nmahammad / salary_prediction

Data scientist salary prediction according to the data from Glassdoor in the USA

Data science salary prediction in the USA

This project represents my personal learning journey, inspired by the insightful YouTube videos of the renowned data scientist, Ken Jee. Its main goal is to provide a comprehensive learning experience in the field of data science by covering various phases of a typical data science project.

Project Overview

The project is divided into several key phases, each essential in the data science workflow:

1. 📁 Data Collection: Data was obtained via web scraping from glassdoor

2. 🔧 Data Cleaning: Once I had my dataset, I dove into the essential task of data cleaning. Data often comes with imperfections and inconsistencies, and this phase demonstrates how to preprocess the data to make it suitable for analysis.

3. 📈 Exploratory Data Analysis (EDA): EDA is a critical phase where I explored and visualized the data to gain insights. I learned how to identify trends, outliers, and patterns in the dataset, which is crucial for informed decision-making.

4. 💻 Model Building: Building predictive models is at the heart of data science. I explored different techniques for model selection and evaluation, emphasizing practical implementation.

5. 🚀 Productionization: In the final phase, I delved into the deployment of the data science project. While this is not always covered in beginner-level projects, it's an essential skill for real-world applications.

Project Details

Please Note: This project is primarily designed for my own learning purposes, and it provides an opportunity to gain hands-on experience in data science concepts and techniques.

Getting Started

To get started with this project and access the accompanying YouTube videos by Ken Jee, simply follow the provided links below:

• Link to YouTube Playlist

Feel free to explore the project at your own pace and refer to the videos for detailed guidance at each phase.

Contributions

If you find any issues or have suggestions for improvements, please feel free to contribute to this project. You can submit pull requests or open issues in the repository.

Acknowledgments

I would like to express my gratitude to Ken Jee for providing invaluable insights and guidance through his YouTube videos, making this project possible.

Happy learning and exploring the fascinating world of data science!