Data Classification (100 points)

Consider the iris flower dataset, which includes information about different variations of iris flowers. (The data can be accessed at Kaggle.com.) Create a Jupyter notebook file that includes the following:

1. An explanation about different classes that already exist in the dataset.
2. Python code that builds different data classifiers (at least two) using the different classification algorithms, trains those classifiers using the training data, and displays their accuracy results.
3. Python code to run those classifiers on the test data and compare their results and accuracy. Discuss the reasons for different accuracies.
4. A summary of the results and your findings.

Important Notes:

• Submit only one .ipynb file that includes all the code and be sure not to submit any other file format.
• Be sure to include a clear explanation before each step you do in a markdown cell in the file.
• Be sure to include your name, the date, your class section and the name of your program at the top of your file in the first cell of the file (markdown cell).
• Be sure to add a table of contents at the second cell in the file (markdown cell).
• Comment your source code and describe your code to someone who may be viewing it for the first time.
• Your code should be executable and use meaningful variable names