poly.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")

train_data.head()
test_data.head()

y = train_data["Survived"]

features = ["Pclass", "Sex", "SibSp", "Parch"]

X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])

# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)

predictions = lin_reg.predict(X_test)

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('my_submission.csv', index=False)
print("Your submission is done")


#Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)

predictions = lin_reg_2.predict(X_test)

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('my_submission.csv', index=False)
print("Your submission is done")