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:

  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 file for details.

Connect with Me

• LinkedIn: Profile
• Contact: Sunny Bibyan