text_html_output_1.png
import numpy as np

import os

import pandas as pd

train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')

train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)

object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']

train.Activity.sample(5)

feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
correlated_values.head()