Project Name

The project is focused on predicting customer churn for a telecom company using random forest regression.

Project Structure

The project has three main folders:

• data: This folder contains the raw data and the clean data used for training and testing the model.

• notebooks: This folder contains the Jupyter notebook used to develop and test the model.

• results: This folder contains the final report in PDF format and the clean data files used for training and testing the model.

The following files are included in the project:

• churn_clean.csv: This is the clean data file containing the data used for training and testing the model.

• rf_clean.csv, rf_train.csv, and rf_test.csv: These are the clean data files used for training and testing the random forest regression model.

• telecom_churn_prediction.ipynb: This is the Jupyter notebook used to develop and test the model.

• Telecom Churn Prediction Report.pdf: This is the final report detailing the analysis and results of the project.

Running the Notebook

To run the notebook, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.
3. Open the telecom_churn_prediction.ipynb notebook in Jupyter.
4. Follow the instructions in the notebook to train and test the model.