In this project, 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 in combination with Ad Topic Line affects the user's decision to click on the add.
| Machine Learning Models Applied | Accuracy |
|---|---|
| Decision Tree Classifier | 93% |
| Logistic Regression | 90% |
| Logistic Regression with Hyperparameter Tuning | 96% |
Predict the probability of user clicking the ad which is shown to them on the partner websites for the next 7 days based on historical view log data, ad impression data and user data. Since every individual may have a different view of your brand, stories or slogans can resonate with everyone differently. Through target marketing, you can better understand each customer's needs and create a marketing campaign that targets a specific audience, so you can meet their expectations.
This project follows the MIT LICENSE.
