mall_customer_analysis_Rprogramming

The aim of this project is to analyze the spending behavior of customer groups using various techniques.

Hypothesis

Hypothesis 1 : Female customers have a higher spending score than male customers.

• Aim 1: Analyze the average spending score by creating a box plot for both genders by grouping the data by gender
• Aim 2: Conduct a statistical test (Two Sample T-test) to determine if the difference in spending scores between the genders is statistically significant.

Hypothesis 2: There is a positive relationship between annual income and spending score.

• Aim 1: Generate a scatter plot to visually examine the relationship between annual income and spending score.
• Aim 2: Linear Regression analysis to examine the relationship between annual income and spending score.
• Aim 3: K-means clustering to investigate the distribution of the relationship between annual income and spending score

Hypothesis 3: There is a positive relationship between middle-aged customers and spending score.

• Aim 1: Categorized the age data into relevant groups (Teenager,Young A-B,Middle Age A-B,Elder A-B,Above 70) then visualise as boxplot and finding the spending means of each class to analyzed the average spending scores.
• Aim 2: To examine the age group with the highest spending score, linear regression and correlation.

Conclusion

Hypothesis 1

• There is no significant difference between the spending scores of males and females according to boxplot and two sample t-test.

Hypothesis 2

• There is no positive relationship between annual income and spending score. K-means clustering result shows that different clusters have different annual income and spending scores.

Hypothesis 3

• It cannot be said that the highest spending score is directly had by middle-aged people. There is a negative relationship between age and spending score, which is determined by correlation matrix and linear regression. It can be said that people aged 20-40 have the highest spending score

References

• Zahair, A. (2022). Retrieved from https://www.corporatevision-news.com/5-customer-segmentation-analysis-methods-for-business-growth/
• Rashmi, Kassambara 06 May 2020, The demo data used in this tutorial is available in the default installation of R. Juste type data(“USArrests”)Reply, & Kassambara. (2018). K-means clustering in R: Algorithm and practical examples. Retrieved from https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/
• (2022). Retrieved from https://www.geeksforgeeks.org/how-to-create-correlation-heatmap-in-r/
• Mall Customer Segmentation Data. (2018, August 11). Kaggle. https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python