[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# KAGGLE competition >> Fatality rate\n# This script trains the model on the latest dataset and predicts the next value\n# Author: Neilay Khasnabish\n\n\n#  Import libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import AdaBoostRegressor\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import RandomizedSearchCV\n\n# Making Kaggle dataset ready\ndef kaggle(dfTrain, dfTest):\n    pd.set_option(\'display.max_columns\', None)\n\n    dfTest[\'DateNew\'] = pd.to_datetime(dfTest[\'Date\'])\n    dfTest = dfTest.drop([\'Date\'], axis=1)\n    dfTest = dfTest.rename(columns={"DateNew": "Date"})\n    dfTest[\'Year\'] = dfTest[\'Date\'].dt.year\n    dfTest[\'Month\'] = dfTest[\'Date\'].dt.month\n    dfTest[\'Day\'] = dfTest[\'Date\'].dt.day\n    dfTest = dfTest.drop([\'Date\'], axis=1)\n    dfTest = dfTest.fillna(\'DummyProvince\')\n    #dfTest.to_csv(\'G:/Kaggle_COVID/covid19-global-forecasting-week-2/dummyTest.csv\')\n\n    dfTrain[\'DateNew\'] = pd.to_datetime(dfTrain[\'Date\'])\n    dfTrain = dfTrain.drop([\'Date\'], axis=1)\n    dfTrain = dfTrain.rename(columns={"DateNew": "Date"})\n    dfTrain[\'Year\'] = dfTrain[\'Date\'].dt.year\n    dfTrain[\'Month\'] = dfTrain[\'Date\'].dt.month\n    dfTrain[\'Day\'] = dfTrain[\'Date\'].dt.day\n    dfTrain = dfTrain.drop([\'Date\'], axis=1)\n    dfTrain = dfTrain.fillna(\'DummyProvince\')\n    #dfTrain.to_csv(\'G:/Kaggle_COVID/covid19-global-forecasting-week-2/dummyTrain.csv\')\n\n    result = pd.merge(dfTest, dfTrain, how=\'left\', on=[\'Country_Region\', \'Province_State\', \'Year\', \'Month\', \'Day\'])\n    result = result.fillna(-1)\n\n    # Clutter removal\n    [rr, cc] = np.shape(result)\n    for iQuit in range(rr):\n        if result.loc[iQuit, \'Day\'] == 4 :\n            result.loc[iQuit, \'ConfirmedCases\'] = -1\n            result.loc[iQuit, \'Fatalities\'] = -1\n\n    #result.to_csv(\'G:/Kaggle_COVID/covid19-global-forecasting-week-2/temp.csv\')\n    return result\n\n\n# Finding RMSE\ndef ErrorCalc(mdl, ref, tag):\n    relError = np.abs(mdl - ref)/ np.abs(ref+1)\n    MeanErrorV = np.mean(relError)\n    print(tag + \': Mean Rel Error in %: \', MeanErrorV * 100)\n    return MeanErrorV\n\n\n# Since cumulative prediction >> This script is not used for Kaggle dataset\ndef AdjustingErrorsOutliers(tempPred, df) :\n    tempPred = np.round(tempPred)\n    tempPrev = df[\'day5\'].to_numpy() # Next cumulative prediction must be more than or equal to previous\n    for i in range(len(tempPred)):\n        if tempPred[i] < tempPrev[i] : # Since cumulative prediction\n            tempPred[i] = tempPrev[i]\n    return tempPred\n\n\n# Train model\ndef TrainMdl (trainIpData, trainOpData) :\n\n\n\n    testSize = 0.1 # 90:10 ratio >> for final testing\n\n    print(\'Training starts ...\')\n\n    randomState=None\n    # randomState = 42 # For train test split\n\n    # Final validation\n    X_train, X_test, y_train, y_test = train_test_split(trainIpData, trainOpData, test_size=testSize, random_state=randomState)\n\n    # Another set of input\n    TrainIP = X_train[[\'day1\', \'day2\', \'day3\', \'day4\', \'day5\', \'diff1\', \'diff2\', \'diff3\', \'diff4\']]\n    TrainOP = X_train[\'gammaFun\']\n    TestIP = X_test[[\'day1\', \'day2\', \'day3\', \'day4\', \'day5\', \'diff1\', \'diff2\', \'diff3\', \'diff4\']]\n    