#!/usr/bin/env python

# coding: utf-8

# In[1]:

import pandas as pd

train=pd.read_csv("C:\Windows\Lenovo\\train.csv")

gender=pd.read_csv("C:\Windows\Lenovo\\gender_submission.csv")

test=pd.read_csv("C:\Windows\Lenovo\\test.csv")

# In[2]:

import matplotlib.pyplot

# In[3]:

def bar_chart(feature):

df.plot(kind="bar",stacked=True,figsize=(10,5))

# In[4]:

train.head()

# In[5]:

test.head()

# In[6]:

gender.head()

# In[7]:

import matplotlib.pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')

import seaborn as sns

sns.set()

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

# In[9]:

train_test_data=[train,test]

for dataset in train_test_data:

dataset["Title"]=dataset["Name"].str.extract('([A-Za-z]+)\.',expand=False)

# In[10]:

train['Title'].value_counts()

# In[11]:

title_mapping={"Mr":0,"Miss":1,"Mrs":2,"Master":3,"Dr":3,"Rev":3,"Col":3,"Major":3,

"Mlle":3,"Countess":3,"Dona":3,"Mme":3,"Capt":3,"Sir":3,"Don":3,

"Jonkheer":3,"Lady":3,"Ms":3}

for dataset in train_test_data:

dataset["Title"]=dataset["Title"].map(title_mapping)

# In[12]:

train["Age"].fillna(train.groupby("Title")["Age"].transform("median"),inplace=True)

test["Age"].fillna(test.groupby("Title")["Age"].transform("median"),inplace=True)

# In[13]:

train.head()

# In[14]:

train.drop("Name",axis=1,inplace=True)

test.drop("Name",axis=1,inplace=True)

# In[15]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Age",shade=True)

facet.set(xlim=(0,train["Age"].max()))

facet.add_legend()

plt.show()

# In[16]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Age",shade=True)

facet.set(xlim=(0,train["Age"].max()))

facet.add_legend()

plt.xlim(20,30)

# In[17]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Age",shade=True)

facet.set(xlim=(0,train["Age"].max()))

facet.add_legend()

plt.xlim(0,20)

# In[18]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Age",shade=True)

facet.set(xlim=(0,train["Age"].max()))

facet.add_legend()

plt.xlim(40,60)

# In[19]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Age",shade=True)

facet.set(xlim=(0,train["Age"].max()))

facet.add_legend()

plt.xlim(60,80)

# In[20]:

train.head()

# In[21]:

for dataset in train_test_data:

dataset.loc[dataset["Age"] <=16, "Age"]=0,

dataset.loc[(dataset["Age"] >16)&(dataset["Age"] <=26), "Age"]=1,

dataset.loc[(dataset["Age"] >26)&(dataset["Age"] <=36), "Age"]=2,

dataset.loc[(dataset["Age"] >36)&(dataset["Age"] <=62), "Age"]=3,

dataset.loc[dataset["Age"] >62, "Age"]=4

# In[22]:

train.head()

# In[23]:

Pclass1=train[train["Pclass"]==1]["Embarked"].value_counts()

Pclass2=train[train["Pclass"]==2]["Embarked"].value_counts()

Pclass3=train[train["Pclass"]==3]["Embarked"].value_counts()

df=pd.DataFrame([Pclass1,Pclass2,Pclass3])

df.index=["1st class","2nd class","3rd class"]

df.plot(kind="bar",stacked=True,figsize=(10,5))

# In[24]:

for dataset in train_test_data:

dataset["Embarked"]=dataset["Embarked"].fillna("S")

# In[25]:

train.head()

# In[26]:

embarked_mapping = {"S":0,"C":1,"Q":2}

for dataset in train_test_data:

dataset["Embarked"]=dataset["Embarked"].map(embarked_mapping)

# In[27]:

test.head()

# In[28]:

train["Fare"].fillna(train.groupby("Pclass")["Fare"].transform("median"),inplace=True)

test["Fare"].fillna(train.groupby("Pclass")["Fare"].transform("median"),inplace=True)

# In[29]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"Fare",shade=True)

facet.set(xlim=(0,train["Fare"].max()))

facet.add_legend()

plt.show()

# In[30]:

for dataset in train_test_data:

dataset.loc[dataset["Fare"] <=17, "Fare"]=0,

dataset.loc[(dataset["Fare"] >17)&(dataset["Fare"] <=30), "Fare"]=1,

dataset.loc[(dataset["Fare"] >30)&(dataset["Fare"] <=100), "Fare"]=2,

dataset.loc[dataset["Fare"] >100, "Fare"]=3

# In[31]:

train.head()

