<jupyter_start><jupyter_text>Assignment 01: Evaluate the Ad Budget Dataset of XYZ Firm*The comments/sections provided are your cues to perform the assignment. You don't need to limit yourself to the number of rows/cells provided. You can add additional rows in each section to add more lines of code.**If at any point in time you need help on solving this assignment, view our demo video to understand the different steps of the code.***Happy coding!*** * *  1: Import the dataset<jupyter_code>#Import the required libraries

#Import the advertising dataset<jupyter_output><empty_output><jupyter_text>2: Analyze the dataset<jupyter_code>#View the initial few records of the dataset

#Check the total number of elements in the dataset<jupyter_output><empty_output><jupyter_text>3: Find the features or media channels used by the firm<jupyter_code>#Check the number of observations (rows) and attributes (columns) in the dataset

#View the names of each of the attributes<jupyter_output><empty_output><jupyter_text>4: Create objects to train and test the model; find the sales figures for each channel<jupyter_code>#Create a feature object from the columns

#View the feature object

#Create a target object (Hint: use the sales column as it is the response of the dataset)

#View the target object

#Verify if all the observations have been captured in the feature object

#Verify if all the observations have been captured in the target object<jupyter_output><empty_output><jupyter_text>5: Split the original dataset into training and testing datasets for the model<jupyter_code>#Split the dataset (by default, 75% is the training data and 25% is the testing data)

#Verify if the training and testing datasets are split correctly (Hint: use the shape() method)<jupyter_output><empty_output><jupyter_text>6: Create a model  to predict the sales outcome<jupyter_code>#Create a linear regression model

#Print the intercept and coefficients 

#Predict the outcome for the testing dataset<jupyter_output><empty_output><jupyter_text>7: Calculate the Mean Square Error (MSE)<jupyter_code>#Import required libraries for calculating MSE (mean square error)

#Calculate the MSE<jupyter_output><empty_output>
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