TestOP = X_test[\'gammaFun\']\n\n\n    # Adaboost Regressor >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n    treeDepth = 10 # Fixed\n    mdl = DecisionTreeRegressor(max_depth=treeDepth) # This is fixed\n    param_grid = {\n        \'n_estimators\': [100, 250, 500],\n        \'learning_rate\': [0.1, 0.01, 0.001]\n                    }\n    regrMdl = AdaBoostRegressor(base_estimator=mdl)\n    clf = RandomizedSearchCV(estimator = regrMdl, param_distributions = param_grid,\n                                         n_iter = 100, cv = 3, verbose=0, random_state=42, n_jobs = -1)\n    clf.fit(TrainIP, TrainOP)\n\n\n    # Calculating Error\n    y_predictedTrain = clf.predict(TrainIP) # Predicting the gamma function\n    y_predictedTrain = AdjustingErrorsOutliers(y_predictedTrain * TrainIP[\'day5\'].to_numpy(), TrainIP)\n    ErrorCalc(y_predictedTrain, y_train.to_numpy(), \'Train Data-set\') # y_predictedTrain converted to numbers\n\n    y_predictedTest = clf.predict(TestIP) # Predicting the gamma function\n    y_predictedTest = AdjustingErrorsOutliers(y_predictedTest * TestIP[\'day5\'].to_numpy(), TestIP)\n    ErrorCalc(y_predictedTest, y_test.to_numpy(), \'Validation Data-set \') # y_predictedTest converted to numbers\n\n    print(\'-----------------------------------------------------------\')\n\n\n    # Read Kaggle dataset\n    dfTrain = pd.read_csv("../input/covid19-global-forecasting-week-2/train.csv")\n    dfTest = pd.read_csv("../input/covid19-global-forecasting-week-2/test.csv")\n    df = kaggle(dfTrain, dfTest)\n\n\n\n    [rr, cc] = np.shape(df)\n    for iP in range(rr):\n        if df.loc[iP, \'Fatalities\'] == -1 : # iP-th position need to be predicted\n            # Create a dataframe\n            day5 = df.loc[iP-1, \'Fatalities\']\n            day4 = df.loc[iP-2, \'Fatalities\']\n            day3 = df.loc[iP-3, \'Fatalities\']\n            day2 = df.loc[iP-4, \'Fatalities\']\n            day1 = df.loc[iP-5, \'Fatalities\']\n            diff1 = day5 - day4\n            diff2 = day4 - day3\n            diff3 = day3 - day2\n            diff4 = day2 - day1\n            data = {\'day1\': [day1], \'day2\': [day2], \'day3\': [day3], \'day4\': [day4], \'day5\': [day5],\n                    \'diff1\': [diff1], \'diff2\': [diff2], \'diff3\': [diff3], \'diff4\': [diff4]}\n            dfPredict = pd.DataFrame(data)\n            finalPrediction = clf.predict(dfPredict[[\'day1\', \'day2\', \'day3\', \'day4\', \'day5\', \'diff1\', \'diff2\', \'diff3\', \'diff4\']]) * day5\n            if finalPrediction < day5 :\n                finalPrediction = day5\n            df.loc[iP, \'Fatalities\'] = np.round(finalPrediction) # Update the current location\n\n\n    return df\n\n\n# Main code starts\ndf =  pd.read_csv("../input/processedtimedata/TrainTest_Fatality.csv") # Processed dta from JHU\ntrainIpData = df[[\'day1\', \'day2\', \'day3\', \'day4\', \'day5\', \'gammaFun\', \'diff1\', \'diff2\', \'diff3\', \'diff4\']]\ntrainOpData = df[\'dayPredict\'] # Predicted fatality\nfatality_dF = TrainMdl (trainIpData, trainOpData) # Kaggle data will be read inside\nprint(\'Completed ...\')\n#predictions_dF[[\'ForecastId\', \'ConfirmedCases\', \'Fatalities\']].to_csv(\'G:/Kaggle_COVID/covid19-global-forecasting-week-2/submission_Fatality.csv\', index = False)\''}, {'role': 'assistant', 'content': "['modelling']"}]