titanic.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB

# Importing dataset
data = pd.read_csv("train.csv")

# Convert categorical variable to numeric
data["Sex_cleaned"]=np.where(data["Sex"]=="male",0,1)
data["Embarked_cleaned"]=np.where(data["Embarked"]=="S",0,
                                  np.where(data["Embarked"]=="C",1,
                                           np.where(data["Embarked"]=="Q",2,3)
                                          )
                                 )
# Cleaning dataset of NaN
data=data[[
    "Survived",
    "Pclass",
    "Sex_cleaned",
    "Age",
    "SibSp",
    "Parch",
    "Fare",
    "Embarked_cleaned"
]].dropna(axis=0, how='any')

# Split dataset in training and test datasets
X_train, X_test = train_test_split(data, test_size=0.5, random_state=int(time.time()))

# Instantiate the classifier
gnb = GaussianNB()
used_features =[
    "Pclass",
    "Sex_cleaned",
    "Age",
    "SibSp",
    "Parch",
    "Fare",
    "Embarked_cleaned"
]

# Train classifier
gnb.fit(
    X_train[used_features].values,
    X_train["Survived"]
)
y_pred = gnb.predict(X_test[used_features])

# Print results
print("Number of mislabeled points out of a total {} points : {}, performance {:05.2f}%"
      .format(
          X_test.shape[0],
          (X_test["Survived"] != y_pred).sum(),
          100*(1-(X_test["Survived"] != y_pred).sum()/X_test.shape[0])
))