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Ads Click Through Rate is the ratio of how many users clicked on your ad to how many users viewed your ad,
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For example, 5 out of 100 users click on the ad while watching a youtube video. So, in this case, the CTR of the youtube ad will be 5%.
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Analyzing the click-through rate help companies in finding the best ad for their target audience.
Acuried the data from kaggle-an open source platform(.csv format)
| Attribute | Data Type | Description |
|---|---|---|
| Daily Time Spent on Site | float64 |
Time (in minutes) a user spends on the site daily. |
| Age | int64 |
Age of the user. |
| Area Income | float64 |
Average income of the user’s area. |
| Daily Internet Usage | float64 |
Time (in minutes) a user spends on the internet daily. |
| Ad Topic Line | object |
The title or topic of the ad. |
| City | object |
City where the user is located. |
| Gender | object |
Gender of the user (Male, Female). |
| Country | object |
Country of the user. |
| Timestamp | object |
Date and time when the user interacted with the ad. |
| Clicked on Ad | int64 |
Binary (0 or 1) indicating if the user clicked on the ad. |
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What is the relationship between Daily Time Spent on Site and the likelihood of clicking on an ad?
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How does Age affect the probability of clicking on an ad?
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Is there a correlation between Daily Internet Usage and Ad Clicks?
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Do users with higher Area Income spend more time on the site?
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Which age group has the highest engagement with ads?
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Does Gender play a role in ad engagement?
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Are there certain Countries where users are more likely to click on ads?
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Does the City influence Daily Time Spent on Site or Ad Clicks?
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Is there a specific time of the day (based on Timestamp) when users click on ads the most?
We used several ml models to gain the high accuracy
| Model Name | Accuracy (%) |
|---|---|
| Logistic Regression | 69.5 |
| Decision Tree | 77.05 |
| Random Forest | 80.55 |
Desgined a api using fast api, to serve the preiction and deployed it over a aws lamda.