Segmenting the market based on shopping patterns

Let's see how to apply unsupervised learning techniques to segment the market based on customer shopping habits. You have been provided with a file named sales.csv. This file contains the sales details of a variety of tops from a number of retail clothing stores. Our goal is to identify the patterns and segment the market based on the number of units sold in these stores.

Create a new Python file and import the following packages:

import csv 
 
import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.cluster import MeanShift, estimate_bandwidth 

Load the data from the input file. Since it's a csv file, we can use the csv reader in python to read the data from this file and convert it into a NumPy array:

# Load data from input file 
input_file = 'sales.csv' 
file_reader = csv.reader(open(input_file, 'r'), delimiter=',') 
 
X = [] 
for count, row in enumerate(file_reader): 
    if...