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 several retail clothing stores. The goal is to identify the patterns and segment the market based on the number of units sold in those 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...