Content
You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data.

Problem Statement
You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.

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Files: ['Mall_Customers.csv']

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6

EDA

Scale Data

Find the Optimal K

Elbow method

Silhouette Score Method

K=2, Silhouette Score=0.3355
K=3, Silhouette Score=0.3579
K=4, Silhouette Score=0.4040
K=5, Silhouette Score=0.4085
K=6, Silhouette Score=0.4311
K=7, Silhouette Score=0.4101
K=8, Silhouette Score=0.3674
K=9, Silhouette Score=0.3744
K=10, Silhouette Score=0.3619

The optimal number of clusters is 6

C:\Users\syarn\AppData\Local\Temp\ipykernel_31460\3546621768.py:8: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.
  cmap = cm.get_cmap('tab10')  # supports up to 10 distinct colors

The analysis indicates 6 as the optimal number of clusters, so we segment the customers into six groups.

	Age	Annual Income (k$)	Spending Score (1-100)	Cluster
0	19	15	39	4
1	21	15	81	4
2	20	16	6	5
3	23	16	77	4
4	31	17	40	5