This project, a part of the Introduction to Object-Oriented Programming course at Udacity, focuses on analyzing Gaussian and Binomial distributions. Originally part of the AWS Machine Learning Foundations Course, it offers a practical approach to understanding these statistical concepts through programming.
Key Features:
- Reading datasets
- Calculating mean and standard deviation
- Plotting histograms and probability density functions
- Adding Gaussian and Binomial distributions
The project structure includes a base class Distribution and two derived classes Gaussian and Binomial.
To install this package, run the following command:
pip install distributions-EH
To use this package, first import the required classes:
from distributions import Gaussian, BinomialFor Gaussian distributions, you can create an instance and use various methods:
# Create Gaussian instance
gaussian_one = Gaussian(25, 2)
# Calculate mean and standard deviation
mean = gaussian_one.mean
standard_deviation = gaussian_one.stdev
# Plot histogram
gaussian_one.plot_histogram()
# Add another Gaussian distribution
gaussian_two = Gaussian(30, 3)
gaussian_sum = gaussian_one + gaussian_two
Similarly, for Binomial distributions:
# Create Binomial instance
binomial_one = Binomial(.4, 20)
# Calculate mean and standard deviation
mean = binomial_one.mean
standard_deviation = binomial_one.stdev
# Plot histogram
binomial_one.plot_histogram()
# Add another Binomial distribution
binomial_two = Binomial(.5, 30)
binomial_sum = binomial_one + binomial_two
This project is licensed under the MIT License - see the LICENSE.md file for details.
Special thanks to Udacity and the AWS Machine Learning Foundations Course for the initial inspiration and guidance in creating this project.
This README is a work in progress and will be updated with more details and usage instructions. Thank you for your interest in Udacity_Gaussian!