Predict the market price of players based on their stats based on Tensorflow
This project utilizes a machine learning model, stored in best_model.h5, to predict the prices of players based on their attributes. The environment dependencies are listed in environment.yml, and users are encouraged to install them independently.
To set up the required environment, use the following command:
conda env create -f environment.ymlRun the following command to check the accuracy of the model predictions, where the maximum prediction error should not exceed 10%.:
python evaluate.py
To make predictions on player prices using their attributes, run:
python predict.py
best_model.h5: The machine learning model for predicting player prices.
environment.yml: Environment dependencies for the project.
evaluate.py: Script to evaluate the accuracy of the model.
predict.py: Script to predict player prices based on input attributes.