In [1]:

    

import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from logistic import Logistic_Regression, hypothesis


    

In [2]:

    

train_df = pd.read_csv('data/train.csv')


    

In [3]:

    

train_df.head()


    

    
Out[3]:






  

    

      

      
PassengerId
      
Survived
      
Pclass
      
Name
      
Sex
      
Age
      
SibSp
      
Parch
      
Ticket
      
Fare
      
Cabin
      
Embarked
    
  
  

    

      
0
      
1
      
0
      
3
      
Braund, Mr. Owen Harris
      
male
      
22.0
      
1
      
0
      
A/5 21171
      
7.2500
      
NaN
      
S
    
    

      
1
      
2
      
1
      
1
      
Cumings, Mrs. John Bradley (Florence Briggs Th...
      
female
      
38.0
      
1
      
0
      
PC 17599
      
71.2833
      
C85
      
C
    
    

      
2
      
3
      
1
      
3
      
Heikkinen, Miss. Laina
      
female
      
26.0
      
0
      
0
      
STON/O2. 3101282
      
7.9250
      
NaN
      
S
    
    

      
3
      
4
      
1
      
1
      
Futrelle, Mrs. Jacques Heath (Lily May Peel)
      
female
      
35.0
      
1
      
0
      
113803
      
53.1000
      
C123
      
S
    
    

      
4
      
5
      
0
      
3
      
Allen, Mr. William Henry
      
male
      
35.0
      
0
      
0
      
373450
      
8.0500
      
NaN
      
S
    
  

In [4]:

    

sns.heatmap(train_df.isnull(),yticklabels=False,cmap='viridis',cbar=False)


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x10fce7b70>






    

In [5]:

    

pclass_1_mean_age = train_df.groupby(['Pclass']).mean().iloc[0]['Age']
pclass_2_mean_age = train_df.groupby(['Pclass']).mean().iloc[1]['Age']
pclass_3_mean_age = train_df.groupby(['Pclass']).mean().iloc[2]['Age']


    

In [6]:

    

def impute_age(cols):
    Age = cols[0]
    Pclass = cols[1]
    
    if pd.isnull(Age):
        if Pclass == 1:
            return pclass_1_mean_age
        elif Pclass == 2:
            return pclass_2_mean_age
        else:
            return pclass_3_mean_age
    else:
        return Age


    

In [7]:

    

train_df['Age'] = train_df[['Age','Pclass']].apply(impute_age,axis=1)


    

In [8]:

    

sns.heatmap(train_df.isnull(),yticklabels=False,cmap='viridis',cbar=False)


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x10fe420f0>






    

In [9]:

    

train_df.drop('Cabin',axis=1,inplace=True)
train_df.dropna(inplace=True)


    

In [10]:

    

sns.heatmap(train_df.isnull(),yticklabels=False,cmap='viridis',cbar=False)


    

    
Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x10fee02b0>






    

In [11]:

    

sex = pd.get_dummies(train_df['Sex'],drop_first=True)
embark = pd.get_dummies(train_df['Embarked'],drop_first=True)
train_df = pd.concat([train_df,sex,embark],axis=1)
train_df.drop(['Sex','Embarked','Name','Ticket','PassengerId'],axis=1,inplace=True)
train_df.head()


    

    
Out[11]:






  

    

      

      
Survived
      
Pclass
      
Age
      
SibSp
      
Parch
      
Fare
      
male
      
Q
      
S
    
  
  

    

      
0
      
0
      
3
      
22.0
      
1
      
0
      
7.2500
      
1
      
0
      
1
    
    

      
1
      
1
      
1
      
38.0
      
1
      
0
      
71.2833
      
0
      
0
      
0
    
    

      
2
      
1
      
3
      
26.0
      
0
      
0
      
7.9250
      
0
      
0
      
1
    
    

      
3
      
1
      
1
      
35.0
      
1
      
0
      
53.1000
      
0
      
0
      
1
    
    

      
4
      
0
      
3
      
35.0
      
0
      
0
      
8.0500
      
1
      
0
      
1
    
  

In [12]:

    

y = train_df['Survived'].copy().as_matrix()


    

In [13]:

    

X = train_df.drop('Survived',axis=1).as_matrix()


    

In [14]:

    

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4)


    

In [15]:

    

initial_theta = [0,0,0,0,0,0,0,0]
alpha = 0.1
iterations = 10000


    

In [16]:

    

final_theta = Logistic_Regression(X_train,y_train,alpha,initial_theta,iterations)


    

In [17]:

    

final_theta


    

    
Out[17]:





[3.3551008916371257,
 0.083981247695455563,
 -8.0577085272903552,
 -2.1257755774497347,
 0.48251739700268836,
 -32.51989230713253,
 -0.97823056431704292,
 3.1311103533613962]

In [18]:

    

def score(theta):
    model_score = 0
    length = len(X_test)
    for i in range(length):
        y_prediction = round(hypothesis(theta,X_test[i]))
        y_real = y_test[i]
        if y_prediction == y_real:
            model_score += 1
    score = model_score / length
    return float(score)


    

In [19]:

    

print("The model was correct " + str(round((score(final_theta) * 100),3)) + "% of the time.")


    

    




The model was correct 75.843% of the time.