{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Football Match Probability Random Prediction","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Activation\nsubmission = pd.read_csv(\"/kaggle/input/football-match-probability-prediction/sample_submission.csv\")\nresult = np.random.rand(len(submission), 3)\nmodel = tf.keras.Sequential([\n    tf.keras.Input((3,)),\n    Activation(\"softmax\")\n])\nresult = model(result).numpy()\nsubmission[[\"home\", \"draw\", \"away\"]] = result\nsubmission.to_csv(\"submission.csv\", index=False)\nsubmission.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-01T16:33:18.916681Z","iopub.execute_input":"2022-05-01T16:33:18.91742Z","iopub.status.idle":"2022-05-01T16:33:26.202187Z","shell.execute_reply.started":"2022-05-01T16:33:18.917324Z","shell.execute_reply":"2022-05-01T16:33:26.201332Z"},"trusted":true},"execution_count":null,"outputs":[]}]}