This is to analyse a dataset reagarding BlackFriday, you can download it from Kaggle
dataset was downloaded from https://www.kaggle.com/mehdidag/black-friday/kernels
@authors are Hao Cong (conghao321@vip.qq.com) and Amanda Chiu (chc10258@gmail.com), who study in Tamkang University (Taiwan) in 2019.
This project is to analyze the purchase data and make some visualizations regarding this data.
Code is running on Python 3.6 and using scikit-learn apis.
However, there may be still many drawbacks or errors, if you find any errors please feel free to contact us
The dataset was 'Black Friday', and it was downloaded from Kaggle.
In the visualization.py:
you can find some procedures about visualization, it compares different groups like visualization of the purchase grouped by age and genders. Before that , we use excel to extract some useful data, if you want to do that by python, you can just simply use function : 'groupby'. To demonstrate that, we use it in the result visualization in the 'models and prediction.py'
In the 'models and prediction.py': you can find the comparison of differenct models, including 'Linear Regression, Decision Tree, Random Forests and Xgboost', as you can guess, xgboost got the highest score. We also save this xgboost model as model.m file, thus you can easily load it later.
In the result visualization, we also illustrated the prediction of the data