The repository consists of four tasks covering various topics on Machine Learning and Statistics:
- Calculating square root of 2 to 100 decimal places without using Python libraries
- Verifying the Chi-squared value
- Python analysis of standard deviation in Microsoft Excel: STDEV.P vs STDEV.S
- Iris k-Nearest Neighbours
Submitted by: Olga Rozhdestvina (Student No: G00387844)
Lecturer: Ian McLoughlin
Programming Language used: Python
Applications used for completion of the tasks are The Jupyter Notebook and cmder
Distribution of Python is Anaconda Python distribution.
Libraries used to complete the tasks: NumPy, Pandas, Matplotlib, Seaborn, SciPy, scikit-learn. All of these are installed with the Anaconda Python distribution.
- Make sure that you have Python installed
- Download or clone current repository "Machine-Learning-and-Statistics-Tasks"
- Open Command Interpreter and get into correct directory
- Install packages by running pip install -r requirements.txt (recommended through virtual environment to avoid possible break of system tools or other projects)
- Run Jupyter notebook
- On the home page of opened Jupyter server select Tasks.ipynb
This project is licensed under the MIT License - see the LICENSE.md file for details