Insecurity |
| chiragsamal/Zomato |
18 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Zomato Restaurants Exploratory Data Analysis, Visualization and Prediction with Sentiment Analysis of Reviews and Recommendation System |
| rugk/crops-parser |
12 |
|
0 |
0 |
about 4 years ago |
0 |
|
5 |
other |
Shell |
| 🌱🍎🍆 A shell script to parse the data by the Food and Agriculture Organization of the United Nations on crops/fruits. |
| local-seasonal/pyfood |
7 |
|
0 |
0 |
over 4 years ago |
0 |
|
4 |
other |
Python |
| 🍋 A Python package to process food |
| MariPlaza/dscooking_old |
6 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
|
Python |
| Data Science Cooking is a personal project to combine Data Science and Cooking. |
| derekdjia/AI_Generated_Recipes |
6 |
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0 |
0 |
about 7 years ago |
0 |
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0 |
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HTML |
| Capstone Project: AI Generated Recipes |
| shishir349/Market-Basket-Analysis-on-Food-Items |
5 |
|
0 |
0 |
almost 6 years ago |
0 |
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0 |
gpl-3.0 |
Jupyter Notebook |
| Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store. |
