sakshidubey2 / p1-1-predicting-diamond-prices

Predicting-Diamond-Prices

This is the first project of Business Analyst Nanodegree program "Predicting Diamond Prices".

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, we will use the results from a predictive model to make a recommendation on how much the jewelry company should bid for the diamonds.

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. I, 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 linear regression model provides an equation that can be used 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.

Both datasets contain carat, cut, and clarity data for each diamond. Only the diamonds.csv dataset has prices. We'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: I1, 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 I’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.

Step 3 – Make a recommendation: Now we you 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 our recommended bid price should represent that.

Results

After summing up the price for each diamond, the recommended price that we will get is $8213465.