I have created Machine Learning Model with K nearest neighbour of DIAMOND PRICE PREDICTION.
In this I've used Python’s Famous libraries like Numpy, Pandas, Matplotlib, Seaborn, Imblearn, Sklearn and much more for Analysis, Vizualization and Model Development.
I've used Jupyter Notebook for coding!
Download the dataset from Kaggle!
A data frame with 53940 rows and 10 variables:
price
price in US dollars (\$326--\$18,823)
carat
weight of the diamond (0.2--5.01)
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))
x
length in mm (0--10.74)
y
width in mm (0--58.9)
z
depth in mm (0--31.8)
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)
.png)
