Simple Machine Learning Tool Kit

Related tags

Machine Learningsmltk
Overview

Getting started

smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during

  • data preparation
  • testing your model

The goal is to implement this package for each step of machine learning process that can simplify your code.

It is part of the educational repositories to learn how to write stardard code and common uses of the TDD.

Installation

If you want to use this package into your code, you can install by python3-pip:

pip3 install smltk
python3
>>> from smltk.metrics import Metrics
>>> help(Metrics)

The package is not self-consistent. So if you want to contribute, you have to download the package by github and to install the requirements

git clone https://github.com/bilardi/smltk
cd smltk/
pip3 install --upgrade -r requirements.txt

Read the documentation on readthedocs for

  • API
  • Usage
  • Development

Change Log

See CHANGELOG.md for details.

License

This package is released under the MIT license. See LICENSE for details.

Owner
Alessandra Bilardi
Data & Automation Specialist | AWS Enthusiasts | CoderDojo Mentor
Alessandra Bilardi
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

Spark Python Notebooks This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, fro

Jose A Dianes 1.5k Jan 02, 2023
Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

1 Jan 01, 2022
Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them

Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.

Anirudh Edpuganti 3 Apr 03, 2022
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
Bayesian optimization in JAX

Bayesian optimization in JAX

Predictive Intelligence Lab 26 May 11, 2022
Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

Tangram Website | Discord Tangram makes it easy for programmers to train, deploy, and monitor machine learning models. Run tangram train to train a mo

Tangram 1.4k Jan 05, 2023
LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading

LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading. The framework simplify development, testing, deployment, analysis and training algo trading strategies

Amichay Oren 458 Dec 24, 2022
A collection of video resources for machine learning

Machine Learning Videos This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous

Dustin Tran 1.5k Dec 29, 2022
Course files for "Ocean/Atmosphere Time Series Analysis"

time-series This package contains all necessary files for the course Ocean/Atmosphere Time Series Analysis, an introduction to data and time series an

Jonathan Lilly 107 Nov 29, 2022
Python package for concise, transparent, and accurate predictive modeling

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern

Chandan Singh 983 Jan 01, 2023
Both social media sentiment and stock market data are crucial for stock price prediction

Relating-Social-Media-to-Stock-Movement-Public - We explore the application of Machine Learning for predicting the return of the stock by using the information of stock returns. A trading strategy ba

Vishal Singh Parmar 15 Oct 29, 2022
Napari sklearn decomposition

napari-sklearn-decomposition A simple plugin to use with napari This napari plug

1 Sep 01, 2022
neurodsp is a collection of approaches for applying digital signal processing to neural time series

neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also inclu

NeuroDSP 224 Dec 02, 2022
This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

B DEVA DEEKSHITH 1 Nov 03, 2021
Diabetes Prediction with Logistic Regression

Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou

AZÄ°ZE SULTAN PALALI 2 Oct 23, 2021
Visualize classified time series data with interactive Sankey plots in Google Earth Engine

sankee Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine Contents Description Installation Using P

Aaron Zuspan 76 Dec 15, 2022
Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

Olá! Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogr

Henrique de Paula 10 Apr 04, 2022
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022
Implementation of K-Nearest Neighbors Algorithm Using PySpark

KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:

Zachary Petroff 4 Dec 30, 2022
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022