Telecom Revenue Time Series Forecasting

This repository contains the work on time series forecasting of Telecom revenue data. The primary focus is to employ the ARIMA model to predict the future revenue of the company.

Project Structure

Here is the structure of the repository and a brief explanation of the key files:

1. D213 Churn Data Considerations and Dictionary.pdf: This file contains important considerations and the data dictionary for the churn data that is used in this project.

2. Findings and Assumptions Report.html: This HTML file contains the findings and assumptions made during the data cleaning and exploratory data analysis phases.

3. Findings and Assumptions Report.ipynb: This Jupyter notebook contains the Python code used for the data cleaning and exploratory data analysis phases. The detailed assumptions and findings are also documented in this notebook.

4. Time Series Forecasting.pdf: This file contains the final report which presents the results from the time series forecasting.

5. teleco_time_series.csv: This is the original Telecom data used in the project. The data is organized as a time series, with a record for each day representing the revenue for that day.

6. clean_time_series.csv: This is the cleaned and preprocessed version of the teleco_time_series.csv data, which is used in the time series forecasting model.

Usage

To work with the data and the notebooks, you need to have Jupyter Notebook installed. If you do not have Jupyter Notebook, you can install it using the following command:

pip install notebook

You also need to install the required libraries. The required libraries can be installed using the following command:

pip install -r requirements.txt

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the terms of the MIT license.