updated: 2022-05-03_12:33:36-04:00

Today's assignment is to look at the data, get comfortable with it.

  1. Load housing data (house_data.csv) from moodle into R.
  2. Explore the data
  3. Make sure to check if there are any NA’s
  4. Remove the columns – lat, long and view which do not contribute to the model.
  5. Take a subset of data if you can’t load everything may be 5000 rows
  6. Use the glimpse function to explore the data
  7. Get a summary of the data
  8. Plot a histogram of price distribution
  9. Plot a histogram of number of bedroom distribution
  10. Get a count of frequency of houses with the number of bedrooms. Basically how many houses with 1 bedroom, 2 bedrooms and so on.
  11. Get a box plot to see how the price and number of bedrooms are associated
  12. Get a box plot to see how the price and number of bathrooms are associated
  13. Plot price against squrefeet
  14. Plot price against number of bedrooms
  15. Create a linear regression model with all the variables
  16. Identify the significant variables.
  17. Explain the coefficients. Which variable has high impact
  18. Plot the correlations using ggcorrplot.