In this repository you will find 5 notebooks:

• Descriptive Analysis: a detailed descriptive analysis of the dataset
• Linear Models Dummies: the cleaned dataset enconded with dummy variables and trained and tested in linear regression models
• Linear Models Ordinal: the cleaned dataset enconded with ordinal variables and trained and tested in linear regression models
• Non Linear Models Dummies: the cleaned dataset enconded with dummy variables and trained and tested in non linear regression models
• Non Linear Models Ordina: the cleaned dataset enconded with ordinal variables and trained and tested in non linear regression models

The goal of the project is to predict the price of diamonds based in its carat, cut, color, clarity, depth%, table and volume/size. The measure unit is rmse

To fulfil the previous goals the next steps have been done:

• INPUT (2 datasets from Kaggle (1 to train, 1 to test))
• src (additional info as diamond images, diamond variables schema and possible models to implement)
• main (the 5 notebooks explained previously)
• OUTPUT (the predicted price of the diamonds of the best models)

The final output are the predicted prices of the best models. The best score (in rmse) was achieved with an Extra Trees Model, with a previous dummy encoding (one hot encoding). The score was: rmse = 532.01097