Diamonds-Price-Prediction

1 Intro:

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

2 Goals:

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

3 Steps:

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)

4 Final Output:

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