- Tools & technologies used: Python, Jupyter Notebook, Pandas, Matplotlib, Seaborn
- Performed Data Cleaning and Manipulation on Jobs data of a dummy company.
- Performed Exploratory Data Analysis using Pandas, Matplotlib and Seaborn libraries. Engineered features from the text of each job description to quantify the value companies put on python, excel, aws, and spark.
- Machine Learning models like Optimized Linear, Lasso, and Random Forest Regressors to reach the best model to estimate data science salaries to help data scientists negotiate their income when they get a job.