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README.md

Modeling Housing Data with Linear Regression

Unit Project


Materials We Provide

Topic Description Link
Starter Code Project Description Here
Dataset Boston Housing Dataset (Provided as back-up to embedded data) Here

Project Objective

In this project, you'll build a linear regression (and KNN) model using guided prompts. You will be exposed to model validation using the train/test split (and K-Fold methods as a bonus) in order to fit different models on sample Boston housing data.

This project includes required and bonus objectives. You only need to complete the required portions, but you're encouraged to try as many of the bonus questions as possible!

For this project, you will be:

  • Creating a train/test split of the data.
  • Training each of your models on the training data.
  • Evaluating each of the models on the test data.
  • Ranking models by how well they scored on the testing data set.

Project Requirements

In a Jupyter Notebook, create working solutions for all of the required questions. Your notebook should include:

  1. Text for each question, copy and pasted from the starter code provided.

  2. A working solution to each problem.

    • Do not include test, practice, or broken code (unless you were unable to create a working solution).
  3. Comments for all of your code.

    • In your comments, describe any assumptions you made in order to solve these problems.
  4. Bonus: After completing the required portions, try your hand at the bonus sections for some additional challenges!


Rubric

For all projects, requirements will be evaluated on a simple point scale of 0, 1, or 2. Additionally, instructors will provide you with feedback on required portions of your project.

Score Expectations
0 Incomplete.
1 Does not meet expectations.
2 Meets expectations, good job!
3 Surpasses our wildest expectations!

Note: Scores of 2 mean that a requirement has been completely fulfilled, while 3 is typically reserved for bonus objectives.


Submission

You will submit your assignment by creating a repository in your github profile, hosting your materials, and sharing a link with your instructional team (Lead Instructor and Instructor Associate)

  • Please make sure to provide interpretation for all outputs!