Gaussian and Binomial Distributions Package

This Python package provides tools for calculating and visualizing Gaussian and Binomial distributions. It is a part of the AWS AI/ML Scholarship program and is particularly useful in educational and practical applications within the fields of Machine Learning and Artificial Intelligence.

Key Applications in Machine Learning and AI

Data Modeling: Both Gaussian and Binomial distributions are crucial in the statistical modeling of data, which is a fundamental aspect of training and evaluating AI models.
Feature Engineering: Understanding the distribution of various features can help in creating better features that improve machine learning models.
Algorithm Assumptions: Many machine learning algorithms assume data to be normally distributed. This package can help in verifying these assumptions.
Performance Metrics: Binomial distribution is particularly useful in classification tasks where the outcome is binary, such as computing the probability of success or failure.

Installation

Clone this repository to your local machine:

git clone git@github.com:snufkinwa/gaussian-binomial-distribution.git

Navigate into the package directory:

cd gaussian-binomial-distribution

Install the package:

pip install .

Usage

Gaussian Distribution

To use the Gaussian distribution module:

from distributions import Gaussian

# Create a Gaussian distribution instance
gaussian = Gaussian(mu=5, sigma=2)

# Read data from a file
gaussian.read_data_file('gaussian_data.txt')

# Calculate mean and standard deviation
mean = gaussian.calculate_mean()
stdev = gaussian.calculate_stdev()

# Plot histogram and PDF
gaussian.plot_histogram_pdf()

# Plot 3D histogram and PDF
gaussian.plot_3d_histogram_pdf()

Histogram of Data and Probability Density Function

Binomial Distribution

To use the Binomial distribution module:

from distributions import Binomial

# Create a Binomial distribution instance
binomial = Binomial(p=0.4, n=20)

# Read data from a file
binomial.read_data_file('binomial_data.txt')

# Calculate mean and standard deviation
mean = binomial.calculate_mean()
stdev = binomial.calculate_stdev()

# Plot histogram and PDF
binomial.plot_bar()
binomial.plot_pdf()

Bar Chart

Probability Density Function

Bivariate Gaussian

To use the Bivariate Gaussian distribution module:

from distributions import BivariateGaussian

# Create a Bivariate Gaussian distribution instance
bivariate = BivariateGaussian(mu_x=5, mu_y=5, sigma_x=1, sigma_y=1, rho=0)

# Generate a grid of points
x_values, y_values, z_values = bivariate.generate_grid(0, 10, 0, 10, 0.1)

# Plot the Bivariate Gaussian distribution
bivariate.plot_bivariate_normal(x_values, y_values, z_values)

3D Visualization

Note: This class is still being improved upon

Acknowledgments

This project was developed with the help of hints and foundational code provided by Udacity as part of the AI Programming with Python.