[{'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:import sklearn\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\ndef processData(df, train=True):\ndf = df.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\nmean_age = df['Age'].mean()\ndf['Age'] = df['Age'].fillna(mean_age)\nfare_means = df.pivot_table('Fare', index='Pclass', aggfunc='mean')\ndf_test['Fare'] = df_test[['Fare', 'Pclass']].apply(lambda x:\nfare_means[x['Pclass']] if pd.isnull(x['Fare'])\nelse x['Fare'], axis=1)\ndf['Gender'] = df['Sex'].map({'male' : 0, 'female' : 1}).astype(int)\ndf = df.drop(['Sex'], axis=1)\nif train == True:\ncols = df.columns.tolist()\ncols = [cols[1]] + cols[0:1] + cols[2:]\ndf = df[cols]\nreturn df.values\ndf_train = pd.read_csv('data/train.csv')\ndf_test = pd.read_csv('data/test.csv')\ntrain_data = processData(df_train)\nprint('\\n')\ntest_data = processData(df_test, False)\nclf = RandomForestClassifier(n_estimators=200)\nclf.fit(train_data[0:, 2:], train_data[0:, 0])\noutput = clf.predict(test_data[:,1:])\nresult = np.c_[test_data[:,0].astype(int), output.astype(int)]\ndf_result = pd.DataFrame(result[:,0:2], columns=['PassengerId', 'Survived'])\nprint(df_result[:10])"}, {'role': 'assistant', 'content': 'modelling'}]