100 Days of Machine Learning: Day 19

Time Series Forecasting of Poverty Rate

This project demonstrates the process of time series data analysis, decomposition, and forecasting using Python libraries. We use a dataset of poverty rates from the OECD to forecast future values of poverty rates across various countries.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

To run this project, you need to have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• statsmodels
• pmdarima

You can install them using pip:

pip install pandas numpy matplotlib statsmodels pmdarima

Dataset

The dataset used in this project is from the OECD and can be downloaded here. Save the downloaded CSV file to your local machine.

Project Structure

This project consists of a Jupyter Notebook that walks you through the following steps:

1. Import necessary libraries
2. Load the dataset
3. Preprocess the data
4. Visualize the data
5. Decompose the time series
6. Forecasting methods
7. Train and fit the ARIMA model
8. Forecast future values
9. Visualize the forecast
10. Unit test

Running the Project

Open the Jupyter Notebook in your preferred environment (e.g., JupyterLab, VSCode, or Jupyter Notebook) and run the cells one by one to see the results. Make sure to update the file path to the downloaded dataset in the notebook.

Contributing

Please feel free to submit pull requests to improve the project or open issues if you encounter any problems.

License

This project is licensed under the MIT License - see the LICENSE file for details.