Perform careful statistical analysis using R to perform data description, perform hypothesis tests, run multiple linear regression or classification models,

Perform careful statistical analysis using R to perform data description, perform hypothesis tests, run multiple linear regression or classification models, do time series forecasting, perform factor analysis and do forecasting and support your findings using real data analysis.

Data Set to be choosen: dataset

Also please consider this proposal for initial understanding of the project.

Proposal: Importance to Business:

This project is crucial for tailoring credit offerings, optimizing credit limits, and minimizing the defaults. By understanding the customer behavior through this data analysis, the business can enhance credit risk assessments, improve their targeted marketing, and develop personalized credit products, leading to increased customer retention and profitability to the business.

Description of Dataset:

The dataset comprises records from a credit card company which includes customer IDs, balance amounts, purchase types, cash advances, payment behaviors, credit limits, and the tenure. It comprises of various quantitative and categorical variables reflecting the financial behavior of credit card users.

Analysis Plan:

Data Cleaning: Handling missing values, outliers, and errors from the dataset.

Descriptive Statistics: Summarizing central tendencies, and distributions of financial indicators.

Correlation Analysis: Identifying relationships between different financial behaviors of users

Methodologies:

Hypothesis Testing: To determine the significance of observed behaviors from the different customer groups.

Multiple Linear Regression: To identify all the factors influencing credit usage and repayment between the users.

Results E xpecting:

Customer Segmentation: Identification of distinct groups based on their spending habits and payment behaviors of these users.

Insights Predicted: Models predicting payment, typical spending patterns, and credit usage.

Behavioral Trends: Insights into how different the customer segments may evolve over the time by informing proactive credit management strategies.

Expectations from the Project : Make sure to include EDA analysis, general regression modeling, and compare with advanced machine learning modeling, etc

Submission Type : Exceutive Summary Word Document (3-4 Pages) ,

Appendix word Document: Detailed explanation of the project (7-8 Pages )

R code file

PPT Presentation (11-13 slides) excluding introduction and Thank you page

PPT Explanation document and R code explanation document (2-3 pages)

Make sure to cite all the references used in the summary as well as appendix.

Please make sure to proceed project without any plagiarism and delays.