Rahul Thorat's Projects
A web app is built to detect anomalies in data.
This dashboard is an example to design and style a dashboard app completely in python with Dash Bootstrap.
de automatische classificatie van organisaties Gezondheid, Sport en Onderwijs
Comparison-text-target-vs-source
match email domains from an input Excel file with a list of known domains of malafide practices
copied code to plot a map with geoplot and geopandas
Importing repositories from Re3data
This is a code to be run in Google Colab.
This is a code to run in Google Colab.
It is important for a granting agency to know how the distribution of the applications qua disciplines is. Ė How many applications belong to Exact Science disciplines, how many fall within one discipline? Ė How many applications with disciplines outside exact sciences domain have been submitted?
Entity Extraction from PDF files using spacy NLP model
This is a code to collect publication information of researchers, if digital object information (DOIs) is available.
Python-script dat gegevens over faillissementen voor het jaar 2023 analyseert en visualiseert
Python-script dat data over faillissementen voor het jaar 2024 analyseert en visualiseert
Funders use Expert Lookup for a list of reviewers for submitted research proposals. It still takes a lot of time to check whether the selected reviewers from Expert Lookup have already in contact with the funder or whether they have already assessed a proposal. With this code a funder can easily filter list of reviewers.
whether the companies submitting subsidy applications to government have been suspected of fraud or commited fraud in the past.
Following codes provide better ways to iterate in pandas dataframe over rows instead of nested loops
Building a natural language processing custom model with annotation tool prodi.gy
functions for matching pattern such as symbols, name, e-mail adress, hyperlinks, string
Running a local Piper TTS server with Python on ubuntu 24.04
Following are a few diagrams as examples to show various types of relationships among grant proposals.
Importing prodi.gy dataset in jupyter notebook, reading labels and exporting list of words per label
Funding organisations who use Elsevier Expert Lookup for searching reviewers for subsidy applications manually place application information per subsidy round in the Expert Lookup. That takes a lot of time and energy. By automatically placing the application information in the Expert Lookup, funding organisations can save a lot of time. Elsevier Expert Lookup provides format in which it can take the lumsum application information. This code provides missing gap, where the lumsum applications information from funding organisation is converted into the format to upload the lumsum information in Expert Lookup.
Funding organisations who use Elsevier Expert Lookup for searching reviewers for subsidy applications manually place application information per subsidy round in the Expert Lookup. That takes a lot of time and energy. By automatically placing the application information in the Expert Lookup, funding organisations can save a lot of time. Elsevier Expert Lookup provides format in which it can take the lumsum application information. This code provides missing gap, where the lumsum applications information from funding organisation is converted into the format to upload the lumsum information in Expert Lookup.
Config files for my GitHub profile.
This code gives ratio of applications and applicant for two subsidie rounds.
Reorienting the table to make keywords from multiple rows appear in a single row
Extracting data repository name from a text
An Alluvial diagram has been used to relate categories, subcategories of grant applications
Following script creates a geographic map of scientific mobility. It shows scientists going from one institute to another institute for a given year.