titanic predictions.py

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

# In[2]:


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'
titles = sorted(set([x for x in df.Name.map(lambda x: get_title(x))]))

def replace_titles(x):
    title = x['Title']
    if title in ['Capt', 'Col', 'Major']:
        return 'Officer'
    elif title in ['Don', 'Jonkheer', 'Dona', 'Sir', 'the Countess']:
        return 'Royalty'
    elif title in ['Mme', 'Ms']:
        return 'Mrs'
    elif title in ['Mlle', 'Lady']:
        return 'Miss'
    else:
        return title

df['Title'] = df['Name'].map(lambda x: get_title(x))
df['Title'] = df.apply(replace_titles, axis=1)

df.drop("Name", axis=1,inplace=True)
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.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_val, y_train, y_val = 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'))


# In[3]:


def prediction_model(pclass,sex,age,sibsp,parch,fare,embarked,title):
   import pickle
   x = [[pclass,sex,age,sibsp,parch,fare,embarked,title]]
   randomforest = pickle.load(open('titanic_model.sav', 'rb'))
   prediction = randomforest.predict(x)
   return prediction


# In[4]:


prediction_model(1,1,23,1,1,21,1,1)


# In[ ]: