rahul-raoniar / distribution_fitter_streamlit_app

Streamlit-based Distribution Fitter Web Application

The distribution fitter application will help you compare multiple distributions and find the best one that fits your data.

Steps to run the distribution fitter application

1. The app works fine with Python 3.6

2. Create an anaconda/miniconda environment with Python 3.6 and install the packages/libraries using the requirements.txt

    # Install Anaconda/Miniconda distribution
    # open anaconda prompt from start menu
   
    # Create an Anaconda/Miniconda environment
    # Here I have created a conda environment called 'streamlit_env' 
    # Create the environment with python 3.6
    conda create --name streamlit_env python=3.6
   
    # Activate the environment
    conda activate streamlit_env
   
    # Specify the directory path where you've cloned the GitHub repository 
    cd "directory path"
   
    # Run the following file on your Anaconda/Miniconda environment
    pip install -r requirements.txt
   
    # Next run the main.py file
    streamlit run main.py
   
    # Enjoy the streamlit-based distribution fitter app😊
   

The application comprised of four sections described as follows:

1. Home 🏡: Basic Information
2. Exploratory Data Analysis 🧮: Exploratory Data Analysis * Descriptive Statistics * Histogram Plotting (using Plotly)
3. Distribution Fitting 🌈: Fitting distribution using Fitter Python Library * Fitting Common Distributions * Fitting Distributions by Manual Selection
4. About 📑: About the Application

1. Home

The home page provides a basic information regarding the application. Additionally, it provides developer information and site related details.

	Github Page: https://github.com/cokelaer/fitter

2. Exploratory Data Analysis

The Exploratory Data Analysis section comprised of two subsections, Descriptive Stats and Visualization.

2.1 Descriptive Stats: A descriptive statistical analysis part has been added to check the data types and basic statistics to get overall idea about the data.

2.2 Visualization: Before you start fitting various distributions it is often recommended to plot a histogram, which will help you understand overall distribution your data follows. This will bring down the number of distributions you might need for comparision which eventually save you time.

3. Distribution Fitting

The fitter class of fitter library in the backend uses the Scipy library which supports 80 distributions. The Fitter class will scan common distributions or manually selected distributions, call the fit function for you, ignoring those that fail or run forever and finally give you a summary of the best distributions in the sense of sum of the square errors.

In this section two separate distribution fitting methods has been deployed which are described as follows:

3.1. Fit Common Distributions : You can select Fit Common Distributions from the drop down menu which will fit ten common distributions provided by the get_common_distributions( ) function. The ten common distributions are [‘cauchy’, ‘chi2’, ‘expon’, ‘exponpow’, ‘gamma’, ‘lognorm’, ‘norm’, ‘powerlaw’, ‘rayleigh’, ‘uniform’].

• Once you click process, the application will start fitting all common distributions and return top five distributions' summary in ascending order of the error [distribution with lowest error on top]. By default the distributions are ranked based on sumsquare_error.

• You can select best distribution parameters by sorting the fitting error based on sumsquare_error, aic or bic criteria

3.2. Fit Selected Distributions: If you have initial idea about possible distributions that might fit your data then select Fit Selected Distributions from the drop down menu and select all the distribution that you want to fit.

• Once you click process, the application will start fitting all selected distributions and return top five distributions' summary in ascending order of the error [distribution with lowest error on top]. By default the distributions are ranked based on sumsquare_error.

• Here also you can select best distribution parameters by sorting the fitting error based on sumsquare_error, aic or bic criteria

4. About Application

The about section provides a breif description of the application's functionality.