Description: This project, the 0th project of my Udacity Predictive Analytics for Business Nanodegree, involves using a predictive model to recommend a bidding price for a jewelry company interested in purchasing a set of 3,000 diamonds. By applying a linear regression model, the project aims to estimate the prices of diamonds based on their attributes such as carat, cut, and clarity. The project involves understanding the data, calculating predicted prices, creating scatter plots, and making a bid recommendation for the company.
Key Points:- Project Type: Udacity Nanodegree Project- Objective: Predict diamond prices and make a bid recommendation for a jewelry company- Dataset: diamonds.csv (used for building the regression model) and new_diamonds.csv (contains data for diamonds to be purchased)- Variables: Carat (numerical), Cut (categorical, transformed into ordinal), Clarity (categorical, transformed into ordinal)- Steps: 1. Understand the data and the regression model equation. 2. Calculate the predicted price for each diamond using the linear model equation. 3. Make a bid recommendation for the entire set of diamonds based on the predicted prices.- Project Submission: PDF file with answers to questions related to understanding the model, data visualization, and bid recommendation.
Note: The model's predicted prices represent the final retail prices, and the company purchases diamonds at 70% of that price.