Exploratory Data Analysis is performed to identify the customer behaviour pattern and also to answer some of the business questions.
- Installation
- Project Description
- File Descriptions
- Results
- Future Work
- Licensing, Authors, and Acknowledgements
This code will run with no issues on Python 3.* versions. Incase, you do not have squarify please do install using "pip install squarify" squarify
Also, due to the data volume is huge and not able to upload, please download the data (2019-Oct.csv) from Kaggle here.
In this project, I will use behaviour data from a multi-category eCommerce store taken from Kaggle, perform Exploratory Data Analysis to answer below business questions.
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What is the daily traffic in Oct?
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What product category and brands are more popular in viewing and purchasing?
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Customer Purchase behaviour (such as number of customers just viewing products or number of customers adding products to their shopping cart and number of customer who actually buy the product)?
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Amount spent by customers who purchased the products?
EDA_on_ecommerce_website.ipynb - The notebook is exploratory in searching through the data pertaining to the questions showcased by the notebook title. Markdown cells & comments were used to assist in describing a cell and the code within it.
The analysis and insights are shared in the IPython file using markdown cells and comments.
As this is just the analysis of the dataset, I am currently working on applying Machine Learning models to predict whether customers buy the product when they add it to their cart. This can be done using Regression models, can test the accuracy levels comparing output of different Machine Learning models and can also decide which model is the best fit for this data.
Must give credit to REES46 Marketing Platform & Kaggle for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here.