djsegal / zelus_assessment

Leveraging Data Science for Strategic Cricket Insights

Zelus Sports Analytics Tech Assessment

This repository contains the code and data for the ML engineer technical assessment from Zelus Sports Analytics.

Structure

The repository is structured as follows:

• Dan_Segal_ML_Engineer_Question_N.ipynb: Jupyter notebook containing the solutions to Questions 1-4.
• data/: Downloaded original input and various intermediate data
• neural_net/ and data/: Directory containing the pre-trained machine learning models (Neural Network and Stacked Ensemble).
• prediction_script.py: Python script used to predict the expected runs per over using the pre-trained models.
• run_model.sh: Bash script used to filter the intermediate data for a specific team and number of overs, and to run prediction_script.py.
• requirements.txt: File containing the Python dependencies required for prediction_script.py.
• Dockerfile: Dockerfile used to build the Docker image for Question 4.

Additional files include:

• Zelus_Written_Interview.pdf: 8 answered questions from the first round interview spanning from work experience to research interests
• Dan_Segal_Julia_Packages_June_2023.pdf: Slides for presentation on improving JuliaPackages.com categorization using the ChatGPT API

Prerequisites

You need to have Docker installed on your machine to build and run the Docker image for Question 4. You can download Docker from here.

To run the Jupyter notebook, you need to have Jupyter installed, and the Python dependencies specified in requirements.txt.

Instructions

Running the Jupyter notebook

To run the Jupyter notebook, navigate to the directory containing Dan_Segal_ML_Engineer_Question_N.ipynb, open a terminal, and type:

jupyter notebook

Then, open Dan_Segal_ML_Engineer_Question_N.ipynb in the Jupyter notebook dashboard.

Building the Docker image

To build the Docker image, navigate to the directory containing Dockerfile, open a terminal, and type:

docker build -t zelus-tech-assessment .

This builds a Docker image and tags it as zelus-tech-assessment.

Running the Docker container

To run the Docker container, open a terminal and type:

docker run -it zelus-tech-assessment <team> <overs>

Replace <team> and <overs> with the name of the team and the number of overs, respectively.

This runs the Docker container, which executes run_model.sh with the specified team and number of overs.

Note

If you encounter any issues or have any questions about the code or data, please open an issue or submit a pull request.