This project is about detecting whether a website visitor clicked on the Ad or not. To find this, i used a Logistic Regression. The following is the result of the project:
0 --> The class representing that group of ads that were not clicked. 1 --> The class representing that group of ads that were clicked.
precision recall f1-score support
0 0.91 0.95 0.93 157
1 0.94 0.90 0.92 143
accuracy 0.93 300
macro avg 0.93 0.93 0.93 300
weighted avg 0.93 0.93 0.93 300
This repository contains a dataset and code for an ad click predictor. The goal of this project is to develop a machine learning model that can predict whether a user will click on an ad based on various features.
The dataset used for this project is not provided in this repository due to its size. However, you can find publicly available datasets related to ad click prediction from various sources, such as online advertising platforms or research repositories. The dataset typically includes features like user demographics, ad attributes, contextual information, and the target variable indicating whether the ad was clicked or not.
Please note that it's important to comply with any licensing or usage restrictions associated with the dataset you choose to use.
To use this ad click predictor, you can follow the steps below:
- Obtain a suitable ad click prediction dataset from a reliable source.
- Preprocess and prepare the dataset for training and evaluation.
- Implement machine learning algorithms or models to train on the dataset.
- Evaluate the model's performance using appropriate metrics and techniques.
- Use the trained model to make predictions on new data, such as unseen ads.
The code provided in this repository can serve as a starting point for developing your ad click predictor. You may need to adapt the code to fit your specific dataset and requirements.