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1 Programming assignment 1. k-means clustering assignment • Mission: Write Python3 code to do clustering using k-means. Use the "elbow" method to help you select the optimal number of clusters by fitting the model with a range of values for k. Plot the clusters in 3-d figures. Data set: seeds Data Set Read the data set description. • Approaches: Clustering algorithm (required): k-means. * Using scikit learn library: sklearn.cluster.KMeans. * Using Yellowbrick to perform the "elbow" method: Elbow Method Plot the clusters (required): the two tools are required for 3-d projections to work. *Using Matplotlib: Matplotlib: Visu zation with Python * Using Matplotlib toolkit: mplot3d - Other data pre-processing or feature engineering methods (optional): You can apply any tech- nique you prefer. • Performance metric: Successfully apply k-means on the dataset and plot the resulting clusters with indicated programming tools.

1 Programming assignment 1. k-means clustering assignment • Mission: Write Python3 code to do clustering using k-means. Use the "elbow" method to help you select the optimal number of clusters by fitting the model with a range of values for k. Plot the clusters in 3-d figures. Data set: seeds Data Set Read the data set description. • Approaches: Clustering algorithm (required): k-means. * Using scikit learn library: sklearn.cluster.KMeans. * Using Yellowbrick to perform the "elbow" method: Elbow Method Plot the clusters (required): the two tools are required for 3-d projections to work. *Using Matplotlib: Matplotlib: Visu zation with Python * Using Matplotlib toolkit: mplot3d - Other data pre-processing or feature engineering methods (optional): You can apply any tech- nique you prefer. • Performance metric: Successfully apply k-means on the dataset and plot the resulting clusters with indicated programming tools.

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1 Programming assignment 1. k-means clustering assignment ● Mission: Write Python3 code to do clustering using k-means. Use the "elbow" method to help you select the optimal number of clusters by fitting the model with a range of values for k. Plot the clusters in 3-d figures. • Data set: seeds Data Set - Read the data set description. • Approaches: Clustering algorithm (required): k-means. * Using scikit learn library: sklearn.cluster.KMeans. * Using Yellowbrick to perform the "elbow" method: Elbow Method Plot the clusters (required): the two tools are required for 3-d projections to work. * Using Matplotlib: Matplotlib: Visualization with Python * Using Matplotlib toolkit: mplot3d Other data pre-processing or feature engineering methods (optional): You can apply any tech- nique you prefer. • Performance metric: Successfully apply k-means on the dataset and plot the resulting clusters with indicated programming tools.
Transcribed Image Text:1 Programming assignment 1. k-means clustering assignment ● Mission: Write Python3 code to do clustering using k-means. Use the "elbow" method to help you select the optimal number of clusters by fitting the model with a range of values for k. Plot the clusters in 3-d figures. • Data set: seeds Data Set - Read the data set description. • Approaches: Clustering algorithm (required): k-means. * Using scikit learn library: sklearn.cluster.KMeans. * Using Yellowbrick to perform the "elbow" method: Elbow Method Plot the clusters (required): the two tools are required for 3-d projections to work. * Using Matplotlib: Matplotlib: Visualization with Python * Using Matplotlib toolkit: mplot3d Other data pre-processing or feature engineering methods (optional): You can apply any tech- nique you prefer. • Performance metric: Successfully apply k-means on the dataset and plot the resulting clusters with indicated programming tools.
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