Machine Learning Helo! Jupyter Notebook Please, in Python Language In this assignment you are to utilize the knowledge presented in chapter 2 to be able to read a data file that is located at UCI's ML repository: https://archive.ics.uci.edu/ml/index.php. I am suggesting the Automobile data set: https://archive.ics.uci.edu/ml/datasets/Automobile. However, if you'd like to use another data set, feel free to do so as long as it is of similar complexity as the automobile dataset. Basically, you need to be able to read this data and visualize as much as you can from it. Here are the specific requirements: 1. Develop a Jupyter Notebook to do the following, this is similar to the work in the ML-SciKit book: a. Setup to read the data, you can obtain it directly from a URL or just read it from a local file. b. Read and Load the data and then display it in a DataFrame c. get the info of the resulting data frame d. provide counts of some categories (numeric and other) e. provide a description of the resulting data f. plot the data as much as possible g. split the data into 20%, 80%. 2. Use another Jupiter notebook to implement the Pocket algorithm again using your own generated data. This time your data doesn't need to be linearly separable, it can actually be any type of data. Implement this method using numpy constructs and use the visualization techniques in question 1 to visualize the data. Make sure that you display the error value (in sample error), and report your results on the out of sample error as well. 

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