import numpy as np import pandas as pd from pandas import Series,DataFrame # Import the `RandomForestClassifier` from sklearn.ensemble import RandomForestClassifier #you can execute arbitrary python code original_train = pd.read_csv("../input/train.csv") original_test = pd.read_csv("../input/test.csv") train = original_train.copy() test = original_test.copy() target = train["Survived"].values # Embarked # only in train, fill the two missing values with the most occurred value, which is "S". train["Embarked"] = train["Embarked"].fillna("S") # Either to consider Embarked column in predictions, # and remove "S" dummy variable, # and leave "C" & "Q", since they seem to have a good rate for Survival. embark_dummies_train = pd.get_dummies(train['Embarked']) #embark_dummies_train.drop(['S'], axis=1, inplace=True) embark_dummies_test = pd.get_dummies(test['Embarked']) #embark_dummies_test.drop(['S'], axis=1, inplace=True) train = train.join(embark_dummies_train) test = test.join(embark_dummies_test) train.drop(['Embarked'], axis=1,inplace=True) test.drop(['Embarked'], axis=1,inplace=True) # Fare # only for test, since there is a missing "Fare" values test["Fare"].fillna(test["Fare"].median(), inplace=True) # convert from float to int train['Fare'] = train['Fare'].astype(int) test['Fare'] = test['Fare'].astype(int) # Age # get average, std, and number of NaN values in train average_age_train = train["Age"].mean() std_age_train = train["Age"].std() count_nan_age_train = train["Age"].isnull().sum() # get average, std, and number of NaN values in test average_age_test = test["Age"].mean() std_age_test = test["Age"].std() count_nan_age_test = test["Age"].isnull().sum() # generate random numbers between (mean - std) & (mean + std) rand_1 = np.random.randint(average_age_train - std_age_train, average_age_train + std_age_train, size = count_nan_age_train) rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test) # fill NaN values in Age column with random values generated train["Age"][np.isnan(train["Age"])] = rand_1 test["Age"][np.isnan(test["Age"])] = rand_2 # convert from float to int train['Age'] = train['Age'].astype(int) test['Age'] = test['Age'].astype(int) #Family # Instead of having two columns Parch & SibSp, # we can have only one column represent if the passenger had any family member aboard or not, # Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not. train['Family'] = train["Parch"] + train["SibSp"] train['Family'].loc[train['Family'] > 0] = 1 train['Family'].loc[train['Family'] == 0] = 0 test['Family'] = test["Parch"] + test["SibSp"] test['Family'].loc[test['Family'] > 0] = 1 test['Family'].loc[test['Family'] == 0] = 0 # drop Parch & SibSp train = train.drop(['SibSp','Parch'], axis=1) test = test.drop(['SibSp','Parch'], axis=1) # Sex # As we see, children(age < ~16) on aboard seem to have a high chances for Survival. # So, we can classify passengers as males, females, and child def get_person(passenger): age,sex = passenger return 'child' if age < 16 else sex train['Person'] = train[['Age','Sex']].apply(get_person,axis=1) test['Person'] = test[['Age','Sex']].apply(get_person,axis=1) # No need to use Sex column since we created Person column train.drop(['Sex'],axis=1,inplace=True) test.drop(['Sex'],axis=1,inplace=True) # create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers person_dummies_train = pd.get_dummies(train['Person']) person_dummies_train.columns = ['Child','Female','Male'] #person_dummies_train.drop(['Male'], axis=1, inplace=True) person_dummies_test = pd.get_dummies(test['Person']) person_dummies_test.columns = ['Child','Female','Male'] #person_dummies_test.drop(['Male'], axis=1, inplace=True) train = train.join(person_dummies_train) test = test.join(person_dummies_test) train.drop(['Person'],axis=1,inplace=True) test.drop(['Person'],axis=1,inplace=True) # Pclass # create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengers pclass_dummies_train = pd.get_dummies(train['Pclass']) pclass_dummies_train.columns = ['Class_1','Class_2','Class_3'] #pclass_dummies_train.drop(['Class_3'], axis=1, inplace=True) pclass_dummies_test = pd.get_dummies(test['Pclass']) pclass_dummies_test.columns = ['Class_1','Class_2','Class_3'] #pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True) train.drop(['Pclass'],axis=1,inplace=True) test.drop(['Pclass'],axis=1,inplace=True) train = train.join(pclass_dummies_train) test = test.join(pclass_dummies_test) #drop unnecessary columns train = train.drop(['PassengerId','Name','Ticket','Cabin','Survived', 'Age'], axis=1) test = test.drop(['PassengerId','Name','Ticket','Cabin','Age'], axis=1) # Random Forests random_forest = RandomForestClassifier(min_samples_split=2, n_estimators=500, random_state=1) random_forest.fit(train, target) survived_prediction = random_forest.predict(test) print(random_forest.score(train, target)) #Print to standard output, and see the results in the "log" section below after running your script #print("\n\nTop of the training data:") #print(train.head()) #print("\n\nSummary statistics of training data") #print(train.describe()) submission = pd.DataFrame({ "PassengerId": original_test["PassengerId"], "Survived": survived_prediction }) submission.to_csv('titanic.csv', index=False)