Key Word(s): kNN regression, k-Nearest Neighbors, Linear Regression, MSE, R-squared

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Title

Exercise: A.1 - Plotting the data

Description

The aim of this exercise is to plot TV Ads vs Sales based on the Advertisement dataset which should look similar to the graph given below.

Instructions:

  • Read the Advertisement data and view the top rows of the dataframe to get an understanding of the data and columns

  • Select the first 7 observations and the columns TV and sales.

  • Create a scatter plot TV budget vs sales like in the lecture.

Hints:

pd.read_csv(filename) : Returns a pandas dataframe containing the data and labels from the file data.

df.iloc[] : Returns a subset of the dataframe that is contained in the row range passed as the argument.

np.linspace() : Returns evenly spaced numbers over a specified interval.

df.head() : Returns the first 5 rows of the dataframe with the column names

plt.scatter() : A scatter plot of y vs. x with varying marker size and/or colour

plt.xlabel() : This is used to specify the text to be displayed as the label for the x-axis

Note: This exercise is auto-graded and you can try multiple attempts.

In [ ]:
# Data set used in this exercise
data_filename = 'Advertising.csv'

# Read advertising.csv file using the pandas library
df = pd.read_csv(___)
In [ ]:
# Print your new dataframe to see if you have selected 7 rows correctly

print(___)

Plotting the graph

In [ ]:
# Use a scatter plot for TV vs Sales
plt.___

# Add axis labels for clarity (x : TV budget, y : Sales)
plt.___
plt.___

Post-exercise question

Instead of just plotting seven points, experiment to plot all points

In [ ]: