youtube-next-activity-predictor is a collection of tools for downloading, training, and predicting youtube user activities.

The goal of this project is to predict a youtube user's next activity given the user's current history of activities.

Project Dependencies

Before running any code, you'll need to install the following dependencies:

• nodejs (and npm)
• python3
• scipy and scikit-learn (python libraries) pip install -U numpy scipy scikit-learn

You'll also need to run npm install in the project's root directory to install the nodejs modules this project depends on.

Download Data

The download/ directory contains code for downloading and maintaining a file-database of youtube users and their activities.

In order to train a model so you can predict the activities of new users, you'll need to download the activity history for the users in data/users.csv.

Important: to download data from youtube (also done in predict.py) you'll need to provide a google developer's API key in the enviromentment variable GOOGLE_API_KEY. Further instructions are located at the bottom of this README.

To download the activity-history for all users in data/users.csv, run node download/downloadUserActivity.js.

Train & Predict

The predict/ directory holds scipts to train and evaluate machine learning models using the downloaded data. It also holds a prediction script.

To train the model using the users in data/users.csv, run python predict/train.py. This script will use 70% of the data to train logistic regression, support vector machine, and k-nearest neighbors models, and use the remaining 30% to evaluate the algorithms' prediction accuracy.

To predict the last event for a user given all history but the last event (youtube returns up to 256 previous history events), use the predict.py script. Call it with the username or channelId of the user you wish to predict. For example, python predict.py UCUAmhvYBcgftL9clT7xSYgg or python predict.py YoussefMeftah.

Specify a Google Developer's API key

To use scripts which interface with youtube's data API, you must provide a google developer's API key in the environment variable GOOGLE_API_KEY.

1. Sign up for a google developer's account.
2. Create a project
3. Enable the youtube data api
4. From the credentials tab, create a new api key (server).
5. Save your api key in the environment variable "GOOGLE_API_KEY"

• linux/mac: export GOOGLE_API_KEY=your_key_here

Limitations

This is a demonstration of my ability to scrape for and parse data as well as use, tune, and evaluate machine-learning algorithms. As such, I didn't spend much time tuning features or tweaking algorithms.

A useful predictor would require much more attention to features. For instance, channel statistics (view count, comment count, ...), recent history, and other chanel/user information could help with the predictive power of this tool. Also, more tuning of the three algorithms using the regularization and k nearest neighbors parameters might go a long way.