adityasaini70 / Gaussian-Mixture-Model-from-scratch

Implementing Gaussian Mixture Model from scratch using python class and Expectation Maximization algorithm. It is a clustering algorithm having certain advantages over kmeans algorithm.

Gaussian-Mixture-Model-from-scratch

Type of algorithm: Clustering algorithm
Dataset used: Iris dataset imported from sklearn

Output of final cluster

Requirements:

Jupyter notebook or Google Colab
Libraries:
Pandas: https://pandas.pydata.org/docs/getting_started/install.html
Numpy: https://numpy.org/install/
Matplotlib: https://matplotlib.org/stable/users/installing.html
sklearn: https://scikit-learn.org/stable/install.html
scipy: https://pypi.org/project/scipy/

Steps involved:

For Google Colab:

1. Open google colab on any browser.
2. Upload the file "Gaussian_Mixture_Model_from_scratch.ipynb" in the Google Colab.
3. Run all the cells in the notebook and view the output.
4. See the plots to visualize the final results.

For Jupyter Notebook:

1. Run the jupyter notebook.
2. Select the file "Gaussian_Mixture_Model_from_scratch.ipynb" from the location where you saved the file.
3. Install the required packages mentioned above.
4. Run all the cells in the notebook and view the output.
5. See the plots to visualize the final results.

References:

http://www.oranlooney.com/post/ml-from-scratch-part-5-gmm/