Simple Logistic Regression Model

import numpy as np from pandas import Series, DataFrame import pandas as pd from sklearn.grid_search import GridSearchCV from sklearn.linear_model import LogisticRegression def has_title(name): for s in [‘Mr.’, ‘Mrs.’, ‘Miss.’, ‘Dr.’, ‘Sir.’]: if name.find(s) >= 0: return True return False def munge(df): # Pclass for i in range(3): cls_fn = lambda x: 0.5 if x == i + 1 else -0.5 df[‘C%d’ % i] = df[‘Pclass’].map(cls_fn) # Sex => Gender gender_fn = lambda x: -0.5 if x == ‘male’ else 0.5 df[‘Gender’] = df[‘Sex’].map(gender_fn) # Name => Title title_fn = lambda x: 0.5 if has_title(x) else -0.5 title_col = df[‘Name’].map(title_fn) title_col.name = ‘Title’ dfn = pd.concat([df, title_col], axis=1) # Embarked s3fa_col = (dfn.Pclass == 3).mul(dfn.Sex == ‘female’).mul(dfn.Embarked == ‘S’).mul(dfn.Title > 0) s3fa_fn = lambda x: 0.5 if x else -0.5 s3fa_col…


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