Git clone this project, run "app.py" and open it using local host in your browser. Then, either enter your own data, or click "Generate Data" and "Predict" to see your results.
Create my own dataset using REST API and GraphQL calls that I can use in machine learning tasks. Focused around the popular videogame DOTA 2. I am using this as a way to reinforce my machine learning and neural network knowledge, while adding the unique aspect of building my own dataset. I am also using this as a way to learn full-stack development.
Using the Open DOTA REST API and Stratz API GraphQL Application, creating my own dataset to use Handling missing data, preprocessing, and feature engineering
Storing data in an SQLite database for convenience and lightweight use
Feel free to use this dataset, and my provided methods in DataPreprocessing.py to use for your own purposes.
Testing different types of machine learning models and neural networks for the predictor. For now, using a random forest model. This project will also serve as a way for me to get comfortable with different models and tasks. I created a large dataset for the purpose of being able to fit regression, binary classification, and multiclass classification tasks. However, much of it will not be implemented in the website, and will exist purely in the "prediction.ipynb" jupyter notebook.
Given that I have never designed a website before, I wanted to take my time and learn HTML, CSS, and JS. My primary goal in designing a website is to have a functional and responsive website, but I also want it to look professional and engaging, while fitting the darker color pallete of DOTA 2.