An internship project at Vistex focused on leveraging data science techniques to analyse and infer market trends for optimal list price adjustments. This repository contains a Jupyter Notebook demonstrating methodologies for predictive modelling and price optimisation.
The core of this project is to enhance the performance of predictive models used in pricing strategies. By analyzing market trends and their impact on list prices, the project aims to provide insights into:
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How market trends can be inferred from available data.
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The relationship between market trends and list price adjustments.
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The effectiveness of different modeling techniques in predicting price adjustments.
This project serves as a valuable resource for:
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Data science students and professionals interested in real-world applications of market analysis and predictive modeling.
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Businesses looking to understand how data-driven insights can inform pricing strategies.
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Academics researching in the fields of economics, market trends, or data analysis.
To get started with this project:
git clone https://github.com/BismahGhafoor/Price-Optimisation-Modelling.gitEnsure you have Python installed on your machine.
Install required libraries:
pip install -r requirements.txt
Open Pythoncode.ipynb in Jupyter Notebook or JupyterLab to explore the project.
If you encounter any issues or have questions, feel free to:
- Open an issue in this repository.
- Contact the maintainer at [email protected].
Contributions to this project are not welcome as this is a project showcasing my internship work.
This project is maintained by Bismah Ghafoor and was developed as part of an internship program at Vistex.
- The Vistex Data Science Team
- Mentor: Sara Inigo Sanchez