vineethsakhamuru29/ad-click-predictor-ml

AdClickPredictorML: Machine learning for ad engagement prediction using logistic regression and random forest classifiers on advertising.csv dataset.

Ad-Click-Predictor-ML

Overview

AdClickPredictorML is a machine learning project developed for predicting user engagement with advertisements. This project aims to optimize ad campaign targeting by leveraging a predictive model trained on a comprehensive advertising dataset. The repository includes code for data preprocessing, exploratory data analysis (EDA), and model training using logistic regression and a random forest classifier.

Project Structure

data: Contains the advertising dataset (advertising.csv) used for training and testing the machine learning model.

notebooks:

media_EDA.ipynb: Jupyter Notebook providing in-depth Exploratory Data Analysis (EDA) of the advertising dataset using Python.

Logistic_Regression_Random_Forest.ipynb: Notebook containing the code for data preprocessing, logistic regression, and random forest classifier model training and evaluation.

Requirements

Python 3.x Jupyter Notebook

Acknowledgments

This project is inspired by the need for accurate ad click predictions to enhance advertising campaign effectiveness. Feel free to contribute, report issues, or suggest improvements! Happy coding!