😊🎉 Predict Price Of Diamond By Regression 😊🎉
My primary objective in this problem is to accurately predict the price of diamonds by analyzing a comprehensive set of their defining characteristics. Diamonds, prized gemstones renowned for their brilliance and rarity, are formed deep within the Earth's mantle over billions of years under intense pressure and heat. The value of a diamond is influenced by a multitude of factors, including the famous "Four Cs": carat weight, cut, color, and clarity. Carat weight refers to the diamond's size, with larger diamonds generally commanding higher prices. The cut refers to the diamond's proportions and how well it has been shaped and faceted, affecting its brilliance and sparkle. Color is graded on a scale from D (colorless) to Z (light yellow or brown), with colorless diamonds being the most valuable. Clarity assesses the presence of internal or external flaws, known as inclusions and blemishes, respectively, with higher clarity grades indicating a higher level of purity. Additional features that contribute to the overall value of a diamond include fluorescence, which refers to the diamond's tendency to emit a soft glow under ultraviolet light, and the presence of any fancy colors, such as pink, blue, or yellow. The diamond's certification from reputable gemological laboratories, such as the Gemological Institute of America (GIA) or the International Gemological Institute (IGI), also plays a crucial role in determining its value and authenticity. By employing various regression algorithms like LinearRegression, KNeighborsRegressor, DecisionTreeRegressor, and RandomForestRegressor, I aim to develop a predictive model that considers these diverse diamond characteristics to estimate their prices accurately.