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Final Project: Predict Boston Housing Prices

You are the best real estate agent in Boston because you use Machine Learning to predict house prices. One of your clients wants to sell a house and they want you to give them an estimate of the best selling price. You are the Kelley Blue Book of homes, so you want to predict the correct price. Not only that, you should be able to convince your client that your estimate is reasonable.

We have seen a lot of models (DT, NN and so on) and various algorithms (ID3, Gradient Descent, etc.) to learn them in the video lessons. Often when a dataset is given, we are trying to fit it to the best model; that is, the best model that generalizes for data we haven’t seen yet. An important skill of a data scientist is to identify the best model. This project is going to help you analyze a dataset and teach you how to choose the best model that generalizes that dataset.

If you were a carpenter, the project is comparable to you building a desk or a tree house. A carpenter does not build the tools that are required for the construction, but he knows which tools to use to get the job done. Similarly, it is important for a good data scientist to know which tools to use to find the best model.

In this project you will get a chance to work with these "tools" and find a model that gives you the best generalization. From this you will have to estimate the price of a particular house for your client and justify it.

The following sections will help you understand the logistics of the final project: implementation details and submission guidelines. These documents are intended for students with a Udacity Coach who enrolled in the full course experience. If you are previewing the courseware you are welcome to look at these documents as well (but understand that you will not submit your project to Udacity).

Project Resources

Submission Instructions

Please submit the following:

  1. A PDF Document that includes answers to the questions in What to Submit? section of the Project Description. Ensure that your report does not exceed 10 pages (including charts). Any report over ten pages will not be graded.

  2. Create a .zip or .tar.gz of your source code, if you modified the code base. You can also upload your source code somewhere and send us a link to it.

  3. Write down a list of Web sites, books, forums, blog posts, github repositories etc that you referred to or used in this submission (Add N/A if you did not use such resources)

  4. Please carefully read the following statement and include it in your email: “I hereby confirm that this submission is my work. I have cited above the origins of any parts of the submission that were taken from Websites, books, forums, blog posts, github repositories, etc. By including this in my email, I understand that I will be expected to explain my work in a video call with a Udacity coach before I can receive my verified certificate.”

Send items 1-4 above to ml-project@udacity.com. Within 7 days of your submission, you will receive an email from your project evaluator (who will not be the coach you’ve been working with) with a graded rubric and instructions for next steps.

If your project meets expectation, you will have an exit interview with us. The purpose of this interview is to discuss your analysis and verify that you are the person who created it. Don’t worry, this interview will not be difficult or stressful (assuming you are the person who made the analysis :) ).

For any further questions, please review the Udacity Project Submission FAQ or email your coach.