Customer-Segmentation-Kmeans-Clustering

Purpose

1. Perform an exploratory analysis on the dataset.
2. Apply K-means clustering algorithm in order to segment customers.
3. Customer Segmentation : Dividing customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.

Data Set Information

The csv file corresponding used herein was downloaded from Kaggle repository. The dataset contains data about customers from a Mall. The information gather is: Customer ID, Age, Annual Income (k$), Spending Score (1-100)

Analysis

Analyzing Data using the above graph becomes much more easier as it gives us a visual aid for better understanding of the data. Kmeans has divided the dataset into 5 clusters based on Annual income and the spending scores of the individual customers. The following clusters are created by the model,

1.Cluster Orange 2.Cluster Blue 3.Cluster Purple 4.Cluster Red 5.Cluster Green

Observation and Conclusion:

• 1.Cluster Orange :Unsatisfied Customers Earning high and spending less. We see that people have high income but low spending scores, this is interesting. Maybe these are the people who are unsatisfied or unhappy by the mall’s services. These can be the prime targets of the mall, as they have the potential to spend money. So, the mall authorities will try to add new facilities so that they can attract these people and can meet their needs.

• 2.Cluster Blue - Spenders This type of customers earns less but spends more Annual Income is less but spending high, so can also be treated as potential target customer we can see that people have low income but higher spending scores, these are those people who for some reason love to buy products more often even though they have a low income. Maybe it’s because these people are more than satisfied with the mall services. The shops/malls might not target these people that effectively but still will not lose them.

• 3.Cluster Purple - Target Customers : Earning high and also spending high Target Customers. Annual Income High as well as Spending Score is high, so a target consumer. we see that people have high income and high spending scores, this is the ideal case for the mall or shops as these people are the prime sources of profit. These people might be the regular customers of the mall and are convinced by the mall’s facilities.

• 4.Cluster Red - Normal Customer : Customers are average in terms of earning and spending An Average consumer in terms of spending and Annual Income we see that people have average income and an average spending score, these people again will not be the prime targets of the shops or mall, but again they will be considered and other data analysis techniques may be used to increase their spending score.

• 5.Cluster Green - Balanced Customers : They earn less and spend less. We can see people have low annual income and low spending scores, this is quite reasonable as people having low salaries prefer to buy less, in fact, these are the wise people who know how to spend and save money. The shops/mall will be least interested in people belonging to this cluster.1.Cluster Orange :Unsatisfied Customers Earning high and spending less. We see that people have high income but low spending scores, this is interesting. Maybe these are the people who are unsatisfied or unhappy by the mall’s services. These can be the prime targets of the mall, as they have the potential to spend money. So, the mall authorities will try to add new facilities so that they can attract these people and can meet their needs.

Dependencies

• Pandas
• NumPy
• Matplotlib
• Seaborn
• pca
• Sklearn