1. A linear approach to predicting house prices
Jeno Yamma

2. Key Takeaway Why exploring the data is very important
Provide an understanding of the initial approach to a dataset
What distributions could tell us about the data
Using a linear regression as oppose to a more complicated model
Understanding the model evaluation method, root mean square log error
Reducing Bias and Variance
Evaluating the model’s prediction

3. Why data exploration is important
Business: Come up with an optimal business solution to the problem
Data exploration allows us to:
check if the data given is appropriate (integrity, missing values, outliers etc.)
Brainstorm ideas
See the behavior of the data
Communicate the data to stakeholders to help explain your prediction
Different tools you can use: R, Python, Tableau, Excel!

4. Initial approach

5. Sit and wait for it to load…

6. Did your tool read in the data correctly?
Do a simple data type check after reading in the data
Important to correct this first: leads to a better model performance and exploration
Should we change
anything?

7. Statistical summaries are important
Gives us an idea of the mean and spread of each variables
What is this
and this ?

8. Missing Values
Keep or remove?

9. Checking variable relationships – scatterplot
Do you know how to read this?

10. Easier to read
Can you find the most significant relationship?

11. Checking the spread of the data using boxplots

12. or Distributions
A house with 8 bathrooms…

13. Always be curious about your data
8 bathrooms, only 4 rooms, built in 1928…damn
8 rooms, a small number of bathrooms, and the land size is close to the average land size…

14. Exploring the categorical section

15. Sales Date

16. Property Type

17. Seller

18. Exploring the categorical section

19. Do some more explorations and try to understand your data
Do some more explorations and try to understand your data. For simplicity use Tableau or Excel 

20. Let’s do some Machine Learning… (just a fancy way of saying let’s do some linear regression)