Predicting Customer Ad Clicks via Machine Learning



Internet marketing has taken over traditional marketing strategies in the recent past. Companies prefer to advertise their products on websites and social media platforms. However, targeting the right audience is still a challenge in online marketing. Spending millions to display the advertisement to the audience that is not likely to buy your products can be costly.

In this article, we will work with the advertising data of a marketing agency to develop a machine learning algorithm that predicts if a particular user will click on an advertisement. The data consists of 10 variables: ‘Daily Time Spent on Site’, ‘Age’, ‘Area Income’, ‘Daily Internet Usage’, ‘Ad Topic Line’, ‘City’, ‘Male’, ‘Country’, Timestamp’ and ‘Clicked on Ad’.

The main variable we are interested in is ‘Clicked on Ad’. This variable can have two possible outcomes: 0 and 1 where 0 refers to the case where a user didn’t click the advertisement, while 1 refers to the scenario where a user clicks the advertisement.

We will see if we can use the other 9 variables to accurately predict the value ‘Clicked on Ad’ variable. We will also perform some exploratory data analysis to see how ‘Daily Time Spent on Site’

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