Titanic Survival Prediction

Imported all the required library

import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

Loading and Viewing the data

data=pd.read_csv('titanic_data.csv')
data.head()

Data Visualisation

Ploting the survival rate

Ploting Age graph

Ploting Survival Based on Sex

Ploting Survival Based on Passenger Class

Ploting Survival Based on Sibling

Ploting Survival Based on Parents

Data filling

clean_test.Age = clean_test.Age.fillna(titanic_data['Age'].mean())
testing_data.Fare=testing_data.Fare.fillna(data.Fare.mean())

Using Different Model's

Creating Training and Testing Data set

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)

Training the model

model=LogisticRegression()
model.fit(x_train,y_train)

Making the prediction

new_prediction=model.predict(testing_data)

Getting the accuracy score

from sklearn.metrics import accuracy_score


acc_logreg = round(accuracy_score(prediction, y_test) * 100, 2)
print(acc_logreg)