Loading and exploring the iris dataset

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Reference no: EM132374594

Predictive Analytics for Decision Making Assignment -

Build Your First k-Nearest Neighbors Model with Scikit-learn

You First Application in Python: Classifying iris species

Follow textbook MLPy from page 13 to 25.

You are expected to have the following components:

Loading and exploring the iris dataset
Building your first model: k nearest neighbors
Making predictions
Evaluating the model

You need to submit a complete Jupyter notebook (.ipynb) file with all the source codes, outputs/charts, and necessary texts/comment in markdown cells.

1. Please "Run All" before submitting your file to make sure there is no error in your codes (each error would result in 1-point deduction). Make sure you have all libraries and modules properly imported before you use them.

2. Please use proper formatting for your markdown cells

3. Conclude your file with your own answers of "What are the strengths and weaknesses of kNNs?". Please don't copy/paste texts from the textbook or any other sources, try to use your own sentences. On the other hand, copying codes directly from the textbook pdf file is expected, just make sure all the codes run well in your notebook document.

About Assignment:

1. Make sure the codes run well in your notebook. Submit only a Jupyter Notebook document (no separate word or pdf is accepted).

2. Minor changes are expected to meet the requirements, meaning you have to show you understand what they (the codes you copy/paste) do. How do you do that? Read the relevant paragraphs next to the codes!

3. Make it presentable (by adding headings in markdown cells etc.) so a reader can follow. It also gives you basic ideas of the major steps in a typical ML work and of how they are done with sklearn/Python.

4. Answer the questions in markdown cells of course.

Attachment:- Predictive Analytics for Decision Making Assignment Files.rar

Reference no: EM132374594

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