OpenFoodFacts
About this Project
The aim if this project is to create an application than can use the data of the OpenFoodFacts open database.
" Open Food Facts gathers information and data on food products from around the world. "
In this project, we will clean and explore the OpenFoodFacts database to evaluate the feasability of our application.
This project is divided into two notebooks:
- In the first notebook, we will clean and filter the OpenFoodfacts data which is constituted of around 2 millions samples.
- In the second notebook we will statistically explore the filtered dataset in order to evaluate the feasability of our application.
In addition, we will also focus on creating tools to automate the exploration of datasets, whichever they are. This will enable us to quickly and efficiently explore datasets, in a transverse way, in the future.
Features
Data Analysis
Keep improving your results! With Data-Analysis we transform the raw data from your app into meaningful insights (and beautiful graphs) which will allow to understand your users and market.
Technologies
Data/Ai
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load : it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.
source: keras.io
NumPy
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
source: wikipedia.org
Pandas
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
source: pandas.pydata.org
Sickit-Learn
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language.
It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project.
source: wikipedia.org
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Q&A
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