This repository contains a Jupyter Notebook that demonstrates the concept of computational complexity, focusing on time complexity and Big-O notation. It aims to help you understand these fundamental concepts and determine the time complexity of given algorithms.
At the end of this experiment, you will be able to:
- Understand the concept of computational complexity
- Understand time complexity, Big-O notation, and their importance in developing algorithms
- Determine the time complexity of given algorithms
- Time_Complexity.ipynb: The main Jupyter Notebook file that contains the code and explanations for understanding and analyzing time complexity.
To run the notebook, you need to have the following installed:
- Python 3.x
- Jupyter Notebook
You can install Jupyter Notebook using pip:
pip install notebook- Clone the repository:
git clone https://github.com/Praveen76/Time-Complexity-Analysis.git- Navigate to the cloned directory:
cd Time-Complexity-Analysis- Launch Jupyter Notebook:
jupyter notebook- Open the
Time_Complexity.ipynbnotebook and run the cells to explore the concepts of time complexity and Big-O notation.
Contributions are welcome! Please feel free to submit a Pull Request or open an issue for any bugs, suggestions, or improvements.
If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.
The code has been tested on Windows system. It should work well on other distributions but has not yet been tested. In case of any issue with installation or otherwise, please contact me on Linkedin
Happy coding!!
I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.