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Project Description
This project aims to predict salaries for data science professions based on skill sets and geographic location. It leverages data collected from Kaggle, explores correlations between skills and salaries, identifies top job markets, and assesses the impact of education on earnings. Through machine learning techniques, it offers valuable insights into the earning potential of data professionals.
Project Information
- Title: Data Science Professions Salary Prediction
- Project Duration: June 2022
- Tools and Technologies: Python, Jupyter Notebook, Machine Learning Libraries (e.g., Scikit-Learn), Data Visualization Tools (e.g., Matplotlib, Seaborn)
- Data Source: Kaggle (Glassdoor dataset)
Methodology:
- Data Collection: Acquire data from Kaggle.
- Exploratory Data Analysis: Perform visual and statistical analysis of the dataset.
- Data Preparation: Includes feature selection and treatment of outliers, as well as resampling.
- Data Modeling: Implement machine learning algorithms for salary prediction.
- Correlation Analysis: Investigate relationships between variables.
- State and Skill-Based Salary Analysis: Explore the impact of skills and location on salaries.
- Top States for Data Science Jobs: Identify the top states with the highest job opportunities.
- Degree and Salary Relationship Plot: Analyze the correlation between educational qualifications and salaries.
- Baseline Performance (OLS): Establish a baseline performance using Ordinary Least Squares.
Conclusion and Recommendation:
- The project provides insights into the correlation between skills, location, and salaries in the data science domain.
- A baseline performance metric (OLS) is established for reference.
- Recommendations may include focusing on acquiring skill sets that yield higher salaries and considering further educational qualifications for career advancement.
- The project provides insights into the correlation between skills, location, and salaries in the data science domain.
- A baseline performance metric (OLS) is established for reference.
- Recommendations may include focusing on acquiring skill sets that yield higher salaries and considering further educational qualifications for career advancement.