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[](https://pypi.org/project/simpliml/)
[](https://pypi.org/project/simpliml/)
[](https://pypi.org/project/simpliml/)
[](https://github.com/rajaddr/simpliml/blob/master/LICENSE)
<hr>
</div>
## 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)](https://pypi.org/project/simpliml)
- Source code repository : [GIT](https://github.com/rajaddr/simpliml)
- Issue tracker : [Issue](https://github.com/rajaddr/simpliml/issues)
- Changelog : [Changelog](https://github.com/rajaddr/simpliml/blob/master/CHANGELOG.md)
## Installation
```sh
# 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
```python
import pandas as pd
import simpliml.timeseriesforecast as tsf
```
#### Build Time Series Data
```python
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](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior)
- freq : str, optional
- Please refer [timeseries-offset-aliases](https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases)
- periods : int, optional
- Forcasting time period
#### Analysis Data
```python
tsf.analysisData(dataDF) # This will work only in interactive computational environment like Jupyter Notebook/lab/hub ..etc
```
#### Build Model and forcast
```python
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
```python
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
Raw data
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"description": "<div align=\"center\">\r\n\r\n \r\n\r\n[](https://pypi.org/project/simpliml/)\r\n[](https://pypi.org/project/simpliml/)\r\n[](https://pypi.org/project/simpliml/)\r\n[](https://github.com/rajaddr/simpliml/blob/master/LICENSE)\r\n<hr>\r\n</div>\r\n\r\n## Project description\r\n**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. \r\n\r\n## Key Features \r\n\r\n- **Data Cleansing and Cleaning** \r\n Simplify the preprocessing of raw data to ensure accurate and reliable model performance. \r\n\r\n- **Data Analysis** \r\n Explore and analyze data with powerful, easy-to-use tools to uncover actionable insights. \r\n\r\n- **Model Execution and Prediction** \r\n Train, validate, and deploy machine learning models seamlessly for accurate and efficient predictions. \r\n\r\n- **Forecasting and Optimization** \r\n Perform precise forecasting and optimize your decision-making processes with ease. \r\n\r\n## Why Choose SimpliML? \r\n\r\n***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. \r\n\r\nGet started today and unlock the full potential of your data with **SimpliML**! \r\n\r\n## Links\r\n- Binary installers : [Python Package Index (PyPI)](https://pypi.org/project/simpliml)\r\n- Source code repository : [GIT](https://github.com/rajaddr/simpliml) \r\n- Issue tracker : [Issue](https://github.com/rajaddr/simpliml/issues) \r\n- Changelog : [Changelog](https://github.com/rajaddr/simpliml/blob/master/CHANGELOG.md) \r\n\r\n## Installation\r\n```sh\r\n# or PyPI\r\npip install simpliml\r\n```\r\n\r\n## Dependencies\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n## Usage\r\n\r\n## 1) Time Series\r\n- **Pre-built Models:** Includes popular time series forecasting models like ARIMA, SARIMA, torch, and more.\r\n- **Seamless Integration:** Load data, preprocess, and run forecasting models in one place.\r\n- **Automatic Forecasting:** Automatically generates forecasts for future time steps once a model is selected.\r\n- **Visualization:** Built-in tools for visualizing both historical data and forecasted values.\r\n- **Customizable:** Fine-tune model parameters to suit your specific use case.\r\n- **Extensive Documentation:** Detailed guides and examples to help you get started.\r\n\r\n\r\n#### Import SimpliML Time Series\r\n```python\r\nimport pandas as pd\r\nimport simpliml.timeseriesforecast as tsf\r\n```\r\n\r\n#### Build Time Series Data\r\n```python\r\nsourceDF = pd.read_csv(\"\") # Any Input data read\r\ndataDF, futureDF = tsf.generateTSData(sourceDF, format='%Y-%m', freq='MS', periods=30)\r\n```\r\nParameters:-\r\n- format : str, optional \r\n - Please refer [strftime-and-strptime-behavior](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior)\r\n- freq : str, optional\r\n - Please refer [timeseries-offset-aliases](https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases)\r\n- periods : int, optional\r\n - Forcasting time period\r\n\r\n#### Analysis Data\r\n```python\r\ntsf.analysisData(dataDF) # This will work only in interactive computational environment like Jupyter Notebook/lab/hub ..etc \r\n```\r\n\r\n#### Build Model and forcast\r\n```python\r\nmdlResult = tsf.runModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80) # Single Process Thread\r\n(OR)\r\nmdlResult = tsf.runThreadModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80) # Single Process Multiple Thread (Thread : CPU Count * 2)\r\n(OR)\r\nmdlResult = tsf.runProcessModel(dataDF, futureDF, seasonal=12, modelApproach = 'FAST', testSize=80) # Multiple Process (Process : CPU Count / 4) # Advise to use only in Windows \r\n```\r\nParameters:-\r\n- seasonal : int, optional\r\n - The number of periods in a complete seasonal cycle, \r\n - Example \r\n - 1 : Yearly data\r\n - 2 : Half-yearly data\r\n - 4 : Quarterly data \r\n - 7 : Daily data with a weekly cycle\r\n - 52 : Weekly Data\r\n- modelApproach : [\"BEST\", \"FAST\"], optional\r\n - BEST : Best model build with multiple permutation and combination\r\n - FAST : Fast model build with limited permutation and combination\r\n- testSize : int, optional\r\n - Test Size by defult 80:20 rule\r\n\r\n\r\n#### Model Result Analysis\r\n```python\r\nmdlOutPut = tsf.modelResult(dataDF, mdlResult, modelApproach='Best') \r\n```\r\nParameters:-\r\n- modelApproach : str, optional\r\n - BEST MAPE analysis report and can pass the model name, get the analysis report\r\n\r\n",
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