Diamonds Project

Overwiew

In this project, I made an exploratoy analysis of a diamonds dataset with pandas, matplotlib, seaborn and tableau. The main goal was to analyse how the distinct variables affect to the price of the diamond and create a model to predict the price of another dataset of diamonds that we didn´t had the price.

Data

• Diamonds Dataset

The dataset has 40.455 diamonds and ten rows with their characteristics:

• Carat: weight of the diamond
• Cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)
• Color: diamond colour, from J (worst) to D (best)
• Clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
• Depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)
• Table: width of top of diamond relative to widest point (43--95))
• Price: price in USD
• x: length in mm
• y: width in mm
• z: depth in mm

Steps

• Explore the dataset with pandas.
• Analyse the statistical values.
• Graphics with seaborn matplotlib for categorical and numerical variables.
• Hypothesis testing grouping variables.
• Report with tableau.
• Making new features
• Create and try ML models and see which one performs better.

📁 Folder structure

└── ih_datamadpt1120_project_m2
    ├── .gitignore
    ├── README.md
    ├── notebooks
               ├── data_analysis_report.ipynb
               ├── ML-models.iypnb