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.

metagenomics pipeline supporting The UnO Project for identifying pathogens in common across outbreak samples

Tools for binning data

Taxonomic classification and read binning of mitochondrial DNA

Optimal binning: monotonic binning with constraints

Photographic binning

hierarchical clustering of DNA sequence using upcxx

Machine learning with sklearn and tensorflow

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.

woe credit

Feature engineering is the process of transforming raw data into features. Here are some basic ideas about feature engineering.

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.

Map of U.S. Electric Power Generation by Fuel Source

Repository contains all needed functioins (Python) to create scorecard

Updated test score data and school district analysis using Python.

School district performance analysis using Python, Pandas, +Jupyter Notebook.

My evaluation of the school district's performance included the examination of standardised test results in math and reading, school budgets, and grade point averages. The information was organised by school size and type, with an emphasis on district and charter schools, using pandas.

these concepts are useful for converting numerical data to categorical