Ho ho ho! ‘Tis the season to be jolly!
The holiday season is just around the corner—Christmas trees have been decorated, lights and wreaths hung, streets all decked up, Santa costumes rented out, and holiday cards in the mailbox. In light of this holiday cheer, retail brands, big and small, want to earn considerable profits, and therefore, are investing significantly in advertising. These brands have approached an advertising agency to plan and execute ad campaigns that will help them increase the footfall in their stores.
You have been hired by this advertising agency to assess the scope of revenue that can be generated by a proposed ad. Based on the demographic information provided, you need to predict whether the revenue generated will cover costs to produce and air the ad. This will help guide decision making for the firm, as they will want to pursue ads that are likely to generate a net gain for their clients— thereby bolstering the advertising firm’s reputation.
Task:
This is a binary classification problem where you need to predict whether an ad buy will lead to a netgain.
Data Description:
Train.csv : 26049 x 12 [including headers] : training data set
Test.csv : 6514 x 11 [including headers] : test data set
sample_submission.csv : example for submission format of Results.csv
|
Data |
Data Description |
|
id |
Unique id for each row |
|
ratings |
Metric out of 1 which represents how much of the targeted demographic watched the advertisement |
|
airlocation |
Country of origin |
|
airtime |
Time when the advertisement was aired |
|
average_runtime(minutes_per_week) |
Minutes per week the advertisement was aired |
|
targeted_sex |
Sex that was mainly targeted for the advertisement |
|
genre |
The type of advertisement |
|
industry |
The industry to which the product belonged |
|
economic_status |
The economic health during which the show aired |
|
relationship_status |
The relationship status of the most responsive customers to the advertisement |
|
expensive |
A general measure of how expensive the product or service is that the ad is discussing. |
|
money_back_guarantee |
Whether or not the product offers a refund in the case of customer dissatisfaction. |
|
netgain [target] |
Whether the ad will incur a gain or loss when sold |
Submission Format:
You need to write your predictions into a .csv file and upload it to 'Upload
Evaluation Criteria:
score=100∗(accuracy_score(actual_values,predicted_values))
