# Column name and type: # nFires (int) Number of fires in the area # perW (float) Percentage of women (noted as float, e.g. 0.51) # perM (float) Percentage of men (noted as float, e.g. 0.51) # perSH (float) Percentage of single households (noted as float, e.g. 0.51) # perMH (float) Percentage of more person households (noted as float, e.g. 0.51) # group0to40 (float) Percentage of people aged 0 to 40 (noted as float, e.g. 0.51) # group40to80 (float) Percentage of people aged 40 to 80 (noted as float, e.g. 0.51) # group80up (float) Percentage of people aged 80 and up (noted as float, e.g. 0.51) import pandas as pd from sklearn.ensemble import RandomForestClassifier names = ['nFires', 'perW', 'perM', 'perSH', 'perMH', 'group0to40', 'group40to80', 'group80up'] fireData = pd.read_csv('link_to_csv', delimiter=r"\s+", names=names, header=None, engine='python') X = fireData[['perW', 'perM', 'perSH', 'perMH', 'group0to40', 'group40to80', 'group80up']] Y = fireData['nFires'] clf = RandomForestClassifier(n_estimators = x, criterion = 'entropy', max_depth = 10, min_samples_leaf = 5) clf = clf.fit(X, Y)
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