An attempt of training DNN models to predict ad click-through rate, implemented with Theano.

Display advertising is a billion dollar effort and one of the central uses of machine learning on the Internet. However, its data and methods are usually kept under lock and key. In this research competition, CriteoLabs is sharing a week’s worth of data for you to develop models predicting ad click-through rate (CTR). Given a user and the page he is visiting, what is the probability that he will click on a given ad? The goal of this challenge is to benchmark the most accurate ML algorithms for CTR estimation. All winning models will be released under an open source license. As a participant, you are given a chance to access the traffic logs from Criteo that include various undisclosed features along with the click labels.

In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.

Analyse advertising data set, indicating whether or not a particular internet user clicked on an Advertisement.predict whether or not they will click on an ad based off the features of that user

To predict whether a user will click on ad or not

A Machine Learning project which will help us to predict the number of clicks on a particular ad on the websites.

ML program that can predict whether someone will click on an online ad as it crucial for understanding how well ads are doing, which is important for search engines.

Given an online ad, this application uses SVM, Naive Bayes, and Logistic Regression to predict whether the ad will be clicked by a user or not. The application works on Apache Spark and is designed to work on a large distributed dataset on a cluster.

An end-to-end ML pipeline predicting ad-click behavior from demographics

Predict click-through rates on display ads (http://www.kaggle.com/c/criteo-display-ad-challenge)

Built a click-fraud detection project for PPC advertising using the TalkingData ad-tracking dataset. Cleaned and engineered behavioral features to distinguish real users from bots/click networks, then trained Decision Tree and Random Forest models to predict fraudulent clicks and highlight key drivers behind fraud patterns.

To boost the income through ad clicks, it is imperative to understand the significance of the factors affecting ad clicks. After mining through data logs provided by Outbrain,we formulated new learning problem in content ranking based on past clickthrough data to predict which pieces of content (ads) likely to be clicked by global users automatically.We used classification models like Naive Bayes, SVM, Random Forest and Stochastic Gradient Descent algorithms for learning parameterized orderings.

Predict whether ad would get clicked.

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This model uses logistic regression to predict whether or not an internet user will click on an ad.

Predicting whether or not a internet accessing user will click on an ad, based on his/her features.

AdFlux Engine is a Foundation model for Advertisement Simulation, predicting user behavior (clicks/views) from historical data. Utilizing advanced models like LAVA, Decision Transformers, Gato, and MuZero, it optimizes ad placements and boosts user engagement. Built for scalability, it integrates NLP, RL, and RLHF techniques.

AdClick Predictor: Optimizing Advertising Efficiency with Predictive Modeling

Predict whether a mobile ad will be clicked In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance. As a result, click prediction systems are essential and widely used for sponsored search and real-time bidding.

A/B testing on advertising effectiveness of Ad campaigns (Facebook and AdWords Ad Campaigns). Regression Analysis for predicting conversions based on ad clicks.

predicting whether an AD is going to be clicked or not using embeddings and SVM classifiers

In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance. This repo uses 11 days data to predict next days' click trough rate for each ad.

An Analysis and prediction of an advertising dataset to predict if readers will click on the AD or not

"Uses logistic regression to predict ad clicks based on demographic and behavioral data. Includes EDA, model training, and evaluation using classification metrics."

Technology Used: Python. A Logistic Regression model is developed to predict if a user clicks an internet add on a website based on the features of the user. Model is developed after careful analysis of website traffic data logs based on time spent on site, age, daily internet usage, clicked on ad and so on. Results are plotted and evaluated using a classification report & confusion matrix. From the predicted results, 91% accuracy is achieved over model-selection.

The Dataset contains information about users on a Social Networking site and using that information as features for our ML model, the model predicts whether a particular user after clicking on an ad on the Social networking site goes on to buy a particular product or not. It is a CLASSIFICATION PROBLEM as the output says whether the user buys the product or not, so it’s either a yes or a no. Well this particular Social Network has a Business client which lets assume is a car company which advertises itself by putting adds on the social networking site. Now the work of the social network here is to gather information as to whether the user bought the product or not.

Predicting users ad click behaviour

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