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Found springboard blog  which is interesting and also the article “19 Free Public Data Sets for Your Data Science Project” . Also found this site that has lots of data for Airbnb and I can do data analysis from there. It will be really interesting and helpful because it is real data from Airbnb ! I downloaded the data from Athens and there are so many things to see and do!

Also I watched some videos on Data analysis Course by Jeff Leek up to video 17 and I learned to download data in R directly from websites, which is a very useful thing to know. Finally I continued the tutorial “Head Start for Data Scientist” on Kaggle, up to exploratory data analysis. I think this is the best way to learn data science because you are actually doing it and learn from other users and Kernels.

Finally did exercise 10 -Unit 3, from the book “An introduction to statistical learning”. Here is the code I used

#Excercise 10, Page 123










#In the regression the Price has a negative coefficient which indicates that the

#price has an effect on sales(It’s difficult to sell more expensive houses)

#Also R has created a UrbanYes variable which takes the values of 1 if the house is

#in a urban area and 0 otherwise. We can see that it has a negative effect on

#sales ,but it is not significant in the regression model we created.

#Finally R has created a USYes variable which takes the values of 1 if the house is

#in US and 0 otherwise.This shows that sales are affected

#by the location of the house, if the house is in US this makes it easier

#to be sold.


#equation form y= 13.043-0.054459*x1-0.021916*x2+ 1.200573*x3+ε


#we can reject the null hypothesis for the intercept,Price,and US





#The first model had a 41.52 R-squared and the second 62.43. The second

#model fits the data better.







#We can see that there is a high leverage observation that affects the model

#which is observation no.43


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