"""
1. Read in the red wine dataset (saved in the data folder) and make sure that the
data was imported correctly
"""
# your code



"""
2. Print the average and the mode of each column
"""
# your code




"""
3. Explore the data a bit by creating three different *types* of plots using the
seaborn package.
"""
# your code



"""
4. Save the plot which you think to be the most interesting to your local disk
"""
# your code



"""
5. Select subset of four features for further analysis and create a subset
dataframe
"""
# your code



"""
6. Check how many missing values there are in each column and in a second step drop
all the rows with missing values
"""
# your code



"""
7. Often in data science we can find interesting relations by studying the interaction
between two variables. Create two new columns in your subset dataframe by
multiplying two other columns where you think that their combined presence
might be more predictive of the wines quality than looking at the individual
numbers alone
"""
# your code



"""
8. Divide your column into a training and testing set. The fraction of the training
size should be somewhere between 70 and 80 percent
"""
# your code



"""
9. Create a multiple linear regression model using the statsmodels package. Train it
on the training data and print the regression summary.
"""
# your code



"""
10. Create the predictions for the test data set and add them as a column to the
testing data data frame. Calculate the means squared error of your predictions
and the quality labels.

mse = average of ((predictions - labels)^2)
"""
# your code



"""
11. Create a plot of your predictions and the actual labels in different colour on
the y-axis and one of the features on the x-axis
"""
# your code



"""
12. Save your regression model to your local folder. Test whether it worked
by re-loading the model as a new instance and make some predictions (e.g. again
on the test dataset)

Hint: have a look at this link
https://stackoverflow.com/questions/16420407/python-statsmodels-ols-how-to-save-learned-model-to-file
"""
# your code
