seaborn-and-linear-regression

Learning Points & Summary

• Use nested loops to remove unwanted characters from multiple columns
• Filter Pandas DataFrames based on multiple conditions using both .loc[] and .query()
• Create bubble charts using the Seaborn Library
• Style Seaborn charts using the pre-built styles and by modifying Matplotlib parameters
• Use floor division (i.e., integer division) to convert years to decades
• Use Seaborn to superimpose a linear regressions over our data
• Make a judgement if our regression is good or bad based on how well the model fits our data and the r-squared metric
• Run regressions with scikit-learn and calculate the coefficients.