An R package for simple, customizable binning of flow cytometric data
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Updated
Aug 26, 2022 - R
An R package for simple, customizable binning of flow cytometric data
Mock exercise as Chief Data Scientist analyzing student standardized testing data for school board.
Taxonomic classification and read binning of mitochondrial DNA
Optimal binning: monotonic binning with constraints
Photographic binning
hierarchical clustering of DNA sequence using upcxx
Feature engineering is the process of transforming raw data into features. Here are some basic ideas about feature engineering.
Machine learning with sklearn and tensorflow
The function here is designed for binning continuous independent variables, in the way minimizing total entropy of corresponding response. Also this function can plot the change of the entropy in the process.
The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.
This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.
metagenomics pipeline supporting The UnO Project for identifying pathogens in common across outbreak samples
Map of U.S. Electric Power Generation by Fuel Source
these concepts are useful for converting numerical data to categorical
In this tutorial it is provided an overview of the anvi’o workflow for the analysis of assembly-based shotgun metagenomic data.
Code for classifying whether someone can repay their loan to a banking institution using a supervised learning approach: Binning and Logistic Regression.
Data auto-binning algorithm- takes x,y,z data and organizes it in x and y user prescribed bins, auto-interpolates data gaps, then plots z
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