K means Clustering Code - Flipkart Online Assessment Data Science Role

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"A company wants to group customer records into clusters based on two numerical behavioral features. Since the records are not labeled, use K-Means clustering to create the groups. Generate a synthetic dataset using scikit-learn and group its data points using the number of clusters provided as input. **Dataset Generation:** Generate the dataset using: `from sklearn.datasets import make_blobs` Use the following fixed parameters: * `n_samples=300` * `centers=4` * `n_features=2` * `random_state=0` **Task:** * Write a Python program that performs the following steps: * Generate the synthetic dataset using the specified parameters. * Read two integers from standard input: * `n_clusters` * `random_state` * Initialize a K-Means model using: * `n_clusters` equal to the input value * `random_state` equal to the input value * `n_init=10` * Train the K-Means model using the generated dataset. * Count the number of data points in every cluster. * Sort the cluster sizes in ascending order. * Print each sorted cluster size on a separate line. **Constraints:** 2 <= n_clusters <= 6 0 <= random_state <= 10000 **Input Format:** Two space-separated integers: `<n_clusters><space><random_state>` **Output Format:** Print `n_clusters` lines. Each line must contain one cluster size. The sizes must be printed in ascending order. **Sample Input 1:** ```text 4 0 ``` **Sample Output 1:** ```text 74 74 75 77 ``` **Note:** Cluster label numbers can vary internally. Therefore, print only the sorted cluster sizes. Do not print any extra text or labels."