# In[32]:

for dataset in train_test_data:

dataset["Cabin"]=dataset["Cabin"].str[:1]

# In[33]:

Pclass1=train[train["Pclass"]==1]["Cabin"].value_counts()

Pclass2=train[train["Pclass"]==2]["Cabin"].value_counts()

Pclass3=train[train["Pclass"]==3]["Cabin"].value_counts()

df=pd.DataFrame([Pclass1,Pclass2,Pclass3])

df.index=["1st class","2nd class","3rd class"]

df.plot(kind="bar",stacked=True,figsize=(10,5))

# In[34]:

cabin_mapping = {"A":0,"B":0.4,"C":0.8,"D":1.2,"E":1.6,"F":2,"G":2.4,"T":2.8}

for dataset in train_test_data:

dataset["Cabin"]=dataset["Cabin"].map(cabin_mapping)

# In[35]:

train["Cabin"].fillna(train.groupby("Pclass")["Cabin"].transform("median"),inplace=True)

test["Cabin"].fillna(train.groupby("Pclass")["Cabin"].transform("median"),inplace=True)

# In[36]:

test.head()

# In[37]:

train['FamilySize']=train["SibSp"]+train["Parch"]+1

test['FamilySize']=test["SibSp"]+test["Parch"]+1

# In[38]:

facet=sns.FacetGrid(train,hue="Survived",aspect=4)

facet.map(sns.kdeplot,"FamilySize",shade=True)

facet.set(xlim=(0,train["FamilySize"].max()))

facet.add_legend()

plt.show()

# In[39]:

Family_mapping = {1: 0,2 :0.4,3: 0.8,

4: 1.2,5: 1.6,6: 2,7: 2.4,

8: 2.8,9: 3.2,10: 3.6,11: 4.0}

for dataset in train_test_data:

dataset["FamilySize"]=dataset["FamilySize"].map(Family_mapping)

# In[40]:

train.head()

# In[41]:

sex_mapping={"male":0,"female":1}

for dataset in train_test_data:

dataset["Sex"]=dataset["Sex"].map(sex_mapping)

# In[42]:

train.head()

# In[43]:

features_drop=["Ticket","SibSp","Parch"]

train=train.drop(features_drop,axis=1)

test=test.drop(features_drop,axis=1)

train=train.drop(["PassengerId"],axis=1)

# In[44]:

train_data=train.drop("Survived",axis=1)

target=train["Survived"]

train_data.shape,target.shape

# In[45]:

train_data.head(10)

# In[46]:

from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

import numpy as np

from sklearn.model_selection import KFold

from sklearn.model_selection import cross_val_score

k_Fold=KFold(n_splits=10,shuffle=True,random_state=0)

# In[47]:

clf=KNeighborsClassifier(n_neighbors=13)

scoring='accuracy'

score=cross_val_score(clf,train_data,target,cv=k_Fold,n_jobs=1,scoring=scoring)

print(score)

# In[48]:

round(np.mean(score)*100,2)

# In[49]:

clf=DecisionTreeClassifier()

scoring='accuracy'

score=cross_val_score(clf,train_data,target,cv=k_Fold,n_jobs=1,scoring=scoring)

print(score)

# In[50]:

round(np.mean(score)*100,2)

# In[51]:

clf=RandomForestClassifier(n_estimators=13)

scoring='accuracy'

score=cross_val_score(clf,train_data,target,cv=k_Fold,n_jobs=1,scoring=scoring)

print(score)

# In[52]:

round(np.mean(score)*100,2)

# In[53]:

clf= GaussianNB()

scoring='accuracy'

score=cross_val_score(clf,train_data,target,cv=k_Fold,n_jobs=1,scoring=scoring)

print(score)

# In[54]:

round(np.mean(score)*100,2)

# In[55]:

clf= SVC()

scoring='accuracy'

score=cross_val_score(clf,train_data,target,cv=k_Fold,n_jobs=1,scoring=scoring)

print(score)

# In[56]:

round(np.mean(score)*100,2)

# In[57]:

clf=SVC()

clf.fit(train_data,target)

test_data=test.drop("PassengerId",axis=1).copy()

prediction=clf.predict(test_data)

# In[65]:

submission =pd.DataFrame({"PassengerId":test["PassengerId"],"Survived":prediction}).to_csv('kaggle0.csv',index=False)

# In[67]:

submission_1=pd.read_csv("kaggle0.csv")

# In[68]:

submission_1.head()

# In[ ]: