This project explores the factors influencing consumers' online purchase intentions during crises in Sri Lanka, using survey data collected from various demographics.
The goal is to:
- Clean and preprocess the survey dataset
- Assess reliability and validity of constructs (using Cronbach’s Alpha and Inter-Item Correlation)
- Create composite variables (e.g., Perceived Ease of Use, Perceived Usefulness)
- Perform exploratory data analysis (EDA)
- Build predictive models (e.g., logistic regression, random forest) to predict purchase intentions
- Interpret the behavioral and cognitive factors influencing online purchasing during crises.
To set up the environment and run the code:
- Clone the repository
git clone https://github.com/your-repo/online-purchase-intention-project.git
cd online-purchase-intention-project- Create a virtual environment
python -m venv env- Activate the environment
- On Windows:
env\Scripts\activate- On Mac/Linux:
source env/bin/activate- Install required packages
pip install -r requirements.txt| Folder | Description |
|---|---|
data/raw/ |
Original survey dataset (not modified) |
data/cleaned/ |
Processed, cleaned data ready for analysis |
notebooks/ |
Jupyter notebooks for each project phase |
scripts/ |
Python scripts for cleaning, feature engineering, utilities |
outputs/figures/ |
Graphs and plots |
outputs/models/ |
Trained machine learning models |
references/ |
Research papers, project description |
report/ |
Final report drafts and presentations |
- Data Inspection and Cleaning
- Handling Missing Values and Encoding
- Reliability and Validity Checks
- Feature Engineering (Composite Variables)
- Exploratory Data Analysis (EDA)
- Predictive Modeling
- Reporting and Presentation
- 220168R – FERNANDO N.P.A.
- 220153R – EKANAYAKA D.M.Y.N.B.
- 220222E – HENDALAGE D.S.D.
- 220263E – JAYASINGHE M.M.S.
- 220731M – WITHANAGE W.I.N
- University course: Introduction to Data Science (CS3121)
- Lecturer: Dr. Nisansa de Silva
- Lecturer: Dr. Sandareka Wickramanayake