# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

print(pd.read_csv('../input/digit-recognizer/train.csv').head())
print(pd.read_csv('../input/digit-recognizer/test.csv').head())
print(pd.read_csv('../input/digit-recognizer/sample_submission.csv').head())

from sklearn.neural_network import MLPClassifier
df=pd.read_csv('../input/digit-recognizer/train.csv')
col = ['pixel%d'%i for i in range(784)]
plf=MLPClassifier((100,100,100),max_iter=5)
plf.fit(df[col], df['label'])

df=pd.read_csv('../input/digit-recognizer/test.csv')
res=plf.predict(df[col])

df=pd.read_csv('../input/digit-recognizer/sample_submission.csv')
df['Label']=res

df.to_csv('submission.csv', index=False)










# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session