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srafay / Machine_Learning_A-Z

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Learning to create Machine Learning Algorithms

machine-learning-algorithms datascience naive-bayes-classifier logistic-regression support-vector-machine polynomial-regression decision-tree-classifier dataprocessing classification-algorithims random-forest-regressor support-vector-regression multiple-regression decision-tree-regression k-means-implementation-in-python k-means-clustering kernel-svm random-forest-classifier simple-linear-regression k-nearest-neighbors machine-learning-az
  • Updated Jun 15, 2021
  • Python

CSBiology / BioFSharp

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Add pretty printers for common biological file formats

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kMutagene
kMutagene commented Dec 1, 2018

As analyzing biological data with BioFSharp involves a lot of scripting, optional pretty printers help a lot when looking at results directly in the fsi window. This should be especially easy to add for parsing libraries that can already write the correct file format. A basic pretty printer for biocollections is already impelemented.
Here are some formats from the top of my head, feel free to add

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up-for-grabs project-all good first issue

akshitvjain / restaurant-finder-featureReviews

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Build a Flask web application to help users retrieve key restaurant information and feature-based reviews (generated by applying market-basket model – Apriori algorithm and NLP on user reviews).

python flask data-science machine-learning natural-language-processing text-mining mongodb sentiment-analysis web-scraping webapp flask-application scrapy tableau tripadvisor dataprocessing restaurant-reviews summarizes-restaurant-reviews
  • Updated May 22, 2020
  • Python

kanishkan91 / Python-DataUpdate-DataProcessor-kbn

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The python module can be used to scrape data and process data from different sources. The python module can output data as either as a dataframe in the country year format or it will output data in excel files This module has primarily been created for processing data for the International Futures (IFs) Project however, it can be used to process data in general. The module can be used to process data from the following sources, 1) World Bank World Development Indicators (WDI) 2) UNESCO Education indicators(UIS) 3) FAO Food Balance Sheets (FAO) 4) IMF Global Finance Statistics (IMF GFS) 5) Health data from the Institute for Health and Metric Evaluation (IHME) 6) Water data from FAO AQUASTAT 7) Energy data from EIA Currently this module can be run as is on Windows. For usage on Macs, the user may have to make changes to the code lines which specify paths.

python data indexing uis eia wdi datascraping dataprocessing concordance fao imf ihme aquastat
  • Updated Jan 11, 2020
  • Python

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