simpliml 1.0.0

Machine Learning, Artificial Intelligence, Mathematics

---

Project description

SimpliML is a versatile machine learning library designed to be a one-stop solution for the entire data lifecycle. Whether you're preparing raw data or deploying advanced predictive models, SimpliML simplifies every step of the machine learning process.

Key Features

• Data Cleansing and Cleaning
  Simplify the preprocessing of raw data to ensure accurate and reliable model performance.

• Data Analysis
  Explore and analyze data with powerful, easy-to-use tools to uncover actionable insights.

• Model Execution and Prediction
  Train, validate, and deploy machine learning models seamlessly for accurate and efficient predictions.

• Forecasting and Optimization
  Perform precise forecasting and optimize your decision-making processes with ease.

Why Choose SimpliML?

SimpliML is designed for data scientists, ML engineers, and enthusiasts who need a reliable, efficient, and easy-to-use toolkit for managing the entire machine learning workflow.

Get started today and unlock the full potential of your data with SimpliML!

Links

• Binary installers : Python Package Index (PyPI)
• Source code repository : GIT
• Issue tracker : Issue
• Changelog : Changelog

Installation

# or PyPI
pip install simpliml

Dependencies

Usage

1) Time Series

• Pre-built Models: Includes popular time series forecasting models like ARIMA, SARIMA, torch, and more.
• Seamless Integration: Load data, preprocess, and run forecasting models in one place.
• Automatic Forecasting: Automatically generates forecasts for future time steps once a model is selected.
• Visualization: Built-in tools for visualizing both historical data and forecasted values.
• Customizable: Fine-tune model parameters to suit your specific use case.
• Extensive Documentation: Detailed guides and examples to help you get started.

Import SimpliML Time Series

import pandas as pd
import simpliml.timeseriesforecast as tsf

Build Time Series Data

sourceDF = pd.read_csv("") # Any Input data read
dataDF, futureDF = tsf.generateTSData(sourceDF, format='%Y-%m', freq='MS', periods=30)

Parameters:-

• format : str, optional
  • Please refer strftime-and-strptime-behavior
• freq : str, optional
  • Please refer timeseries-offset-aliases
• periods : int, optional
  • Forcasting time period

Analysis Data

tsf.analysisData(dataDF) # This will work only in interactive computational environment like Jupyter Notebook/lab/hub ..etc 

Build Model and forcast

mdlResult = tsf.runModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80)  # Single Process Thread
(OR)
mdlResult = tsf.runThreadModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80) # Single Process Multiple Thread (Thread : CPU Count * 2)
(OR)
mdlResult = tsf.runProcessModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80) # Multiple Process (Process : CPU Count / 4) # Advise to use only in Windows  

Parameters:-

• seasonal : int, optional
  • The number of periods in a complete seasonal cycle,
  • Example
    • 1 : Yearly data
    • 2 : Half-yearly data
    • 4 : Quarterly data
    • 7 : Daily data with a weekly cycle
    • 52 : Weekly Data
• modelApproach : ["BEST", "FAST"], optional
  • BEST : Best model build with multiple permutation and combination
  • FAST : Fast model build with limited permutation and combination
• testSize : int, optional
  • Test Size by defult 80:20 rule

Model Result Analysis

mdlOutPut = tsf.modelResult(dataDF, mdlResult, modelApproach='Best') 

Parameters:-

• modelApproach : str, optional
  • BEST MAPE analysis report and can pass the model name, get the analysis report