Movie Wars – Building a ML movie recommender

A workshop where we will build a movie recommendation engine using Machine Learning, covering all steps of the process from data analysis to making predictions.

Contents of the workshop:

• Episode I –The Phantom Problem
• Episode II –Attack of the Data
• Episode III –Revenge of the Outliers
• Episode IV – A New Pipeline
• Episode V – The Training Strikes Back
• Episode VI – Return of the Metrics
• Episode VII – The Model Awakens
• Episode VIII – The Last Hyper-parameter
• Episode IX – The Rise of Predictions
• Bonus – The Dark Side of The Data

About the repository

This repository contains all the material for the Movie Wars - Building a ML movie recommender of the South Tech Week 2020.

🚀 Getting started

To get a local copy up and running follow these simple example steps.

📋 Prerequisites

1. Install Python 3.7+: https://www.python.org/downloads/windows/

2. Install Pip:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py 
python get-pip.py

3. Install Jupyter notebook:

pip install notebook

4. Install required libraries:

pip install statistics stats pandas seaborn statsmodels numpy scikit-learn matplotlib scipy

5. Install Visual Studio Code: https://code.visualstudio.com/

6. Install .NET core 3.1+: https://dotnet.microsoft.com/download/dotnet-core/3.1

7. Restore NuGets:

• ML.NET
• ML.NET Recommender

🔧 Installation

Just clone this repository:

git clone https://github.com/Ellyster/STW2020-MovieWars.git

⚙️ Usage

1. Open the PowerPoint presentation and follow it along.

2. To open the first part (Episode II & Episode III) and second parts (Episode V & Episode VI) notebooks, use:

cd Jupyter
jupyter notebook

3. To open the third part ML.NET project , use:

cd ML.NET
code .

4. If needed, the solutions to the ML.NET exercises are available here.

🛠️ Contribute

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

✒️ Contact

• Alonso Gragera - AI Product Manager & ML Engineer @Unit4
• Manuel Ruiz - Data Scientist @Unit4