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def binomial(n, k):
    """
    A fast way to calculate binomial coefficients by Andrew Dalke.
    See http://stackoverflow.com/questions/3025162/statistics-combinations-in-python
    """
    if 0 <= k <= n:
        ntok = 1
        ktok = 1
        for t in xrange(1, min(k, n - k) + 1):
            ntok *= n

DiogoRibeiro7

import matplotlib.pyplot as plt
%matplotlib inline
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import skflow

DiogoRibeiro7

titanic_df = pd.read_excel('titanic3.xls', 'titanic3', index_col=None, na_values=['NA'])
titanic_df.head()

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titanic_df['survived'].mean()

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class_sex_grouping = titanic_df.groupby(['pclass','sex']).mean()
class_sex_grouping

class_sex_grouping['survived'].plot.bar()

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titanic_df.groupby('pclass').mean()

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group_by_age = pd.cut(titanic_df["age"], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(group_by_age).mean()
age_grouping['survived'].plot.bar()

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titanic_df.count()
titanic_df = titanic_df.drop(['body','cabin','boat'], axis=1)
titanic_df["home.dest"] = titanic_df["home.dest"].fillna("NA")
titanic_df = titanic_df.dropna()
titanic_df.count()

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def preprocess_titanic_df(df):
    processed_df = df.copy()
    le = preprocessing.LabelEncoder()
    processed_df.sex = le.fit_transform(processed_df.sex)
    processed_df.embarked = le.fit_transform(processed_df.embarked)
    processed_df = processed_df.drop(['name','ticket','home.dest'],axis=1)
    return processed_df

processed_df = preprocess_titanic_df(titanic_df)

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X = processed_df.drop(['survived'], axis=1).values
y = processed_df['survived'].values

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X,y,test_size=0.2)