This project explores factors that influence how often people shop online using survey-based data analytics. The analysis focuses on demographic characteristics, shopping frequency, shopping preference, and perceived importance of factors such as website quality, security, delivery, convenience, and product range.
The project investigates consumer online shopping behaviour through descriptive statistics, probability-based analysis, chi-square testing, correlation analysis, and data visualisation. The goal is to identify patterns in online shopping activity and understand which factors are linked to more frequent online shopping.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SciPy
- Data cleaning and preparation
- Descriptive statistics
- Conditional probability and Bayes’ theorem
- Chi-square test for independence
- Chi-square goodness-of-fit test
- Correlation analysis
- Data visualisation
- Online shopping frequency varies across respondents
- Shopping preference showed a stronger relationship with frequent online shopping
- Security, convenience, and delivery were important perceived factors
- The project highlights how statistical analysis can support better understanding of consumer behaviour
data_analytics_project.py– Python analysis scriptData Analytics_Project_Report.pdf– project report
This project demonstrates practical skills in statistical data analysis, hypothesis testing, visualisation, and interpretation of consumer behaviour data.