Here's some data!

1. Import the data. It's stored in a file called 'movie_data_detailed.xlsx'.

df = None #Your code here

2. Fill all the null values with zero.

#Your code here

3. Normalize the data so that all features have a minimum of zero and a maximum of one.

#Your code here

4. Define 4 variables: X_train, Y_train, X_test, Y_test using a 80-20 split for train and test data. X should be a matrix of data features predicting y, Domestic Gross Sales. Use random_state=42 for consistency.

from sklearn.model_selection import train_test_split

X = None
y = None

#Your code here

5. Import import sklearn.linear_model.LinearRegression

Create an instance of the LinearRegression class. Then use the fit method to train a model according to the data.

import sklearn.linear_model as linreg

#Create Instance of LinearRegression (Ordinary Least Squares Regressor)

#Fit the model to the train set

6. Scatter Plot

Create a Scatter Plot of the budget and Domestic Gross (domgross) along with your model's predictions.

%matplotlib inline
import matplotlib.pyplot as plt

#Scatter Plot

7. Calculate the RSS for both the train and test sets.

Define a function called rss(y,y_hat). Call it on the train and test sets.

def rss(y, y_hat):
    pass

# print('RSS Training: {}'.format())
# print('RSS Test: {}'.format())