Price Predictive Analysis of Diamond Using Python: All You Need to Know

Diamond Using Python

Python is an object-oriented high-level programming language. Object-oriented languages focus on data and objects rather than focusing on logic and attributes. In easy words, if you are to design a program that builds a house, the doors, walls, floor, and other attributes are considered objects and have their own pattern of behavior.

The level of a programming language refers to the narration that a programming language needs to employ. Being high-level doesn’t necessarily mean better. It only means that Python requires a compiler to turn the code into machine language.

Python, being one of the most easy-to-learn and readable programming languages, is used to predict the prices of diamonds. Machine learning models available in Python are employed to determine the optimum price for a cut diamond.

How Diamond Prices Are Predicted

In general, lab grown diamonds are the ones that require a price prediction analysis. Lab grown diamonds are manufactured in controlled environments where it’s easier to get finished products that are without blemishes and deformities.

The lab grown diamonds may look and feel the same as natural diamonds, but they, not being mined, lacks the luxury value that natural diamonds hold.

As the natural diamond already has a large market, there is generally a demand-supply equilibrium in these markets. But this is not the case with lab grown diamonds. However, using accurate data on both types of diamond prices can be analyzed in Python.

In this article, the correlation between the diamond pieces and pricing is analyzed with machine learning models in Python. A correlation is a measure of how one variable affects another variable e.g. how the different attributes of a diamond piece influence the price of the diamond. Here’s how it’s done.

Importing the Available Datasets

The first step towards developing a machine learning model is to have access to accurate datasets. For diamonds, these datasets are extracted and imported from sources that certify each diamond, like GIA.

Typically, 9-10 data points are considered in a diamond dataset. The most common data points as well as their brief explanation are as follows:

Carat weight

Carat weight isn’t the same as the volume of the diamond. It’s just the mass of a diamond piece.


Referring to the certifications, ‘ideal’, ‘premium’, ‘very good’, ‘good’, and ‘fair’ cuts of the diamonds are used in the machine learning algorithm.


Each color quality is associated with a variable that denotes the color quality of the diamonds.


Datasets are arranged with respect to the inclusions and blemishes on the diamond. These attributes are also sourced from the certifications.

Table Size

Table size denotes the flat surface at the top of the diamond. It’s expressed as the percentage of its average diameter.


Length of the diamond


Depth is the total height of the diamond from the table to the culet.


Width is the average width of the diamond in mm.


Known price of the diamonds

You may find it surprising as to why the price is being included in the datasets as it should be the target variable; but to train a machine learning model, you need to associate the price of the diamonds with known attributes.

Many other attributes may also be considered depending upon the complexity of the machine learning model. The more data is used to train the model, the more accurate the machine learning model becomes.

Handling the Irregular Data

After you’ve imported the data from the available datasets to train the machine learning model, it’s time to find the NaN (null value) and zero values and strip them out of the equation. You may also replace them with mean or median values, but if you have thousands of datasets it’s futile.

For diamond datasets, it doesn’t make sense if the width of a diamond is zero. It’s also the same for all other attributes mentioned here. If a significant portion of the datasets has values that are irregular or inaccurate, the end results of deploying a machine learning model may not be optimum.

Therefore, it is better to drop unnamed or irregular rows before starting with the next process.

Clerical errors also contribute to the inaccuracy of the models. A diamond with a 0.00002mm height or a 2457.85mm width doesn’t make sense. These values shall be dropped from the training set even if they aren’t zero.

In order to make sure that that dataset has no irregular values,scale the values in a graphical plot. If the values are distributed over a small scale, they are accurate and can be used to train the model.

Finding Correlation Between Features and Price

The objective of training a price prediction model is to find the correlation between the attributes and the pricing trends of diamonds. The model helps to find the answer to the questions like which attributes contribute to the pricing more than others and where should the marketing departments focus.

This model, with many others, has been developed to find the correlation between the attributes and prices of diamonds.

Below chart shows the effect of each attribute on prices of the diamond. The darker the color of an attribute is, the more it has an impact on pricing.

Finding Correlation Between Features and Price

Carat Vs Price

Carat has the most significant impact on diamond pricing. Since the larger stones are rare, the change in price is found to be exponential. With the increase in carat weight, the price of diamonds goes up significantly for the same quality.

In search of bigger diamonds, we often risk losing quality to keep ourselves within budget. A smaller diamond with better quality in an optimum setting looks better and brighter than a cheaper bigger diamond.

Cut Vs Price

With a higher cut quality (ideal or premium), the price of a diamond increases significantly. The relation, although isn’t as drastic as the carat weight, is linear. This phenomenon predominantly is based on wastage. Rough diamonds need to be removed and wasted more to achieve a greater cut quality.

Color Vs Price

A strange correlation is noticed between color and pricing. While the colorless diamonds on average are more expensive, near-colorless and very light yellow diamonds are found to be equally or more expensive in some cases.

Clarity Vs Price

Diamond clarity is based on the blemishes inside the diamond. The blemishes can occur due to a crack or trapped minerals inside the pieces. VS1 and VS2 diamonds are the most expensive ones followed by SI1 and SI2 clarity grade diamonds.

I1 grade diamonds can reach similar pricing, but they lack in the lower end where the demand for these diamonds is not significant.

Depth Vs Price

The depth of the diamond doesn’t hold much weight in terms of affecting the price. It’s because a more-than-optimum smaller or larger depth can make the diamonds appear darker. The reflections are either lost or can’t reach their full potential with suboptimal depths. The pricing trend shows an inverse relationship between the depth and price.

Table Size Vs Price

If the table is too large, the light doesn’t reflect on the crown angles as effectively and fails to generate sparkly rainbows. But a smaller table traps the reflections and forces the rays to leak out from other places hidden beneath the diamond jewelry.

The Bottom Line

Manual price analysis of diamonds lacks the accuracy and pace that automation and machine learning can achieve. Python analysis of diamonds has opened ways for sellers and buyers to get the best value for their products and money, respectively, without going through the complex traditional process of analysis.

Between carat and price, the correlation is the highest. The greater carat size attracts better prices. Better cut quality, clarity, and color of the diamonds also contribute to the overall price setting of diamonds. The depth and the table have minimal effect on the pricing as a suboptimal depth and table can trap the reflections; making the diamond appear dull.

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About the Author: John Vick

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