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Diamonds - Part 1 - In the rough - An Exploratory Data Analysis

In this case study, we will explore the diamonds dataset, then build linear and non-linear regression models to predict the price of diamonds. Data Description The diamonds dataset contains the prices in 2008 USD terms, and other attributes of almost 54,000 diamonds. Attribute Description price price in 2008 USD carat weight of a diamond (1 carat = 0.2 gms) cut quality of the cut (Fair, Good, Very Good, Premium, Ideal) color diamond color from D (best) to J (worst) clarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)) x length in mm y width in mm z depth in mm depth total depth percentage = z/mean(x, y) table width of the top of diamond relative to widest point Data Summaries A preliminary visual summary of the whole dataset shows all the features and their types.