Vangelis Gkiastas - Data Science and Machine Learning Portfolio

Welcome to my Data Science and Machine Learning Notebook Repository! This repository contains a collection of uses cases that showcase my exploration and problem-solving skills in the field of data science.

Projects

Here's a brief overview of the folders in this repository:

• User Monetization: Process of building classification models that can predict whether a user will buy a subscription.
• Optimal Price Prediction: Find the optimal price to sell an item based on various factors.
• User Profile Ranking: The objective of this case study is to optimize a search engine and email outreach system by building and evaluating various algorithms and machine learning models.
• Customer Churn: Utilize data to understand customer churn and identify key factors that lead to customer attrition.

Installation

To run the notebooks in this repository, you'll need to have Python 3 installed on your machine. Clone this repository and install the required Python packages using pip:

git clone https://github.com/egkiastas/portfolio.git
cd portfolio
pip install -r requirements.txt

Usage

To start Jupyter Notebook, run the following command:

jupyter notebook

Then navigate to the notebook you want to run, click on it, and the notebook will open in your web browser.

Note that due to privacy reasons, I cannot provide the data used in these notebooks. However, I have included detailed descriptions of the datasets used and where you can obtain them if you'd like to follow along and reproduce the analyses.

Contact

If you have any questions or comments about the notebooks in this repository, feel free to reach out to me via LinkedIn.

Thank you for checking out my repository!