UNPKG

datamagic-ml

Version:

A lightweight JavaScript library for essential feature engineering tasks in machine learning. Provides utilities for normalization, standardization, one-hot encoding and missing value handling. Designed for simplicity and performance in both Node.js and b

58 lines (50 loc) 1.72 kB
# DataMagic-ML A lightweight JavaScript library for essential feature engineering tasks. Provides utilities for normalization, standardization, one-hot encoding, and missing value handling. Designed for simplicity and performance in both Node.js and browser environments. ## Features - **Min-Max Scaling**: Normalize data to a specific range. - **Standardization**: Transform data to have zero mean and unit variance. - **One-Hot Encoding**: Convert categorical data into numerical format. - **Missing Value Handling**: Replace missing values with mean, median, or a constant. ## Installation You can install `datamagic-ml` via npm: ```bash npm install datamagic-ml ``` Or using yarn: ```bash yarn add datamagic-ml ``` ## Usage Importing the Library: ```bash const { MinMaxScaler, StandardScaler, OneHotEncoder, CleanMissings } = require('datamagic-ml'); ``` Min-Max Scaling ```bash const scaler = new MinMaxScaler(); const data = [1, 2, 3, 4, 5]; scaler.fit(data); console.log(scaler.transform(data)); ``` Standardization ```bash const stdScaler = new StandardScaler(); const data = [1, 2, 3, 4, 5]; stdScaler.fit(data); console.log(stdScaler.transform(data)); ``` One-Hot Encoding ```bash const encoder = new OneHotEncoder(); encoder.fit(['red', 'green', 'blue']); console.log(encoder.transform(['green', 'red', 'yellow', 'blue'])); ``` Handling Missing Values ```bash const testArray = [1, null, 3, 4, NaN, 6]; console.log(CleanMissings(testArray, 'mean')); console.log(CleanMissings(testArray, 'median')); console.log(CleanMissings(testArray, 'constant', 0)); ``` ## License Licensed under the [MIT License](https://opensource.org/licenses/MIT).