[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:# Filter selected features\ndata = all_data.copy()\nfeatures = [\'Id\', \'ForecastId\', \'Country_Region\', \'Province_State\', \'ConfirmedCases\', \'Fatalities\', \n       \'Day_num\']\ndata = data[features]\n\n# Apply log transformation to all ConfirmedCases and Fatalities columns, except for trends\ndata[[\'ConfirmedCases\', \'Fatalities\']] = data[[\'ConfirmedCases\', \'Fatalities\']].astype(\'float64\')\ndata[[\'ConfirmedCases\', \'Fatalities\']] = data[[\'ConfirmedCases\', \'Fatalities\']].apply(lambda x: np.log1p(x))\n\n# Replace infinites\ndata.replace([np.inf, -np.inf], 0, inplace=True)\n\n\n# Split data into train/test\ndef split_data(data):\n    \n    # Train set\n    x_train = data[data.ForecastId == -1].drop([\'ConfirmedCases\', \'Fatalities\'], axis=1)\n    y_train_1 = data[data.ForecastId == -1][\'ConfirmedCases\']\n    y_train_2 = data[data.ForecastId == -1][\'Fatalities\']\n\n    # Test set\n    x_test = data[data.ForecastId != -1].drop([\'ConfirmedCases\', \'Fatalities\'], axis=1)\n\n    # Clean Id columns and keep ForecastId as index\n    x_train.drop(\'Id\', inplace=True, errors=\'ignore\', axis=1)\n    x_train.drop(\'ForecastId\', inplace=True, errors=\'ignore\', axis=1)\n    x_test.drop(\'Id\', inplace=True, errors=\'ignore\', axis=1)\n    x_test.drop(\'ForecastId\', inplace=True, errors=\'ignore\', axis=1)\n    \n    return x_train, y_train_1, y_train_2, x_test\n\n\n# Ridge replace of the Linear regression model\ndef ridge_reg(X_train, Y_train, X_test):\n    # Create Ridge regression object\n    #regr = Ridge()        # commit 2\n    #regr = RidgeCV(cv=5)  # commit 4\n    #regr = Ridge(alpha=10) # commit 5\n    regr = Ridge(alpha=10) # now\n\n    # Train the model using the training sets\n    regr.fit(X_train, Y_train)\n\n    # Make predictions using the testing set\n    y_pred = regr.predict(X_test)\n    \n    return regr, y_pred\n\n\n# Submission function\ndef get_submission(s, df, target1, target2):\n    \n    prediction_1 = df[target1]\n    prediction_2 = df[target2]\n\n    # Submit predictions\n    prediction_1 = [int(item) for item in list(map(round, prediction_1))]\n    prediction_2 = [int(item) for item in list(map(round, prediction_2))]\n    \n    submission = pd.DataFrame({\n        "ForecastId": df[\'ForecastId\'].astype(\'int32\'), \n        "ConfirmedCases": prediction_1, \n        "Fatalities": prediction_2\n    })\n    submission.to_csv(s + \'.csv\', index=False)\''}, {'role': 'assistant', 'content': 'modelling'}]