https://github.com/sunnybibyan/random_data_generation

A project that generates a dataset using various statistical distributions (Normal, Uniform, Exponential, Random Integers, and Binomial) and performs data analysis. Includes visualizations and an option to export the data as a CSV file.
https://github.com/sunnybibyan/random_data_generation

data-analysis data-visualization python random-data-generation statistics streamlit-webapp

Last synced: 6 days ago
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A project that generates a dataset using various statistical distributions (Normal, Uniform, Exponential, Random Integers, and Binomial) and performs data analysis. Includes visualizations and an option to export the data as a CSV file.

• Host: GitHub
• URL: https://github.com/sunnybibyan/random_data_generation
• Owner: SunnyBibyan
• License: mit
• Created: 2024-09-12T12:59:57.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T14:03:12.000Z (2 months ago)
• Last Synced: 2024-09-13T01:11:35.505Z (2 months ago)
• Topics: data-analysis, data-visualization, python, random-data-generation, statistics, streamlit-webapp
• Language: Python
• Homepage: https://sunnybibyan-random-data-generation-app-mptcjw.streamlit.app/
• Size: 14.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Random Data Generation and Basic Statistical Analysis

## Overview

This project generates a synthetic dataset using various statistical distributions, providing insights into the nature of random data. The dataset includes values from Normal, Uniform, Exponential, Random Integers, and Binomial distributions, allowing for a comprehensive analysis of different types of data.

The dataset is designed for educational purposes, offering a practical example of how to generate and analyze random data.

## Dataset Generation

### Key Features

- **Data Sources:** Data is generated using Python libraries such as NumPy and Pandas.

- **Distributions:**

  - **Normal Distribution:** Simulates continuous data with a Gaussian distribution.

  - **Uniform Distribution:** Provides values within a specified range.

  - **Exponential Distribution:** Models the time between events.

  - **Random Integers:** Simulates discrete values.

  - **Binomial Distribution:** Represents binary outcomes.

- **Statistics:** Descriptive statistics including mean, median, and standard deviation are computed.

- **Visualizations:** Histograms are created to observe the distribution patterns.

## Tools & Technologies

- **Python:** For data generation and analysis.

- **NumPy:** For numerical operations and random data generation.

- **Pandas:** For data manipulation and analysis.

- **Matplotlib:** For plotting visualizations.

- **Seaborn:** For enhanced data visualization.

## Dataset Information

The generated dataset includes the following columns:

- **Normal Distribution:** Values drawn from a Gaussian distribution.

- **Uniform Distribution:** Values uniformly distributed between specified limits.

- **Exponential Distribution:** Values following an exponential distribution.

- **Random Integers:** Integer values within a specified range.

- **Binomial Distribution:** Values from a binomial distribution representing binary outcomes.

## Visualizations

The project includes histograms for each type of distribution:

- **Normal Distribution Histogram:** Shows the distribution of values from the Gaussian distribution.

- **Uniform Distribution Histogram:** Displays the range and frequency of uniformly distributed values.

- **Exponential Distribution Histogram:** Illustrates the spread of values from the exponential distribution.

- **Random Integers Histogram:** Visualizes the frequency of discrete integer values.

- **Binomial Distribution Histogram:** Represents the frequency of binary outcomes.

## Project Structure

### How to Use the Project

1. **Run the Script:** Execute `App.py` to generate the dataset and visualizations.

2. **Explore Visualizations:** Use the Streamlit interface to select columns and view histograms.

3. **Download Data:** Use the download button to save the generated dataset as a CSV file.

## Requirements

- **Install the necessary Python libraries:**

  ```sh

  pip install -r requirements.txt

## Insights and Recommendations**

- **Distribution Patterns:** Analyze how different statistical distributions generate data with varying patterns.

- **Data Analysis:** Utilize the generated dataset for educational purposes, testing, and further analysis.

  

## License

This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.

## Connect with Me

- **LinkedIn**: [Profile](https://www.linkedin.com/posts/sunny-bibyan)

- **Contact**: [Sunny Bibyan](mailto:[email protected])