training_model.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
from sklearn.model_selection import train_test_split
import pickle
from sklearn.ensemble import RandomForestClassifier

df = pd.read_csv('train.csv')

def get_title(name):
    if '.' in name:
        return name.split(',')[1].split('.')[0].strip()
    else:
        return 'Unknown'

def replace_titles(x):
    title = x['Title']
    if title in ['Capt', 'Col', 'Major']:
        return 'Officer'
    elif title in ["Jonkheer","Don",'the Countess', 'Dona', 'Lady',"Sir"]:
        return 'Royalty'
    elif title in ['the Countess', 'Mme', 'Lady']:
        return 'Mrs'
    elif title in ['Mlle', 'Ms']:
        return 'Miss'
    else:
        return title
    
df['Title'] = df['Name'].map(lambda x: get_title(x))
df['Title'] = df.apply(replace_titles, axis=1)

df['Age'].fillna(df['Age'].median(), inplace=True)
df['Fare'].fillna(df['Fare'].median(), inplace=True)
df['Embarked'].fillna("S", inplace=True)
df.drop("Cabin", axis=1, inplace=True)
df.drop("Ticket", axis=1, inplace=True)
df.drop("Name", axis=1, inplace=True)
df.Sex.replace(('male','female'), (0,1), inplace = True)
df.Embarked.replace(('S','C','Q'), (0,1,2), inplace = True)
df.Title.replace(('Mr','Miss','Mrs','Master','Dr','Rev','Officer','Royalty'), (0,1,2,3,4,5,6,7), inplace = True)

x = df.drop(['Survived', 'PassengerId'], axis = 1)
y = df['Survived']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.1)
randomforest = RandomForestClassifier()
randomforest.fit(x_train, y_train)


pickle.dump(randomforest, open('titanic_model.sav', 'wb'))


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