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Project: ICER User Data Analytics

This document provides instructions for setting up the environment, installing necessary packages, and running demo code for the ICER User Data Analytics project. Follow these steps to ensure a smooth setup.

Prerequisites

  • Ensure you have git installed on your system to clone the repository.

  • Download and install Miniconda or Anaconda. for your operating system from the Anaconda website. This will be used to manage the project's environment and dependencies.

Cloning the Repository

  1. Open a terminal or command prompt.

  2. Clone the project repository using the following command:

git clone https://github.com/tairaeli/ICER_user_data_analytics.git
  1. Navigate into the project directory:
cd ICER_user_data_analytics

Dependencies

Once in the package directory, there are several methods to ensure that all of the depedencies are installed properly on your device:

  1. Using Conda

If you are a conda user, you can use the following code in the project's main directory

conda env create --file ./environment.yml
conda activate user_dat_analysis
  1. Manual Installation

This project uses mostly relies on many popular, well-maintained packages, so a manual installation is quite simple. All you need to do is to make sure all of the following packages are installed:

  • numpy
  • scikit-learn
  • matplotlib
  • pandas
  • seaborn

Data Instructions

Since the project does not include large datasets in the repository, please follow the instructions provided by the project team or instructors to obtain the necessary data. Place this data in the data directory located at the root of the project repository. Use relative paths in your code to access this data to ensure portability.

Running the Demo

  1. Start JupyterLab or Jupyter Notebook:

This can be done by any way you are comfortable with. So long as you are able to open a working Jupyter notebook th

jupyter lab

Or:

jupyter notebook
  1. Once the jupyter notebook session is created, open the demo notebook file (demo.ipynb) located in the main project directory. This notebook contains example code demonstrating the use of the software, including data preprocessing, analysis, and visualization techniques.

Troubleshooting

  • If you encounter any issues related to UTF8 errors on Windows, run the following command in your git repository directory to ensure the correct encoding:
conda env export --from-history | Set-Content -Encoding utf8 environment.yml

Support

For any issues or questions, please refer to the contacts of the project maintainers.