The main purpose of this project is to build a machine learning model to predict the price of diamond based on its features
Diamond forms under high temperature and pressure conditions that exist only about 100 miles beneath the earth’s surface. Diamond’s carbon atoms are bonded in essentially the same way in all directions. Another mineral, graphite, also contains only carbon, but its formation process and crystal structure are very different. Diamonds have been used as decorative items since ancient times; some of the earliest references can be traced back to 25,000–30,000 B.C. Facts Mineral: Diamond Chemistry: C Color: Colorless Refractive Index: 2.42 Birefringence: None Specific Gravity: 3.52 (+/-0.01) Mohs Hardness: 10 Currently, gem production totals nearly 30 million carats (6.0 tonnes; 6.6 short tons) of cut and polished stones annually, and over 100 million carats (20 tonnes; 22 short tons) of mined diamonds are sold for industrial use each year, as are about 100 tonnes (110 short tons) of synthesized diamond. Diamonds are such a highly traded commodity that multiple organizations have been created for grading and certifying them based on the “four Cs”, which are color, cut, clarity, and carat. You can read about “four Cs” from this link.
Decision tree Regression proved to be the best recipe for a diamond’s price prediction which was shortly followed by Neural Networks and Polynomial regression of degree 2.