Context

A company wants to know the CTR ( Click Through Rate ) in order to identify whether spending their money on digital advertising is worth or not. A higher CTR represents more interest in that specific campaign, whereas a lower CTR can show that the ad may not be as relevant. High CTRs are important because they show that more people are clicking through the website. Along with this high CTRs also help to get better ad position for less money on online platforms like Google, Bing etc.

Content

The dataset divided to train (463291, 15) and test (128858, 14). Features are clear and target is "is_click" , 0 (No) , 1(Yes).

Kerner(s)

• Quick EDA and Base Model

Inspiration

• EDA and customer analytics
• Prediction of the ad click rate of a customer

Dataset

This dataset was created by Sulabh Shrestha

Contents

Advertisement CTR (click-through-rate) prediction is the key problem in the area of computational advertising. Increasing the accuracy of advertisement CTR prediction is critical to improve the effectiveness of precision marketing. Based on the following datasets, a Kaggle competition was run for optimal advertisement CTR prediction models. The datasets contain the advertising behavior data collected from seven consecutive days, including a training dataset and a testing dataset.

Most organizations today rely on email campaigns for effective communication with users. Email communication is one of the popular ways to pitch products to users and build trustworthy relationships with them. Email campaigns contain different types of CTA (Call To Action). The ultimate goal of email campaigns is to maximize the Click Through Rate (CTR). CTR = No. of users who clicked on at least one of the CTA / No. of emails delivered. This Dataset contains details of body length, sub length, mean paragraph , day of week, is weekend, etc.

Click-Through Rate is calculated as the number of clicks an ad receives divided by the number of times the ad is shown (impressions), expressed as a percentage. The CTR prediction task involves modeling the likelihood of a click based on ad characteristics, user profile data, and contextual features.

Predicting the click-through Rate (CTR) is crucial for optimizing online advertising campaigns. By accurately estimating the likelihood of a user clicking on an ad, businesses can make informed decisions about ad placement and design, ultimately maximizing their return on investment (ROI).

Dataset

This dataset was created by Sharan Harsoor

Released under Apache 2.0

Contents

Criteo Display Advertising Challenge dataset, which is provided by the Criteo company on the famous machine learning website Kaggle for advertising CTR .

Dataset

This dataset was created by Atirpetkar

Contents

Dataset

This dataset was created by sambanjie

Contents

📊 Criteo 1TB Click Logs Dataset

This dataset contains feature values and click feedback for millions of display ads. Its primary purpose is to benchmark algorithms for clickthrough rate (CTR) prediction. It is similar, but larger than the dataset released for the Display Advertising Challenge hosted by Kaggle:🔗 Kaggle Criteo Display Advertising Challenge

  📁 Full Description

This dataset contains 24 files, each corresponding to one day of data.

  🏗️… See the full description on the dataset page: https://huggingface.co/datasets/criteo/CriteoClickLogs.

Dataset

This dataset was created by Anand Panda

Released under Attribution 4.0 International (CC BY 4.0)

Contents

Dataset

This dataset was created by Madhu41289

Contents

Dataset

This dataset was created by Gaurav Dutta

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Gaurav Dutta

Contents

Dataset

This dataset was created by Gaurav Dutta

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Dataset

This dataset was created by Darrell Cornelius Rivaldo

Contents

This contains random 15M records from avazu Dataset & then divided into a file containing 100k records

Dataset

This dataset was created by Santanu Kundu

Contents

Dataset

This dataset was created by wuyingwen06

Contents

Dataset

This dataset was created by agus abdul rahman

Released under CC0: Public Domain

Contents