Example: Predicting Home Prices The purpose of this section is to give you an idea of what a finished project should look like. This example uses a different data set than the one you will be using for the project but it follows the same pattern. The scenario for this example project is that a group of real estate investors are looking to put a bid on a group of 10 homes that have come up for sale. They want a 20% margin so they can make a profit when they resell the homes. Your job is to predict the price for the homes up for bid and then factor in the margin to give the investors a final bid amount to make for these houses. At the bottom of the page you will see a project submission template that has a set questions for you to answer. You will also see a completed project submission for your review. Also, at the bottom of the page you will find an Excel workbook. One of the sheets in that workbook contains the past home sales (Past Sales Data). The past home sales data was used to build a linear regression model, which produced a formula to help predict a home's value based tells on the number of bedrooms, bathrooms, and the square footage of the home. That formula is: price = 51880.41 + 44.72 \ *square feet + 52613.9 * bedrooms + 27513.48 * bathrooms (This is based on fake data) This formula is then applied to the homes up for bid, which you can see in the predicted price column in the Homes up for Bid worksheet. Lastly, Data Combined for Graph shows how to graph bedroom vs. price for both the past home sales and predicted prices of the homes up for bid. Please use this project submission as an example to help you on the Predicting Diamond Prices Project. Supporting Materials Excel Workbook Submission Template . Completed Report Project Details Predicting Diamond Prices This project is designed for three main reasons: • • • To give you a feel for what you'll be doing throughout the Nanodegree Program To introduce you to Udacity's project submission and review process To make sure you feel comfortable with the basics before you begin. If it feels too easy, don't worry. We have some great stuff in store for you. Project Overview A jewelry company wants to put in a bid to purchase a large set of diamonds, but is unsure how much it should bid. In this project, you will use the results from a predictive model to make a recommendation on how much the jewelry company should bid for the diamonds. US Number System All numbers that will be presented in this Nanodegree program will be based on the US numbering system where 5,269 is "five thousand two hundred sixty nine" and 158.1 is "one hundred fifty eight point one" where 1 is a decimal number. This is very important so please take note of this. Project Details A diamond distributor has recently decided to exit the market and has put up a set of 3,000 diamonds up for auction. Seeing this as a great opportunity to expand its inventory, a jewelry company has shown interest in making a bid. To decide how much to bid, the company's analytics team used a large database of diamond prices to build a linear regression model to predict the price of a diamond based on its attributes. You, as the business analysts, are tasked to apply that model to make a recommendation for how much the company should bid for the entire set of 3,000 diamonds. The following diagram represents the analysis at a high level. Since the model is already built, your analysis will focus on the right side of the diagram. Diamonds (Predictors & Price) New Diamonds (Predictors only) Regression Equation Predicted Diamond Prices The linear regression model provides an equation that you can use to predict diamond prices for the set of 3,000 diamonds. The equation is below: **Price** = -5,269 + 8,413 x **Carat** + 158.1 x **Cut** + 454 x **Clarity** Step 1 · Understand the data: There are two datasets. diamonds.csv contains the data used to build the regression model. new_diamonds.csv contains the data for the diamonds the company would like to purchase. carat cut cut_ord color clarity clarity_ord price 0.51 Premium 4F VS1 4 1749 2.25 Fair 1G |1 1 7069 0.7 Very Good 3 E VS2 5 2757 0.47 Good 2F VS1 4 1243 0.3 Ideal 5G VVS1 7 789 0.33 Ideal 5D SI1 3 728 2.01 Very Good 3 G SI1 3 18398 0.51 Ideal 5F VVS2 6 2203 1.7 Premium 4D SI1 3 15100 0.53 Premium 4D VS2 5 1857 Both datasets contain carat, cut, and clarity data for each diamond. Only the diamonds.csv dataset has prices. You'll be predicting prices for the new diamonds.csv dataset. • Carat represents the weight of the diamond, and is a numerical variable. Cut represents the quality of the cut of the diamond, and falls into 5 categories: fair, good, very good, ideal, and premium. Each of these categories are represented by a number, 1-5, in the Cut_Ord variable. Clarity represents the internal purity of the diamond, and falls into 8 categories: 11, SI2, SI1, VS1, VS2, VVS2, VVS1, and IF. Each of these categories are represented by a number, 1-8, in the Clarity_Ord variable. Note: Transforming category variables to ordinal variables like this is not always appropriate, but we've done it here for simplicity. Step 2- Calculate the predicted price for diamond: For each diamond, plug in the values for each of the variables into the linear model (equation). Then solve the equation to get the estimated, or predicted, diamond price. We suggest using a spreadsheet tool like Excel, Numbers, or Google Sheets. You could also do it in Alteryx and/or Tableau if you already have your license. - Step 3 – Make a recommendation: Now that you have the predicted price for each diamond, it's time to calculate the bid price for the whole set. Note: The diamond price that the model predicts represents the final retail price the consumer will pay. The company generally purchases diamonds from distributors at 70% of that price, so your recommended bid price should represent that. Project Submission To complete this project, you will be submitting a file in pdf format that contains the answers to the following questions across three steps. Step 1 - Understanding the Model: • According to the linear model provided, if a diamond is 1 carat heavier than another with the same cut and clarity, how much more would the retail price of the heavier diamond be? Why? . If you were interested in a 1.5 carat diamond with a Very Good cut (represented by a 3 in the model) and a VS2 clarity rating (represented by a 5 in the model), what retail price would the model predict for the diamond? Step 2 - Visualize the Data: Create two scatter plots. If you're not sure what a scatter plot is, see here (opens in a new tab) + Plot 1 - Plot the data for the diamonds in the database, with carat on the x-axis and price on the y-axis. . Plot 2 - Plot the data for the diamonds for which you are predicting prices with carat on the x-axis and predicted price on the y-axis. • Note: You can also plot both sets of data on the same chart in different colors. What strikes you about this comparison? After seeing this plot, do you feel confident in the model's ability to predict prices? Step 3 - The Recommendation: What bid do you recommend for the jewelry company? Please explain how you arrived at that number. Supporting Materials Use the submission template to submit your project. The submission template is available at the bottom of this page under Supporting Materials. Data • diamonds.csv - contains carat, cut, clarity, and price information for each diamond in the dataset used to build the regression model. • new_diamonds.csv - contains carat, cut, and clarity information for the diamonds the company would like to purchase. Spreadsheet Calculations Throughout this Nanodegree you will be presented with problems that require you to make calculations. While you are free to use whatever analytical tool of