CLUSTERING with SKLEARN
- Jul 22, 2023
- 1 min read
This project demonstrates how to use the scikit-learn library for data preprocessing, modeling, and clustering. The notebook first imports the necessary libraries and then loads the Iris dataset, which is a dataset of flower measurements. The notebook then uses the scikit-learn library to perform a variety of data preprocessing tasks, such as:
Splitting the dataset into a training set and a test set
Scaling the features
Handling missing values
The notebook then uses the scikit-learn library to build a clustering model. The model is a Gaussian mixture model, which is a type of probabilistic clustering model. The model is trained on the training set, and it is then evaluated on the test set. The model is able to achieve a high accuracy on the test set, which shows that it is able to cluster the flowers correctly. The notebook also includes a section on how to visualize the clustering results. The clustering results are visualized using a scatter plot, which shows the different clusters in different colors.
This project is a great way to learn how to use the scikit-learn library for data preprocessing, modeling, and clustering. The notebook is well-organized and easy to follow, and it includes clear explanations of the code.
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