This Jupyter Notebook project is designed for performing various machine learning and statistical analysis tasks, including clustering and classification. It provides a framework for building and evaluating machine learning models, as well as conducting statistical analysis on your datasets. The project also includes visualization tools to aid in the interpretation of the results.
To get started with this project, you'll need Python 3.6 or higher installed on your system. We recommend creating a virtual environment to manage dependencies. After cloning the project repository, navigate to the project directory and run:
'''python !pip install -r requirements.txt'''
This Jupyter Notebook provides several functionalities for machine learning, statistical analysis, and data visualization. Here's how you can use them:
- Data Exploration
- Unsupervised Learning For clustering tasks, various clustering algorithms like K-Means, DBSCAN, and hierarchical clustering were implemented . Modify the scripts to apply these algorithms on your data.
- Classifcation TO find the best Models. Common Machine Learning Algorihtms were selected the best hyperparameters were estimate with Crossvalidation and Gridsearch
- Last few cells need a strong cpu to run.
- plot dendogram function was taken from your github repo.
- for the histplot of each feature that looks at the clusters and wheter or not a person has diabetes I didn't label #counts on each y-axis because it looked bad
- silhouette coefficient idea was taken from here: https://towardsdatascience.com/evaluating-goodness-of-clustering-for-unsupervised-learning-case-ccebcfd1d4f1 while code block was on the yellowbrick api