Titanic data analysis using logistic regression

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import math
% matplotlib inline

titanic_data1=pd.read_csv("train.csv")
titanic_data1.head(12)

print('No of passengers in original data:' + str(len(titanic_data1.index)))

Analyzing the data :

sns.countplot(x="Survived",data=titanic_data1)

sns.countplot(x="Survived",hue="Sex",data=titanic_data1)

sns.countplot(x="Survived",hue="Age",data=titanic_data1)

sns.countplot(x="Survived",hue="Pclass",data=titanic_data1)

titanic_data1["Age"].plot.hist()

sns.countplot(x="Survived",hue="SibSp",data=titanic_data1)

Data Wrangling :

titanic_data1.isnull()

titanic_data1.isnull().sum()

sns.heatmap(titanic_data1.isnull(),yticklabels="False",cmap='viridis')

sns.boxplot(x='Pclass',y='Age',data=titanic_data1)

titanic_data1.drop("Cabin",axis=1,inplace=True)

titanic_data1.head(10)

titanic_data1.dropna(inplace=True)

sns.heatmap(titanic_data1.isnull(),yticklabels=False,cbar=False)

titanic_data1.isnull().sum()

titanic_data1.head(2)

sex=pd.get_dummies(titanic_data1['Sex'],drop_first=True) # If 1 then it is male and if 0 then female
sex.head(5)

embark=pd.get_dummies(titanic_data1['Embarked'],drop_first=True)
embark.head(5)

Pcl=pd.get_dummies(titanic_data1['Pclass'],drop_first=True)
Pcl.head(5)

titanic_data1=pd.concat([titanic_data1,sex,embark,Pcl],axis=1)

titanic_data1.head(5)

titanic_data1.drop(['Sex','Embarked','PassengerId','Name','Ticket'], axis=1,inplace=True)

titanic_data1.head()

titanic_data1.drop(['Pclass'], axis=1,inplace=True)

titanic_data1.head()

Train & Test :

X=titanic_data1.drop("Survived",axis=1)
y=titanic_data1["Survived"]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

from sklearn.linear_model import LogisticRegression

logmodel=LogisticRegression()

logmodel.fit(X_train,y_train)

predictions=logmodel.predict(X_test)

from sklearn.metrics import classification_report

classification_report(y_test,predictions)

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test,predictions)

from sklearn.metrics import accuracy_score

accuracy_score(y_test,predictions)