CTR-Prediction

CTR prediction using Random Forest Classifier This repository contains code for predicting click-through rates using a Random Forest Classifier. The dataset used for training and testing is provided in the train.csv and test.csv files, respectively.

Dataset

The dataset consists of various features related to online advertising, and the goal is to predict whether a user will click on an ad or not. The features include information such as the hour of the day, device type, site information, and more.

Model Training

The Random Forest Classifier is employed for training the click-through rate prediction model. GridSearchCV is used to find the best hyperparameters for the model.

Evaluation

The model's performance is evaluated using accuracy and precision scores on a test set.
My accuracy is : 0.8394897124329566
My precision is : 0.5422661870503597

Creating a Submission File

The trained model is used to make predictions on the test set, and the results are saved in a CSV file named submission.csv.

Usage

1.Ensure you have the required Python libraries installed by running:
pip install pandas scikit-learn
2.Run the Jupyter notebook or Python script to train the model and generate predictions.
3.View the evaluation metrics and the submission file.

Files Included

• train.csv: Training dataset.
• test.csv: Testing dataset.
• submission.csv: Final submission file.
  
  Feel free to explore and modify the code to suit your needs. If you have any questions or suggestions, please open an issue.