unsupervised-machine-learning-challenge

Clustering algorithms to explore whether the patients of a medical research can be placed into distinct groups. Exploring the possibility of unsupervised machine learning. Visualisation to share the findings with the team and other key stakeholders.

Instructions

1. To execute the project follow the below commands: git clone https://github.com/BharatGuturi/Unsupervised-machine-learning.git
2. Install the required libraries using the following commands: pip install matplotlib pip install pandas pip install plotly pip install scikit-learn
3. Run the "Unsupervised Machine Learning.ipynb" file

Summary

PCA and t-SNE clustering

kmeans analysis

Conclusion

Using PCA and tsne, the data could not be clustered. Hence cluster analysis using kmeans was done.The elbow curve between intertia and number of customers could not determine the number of customers where the curve converges. To obtain a variance ratio of 0.9, number of clusters required were 10. Using 10 clusters, when the 3dimensional graph was plotted, the clusters could not be identified clearly. Hence, clustering of the patient's data couldnot be achieved.