Diamond Price Analysis using Python

A diamond is one of the most expensive stones. The price of diamonds varies irrespective of the size because of the factors affecting the price of a diamond. So, if you want to learn how to use your Data Science skills to analyze and predict the price of a diamond, this article is for you. In this article, I will take you through the task of Diamond Price Analysis and prediction using the Python programming language.

Diamond Price Analysis

To analyze the price of diamonds according to their attributes, we first need to have a dataset containing diamond prices based on their features. I found ideal data on Kaggle containing information about diamonds like:

1. Carat
2. Cut
3. Colour
4. Clarity
5. Depth
6. Table
7. Price
8. Size

You can download this dataset from here. In the section below, I will take you through the task of Diamond Price Analysis using Python.

Diamond Price Analysis using Python

Let’s start the task of diamond price analysis by importing the necessary Python libraries and the dataset:

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go

data = pd.read_csv("diamonds.csv")
print(data.head())

   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \
0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   
1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   
2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   
3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   
4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   

      z  
0  2.43  
1  2.31  
2  2.31  
3  2.63  
4  2.75  

This dataset contains an Unnamed column. I will delete this column before moving further:

data = data.drop("Unnamed: 0",axis=1)

Now let’s start analyzing diamond prices. I will first analyze the relationship between the carat and the price of the diamond to see how the number of carats affects the price of a diamond:

figure = px.scatter(data_frame = data, x="carat",
                    y="price", size="depth", 
                    color= "cut", trendline="ols")
figure.show()

We can see a linear relationship between the number of carats and the price of a diamond. It means higher carats result in higher prices.

Now I will add a new column to this dataset by calculating the size (length x width x depth) of the diamond:

data["size"] = data["x"] * data["y"] * data["z"]
print(data)

       carat        cut color clarity  depth  table  price     x     y     z  \
0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   
1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   
2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   
3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   
4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   
...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...   
53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50   
53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61   
53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56   
53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74   
53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64   

             size  
0       38.202030  
1       34.505856  
2       38.076885  
3       46.724580  
4       51.917250  
...           ...  
53935  115.920000  
53936  118.110175  
53937  114.449728  
53938  140.766120  
53939  124.568444  

[53940 rows x 11 columns]

Now let’s have a look at the relationship between the size of a diamond and its price:

figure = px.scatter(data_frame = data, x="size",
                    y="price", size="size", 
                    color= "cut", trendline="ols")
figure.show()

The above figure concludes two features of diamonds:

1. Premium cut diamonds are relatively large than other diamonds
2. There’s a linear relationship between the size of all types of diamonds and their prices

Now let’s have a look at the prices of all the types of diamonds based on their colour:

fig = px.box(data, x="cut", 
             y="price", 
             color="color")
fig.show()

Now let’s have a look at the prices of all the types of diamonds based on their clarity:

fig = px.box(data, 
             x="cut", 
             y="price", 
             color="clarity")
fig.show()

Now let’s have a look at the correlation between diamond prices and other features in the dataset:

correlation = data.corr()
print(correlation["price"].sort_values(ascending=False))

price    1.000000
carat    0.921591
size     0.902385
x        0.884435
y        0.865421
z        0.861249
table    0.127134
depth   -0.010647
Name: price, dtype: float64

Diamond Price Prediction

Now, I will move to the task of predicting diamond prices by using all the necessary information from the diamond price analysis done above.

Before moving forward, I will convert the values of the cut column as the cut type of diamonds is a valuable feature to predict the price of a diamond. To use this column, we need to convert its categorical values into numerical values. Below is how we can convert it into a numerical feature:

data["cut"] = data["cut"].map({"Ideal": 1, 
                               "Premium": 2, 
                               "Good": 3,
                               "Very Good": 4,
                               "Fair": 5})

Now, let’s split the data into training and test sets:

#splitting data
from sklearn.model_selection import train_test_split
x = np.array(data[["carat", "cut", "size"]])
y = np.array(data[["price"]])

xtrain, xtest, ytrain, ytest = train_test_split(x, y, 
                                                test_size=0.10, 
                                                random_state=42)

Now I will train a machine learning model for the task of diamond price prediction:

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
model.fit(xtrain, ytrain)

Now below is how we can use our machine learning model to predict the price of a diamond:

print("Diamond Price Prediction")
a = float(input("Carat Size: "))
b = int(input("Cut Type (Ideal: 1, Premium: 2, Good: 3, Very Good: 4, Fair: 5): "))
c = float(input("Size: "))
features = np.array([[a, b, c]])
print("Predicted Diamond's Price = ", model.predict(features))

Diamond Price Prediction
Carat Size: 0.60
Cut Type (Ideal: 1, Premium: 2, Good: 3, Very Good: 4, Fair: 5): 2
Size: 40
Predicted Diamond's Price =  [937.13946429]

Summary

So this is how you can use your Data Science skills for the task of diamond price analysis and prediction using the Python programming language. According to the diamond price analysis, we can say that the price and size of premium diamonds are higher than other types of diamonds. I hope you liked this article on Diamond Price analysis and prediction using Python. Feel free to ask valuable questions in the comments section below.