Among Unsupervised Machine Learning Algorithms, K means clustering is one of the most popular. It is used to solve Classification Problems. K Means clustering divides the unlabelled data into different groups, called clusters, based on features and patterns that are similar to cach other. Implement K Means Clustering Algorithm for a randomly generated sequence Option1: By writing a Python code or Option2: Implement and execute according to the flow given: Follow the steps: 1. Import the basic libraries 2. Generate 200 random numbers in 2 dimensional space 3. Train K Means algorithm by considering number of clusters equal to 3 4. Display the 3 clusters on 2D scattered plot. 5. Use Elbow Curve method to choose the number of centroids for improved clustering from the poor data we generated in step 1 by considering computational expense and knowledge gain from a dataset. 6. Mention the optimal number of clusters chosen and display improved clusters on 2D scattered plot Attach the screen shots of Executable code and results. (Choose either option 1 or option 2)