This repository contains a Python script that uses machine learning to predict the results of Dota2 games.
The script uses a neural network model implemented using TensorFlow and Keras. The model is trained on a dataset of Dota2 games, which is split into training, validation, and testing sets. The model's performance is evaluated based on its accuracy on the testing set.
The code can be divided into several sections:
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Data Loading: The Dota2 Games Results dataset is loaded from Google Drive.
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Data Preprocessing: The data is split into features and target variable, and further split into training, validation, and testing sets. The labels are also converted to one-hot encoded vectors.
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Model Definition: A neural network model is defined with several dense layers and dropout layers for regularization.
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Model Training: The model is trained for 50 epochs using the Adam optimizer and the categorical cross-entropy loss function.
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Model Saving and Loading: The trained model is saved and then loaded again to ensure it has been saved correctly.
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Model Evaluation: The model's performance is evaluated on the testing set, and the test accuracy is printed out.
To run the script, you need to have Python installed along with the following libraries:
- pandas
- numpy
- tensorflow
- sklearn
- keras
You also need to have access to the Dota2 Games Results dataset, which should be placed in your Google Drive under the path '/content/drive/MyDrive/colab_test_data/'.
If you have any questions or suggestions, feel free to open an issue or submit a pull request.
email : saradhi8142385201@gmail.com