Diamond Price Prediction Model

Introduction About the Data :

The dataset The goal is to predict price of given diamond (Regression Analysis).

There are 10 independent variables (including id):

• id : unique identifier of each diamond
• carat : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.
• cut : Quality of Diamond Cut
• color : Color of Diamond
• clarity : Diamond clarity is a measure of the purity and rarity of the stone, graded by the visibility of these characteristics under 10-power magnification.
• depth : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface)
• table : A diamond's table is the facet which can be seen when the stone is viewed face up.
• x : Diamond X dimension
• y : Diamond Y dimension
• x : Diamond Z dimension

Target variable:

• price: Price of the given Diamond.

Dataset Source Link : https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv

Bar Graphs Showing Relation between Categorical Features and Price:

Price Vs Cut

Price Vs Clarity

Price Vs Color