One classic exercise with position-salaries.csv is building a or decision tree to predict salary based on position, level, and experience.
The primary goal is often predicting what a "Level 6.5" employee (someone between a Regional Manager and a Partner) should earn. Common Key Insights
features, the model can "curve" to fit the points accurately. Support Vector Regression (SVR) position-salaries.csv
This is where position-salaries.csv shines. It is the perfect candidate for Polynomial Regression. By transforming the input variable ($x$) into polynomial terms ($x^2, x^3, x^4$), the model can fit a curve to the data.
Once you’ve mastered the basics, enrich your dataset by merging with external sources: One classic exercise with position-salaries
Now the fun begins. Visualize the distribution of salaries by position:
Fix these with:
eng_salaries = df[df['Position'] == 'Software Engineer']['Salary'] pm_salaries = df[df['Position'] == 'Product Manager']['Salary']
To work with this file, most developers use the and Scikit-Learn libraries. Below is the standard workflow for processing the data. 1. Importing the Data Once you’ve mastered the basics, enrich your dataset
⭐⭐⭐⭐ (4/5) – Excellent for learning, limited for production.