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## cryprocurrency_prediction.ipynb

      
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              October 31, 2018 19:17
            
              
                cryprocurrency_prediction.ipynb
              
          
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## data_prep.py
'''
Part 1.
Preparing data for conv net
Data can be found here:
https://www.kaggle.com/ucffool/dice-d4-d6-d8-d10-d12-d20-images
'''

from PIL import Image
import os
import numpy as np

## cnn_model.py
'''
Part 2.
Loading previously saved data, modeling the convolutional NN, fitting the model.
It took me ~10 minutes to train on my PC, so I saved it for further use.
'''

import numpy as np
import pickle
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Flatten, Dropout

## data_preparation.py
import pandas as pd

# prepare data for models
def pepper_data(df):
    df.rename(columns={'v2a1':'monthly_rent',
                       'hacdor':'overcrowd_bedroom',
                       'hacapo':'overcrowd_rooms',
                       'v14a':'has_bathroom',
                       'refrig':'has_refrigerator',
                       'r4t1':'persons_younger_12',

## DSPLab.py
'''
Тестовое задание: необходимо составить программу на python,
рисующую графики ошибок 1го и 2го рода для двух статистических гипотез (H0 и H1).
Параметры распределения гипотез выбрать самостоятельно.
'''

import numpy as np
import matplotlib.pyplot as plt

def gauss(x,mu=0,sigma=1):

## KaggleHouseRegression.py
import pandas as pd
import numpy as np
import seaborn as sns

# loading data
training_data=pd.read_csv('train.csv')
test_data=pd.read_csv('test.csv')

# preprocessing data
y_train=training_data['SalePrice']
	'''
	Part 1.
	Preparing data for conv net
	Data can be found here:
	https://www.kaggle.com/ucffool/dice-d4-d6-d8-d10-d12-d20-images
	'''

	from PIL import Image
	import os
	import numpy as np
	'''
	Part 2.
	Loading previously saved data, modeling the convolutional NN, fitting the model.
	It took me ~10 minutes to train on my PC, so I saved it for further use.
	'''

	import numpy as np
	import pickle
	import tensorflow as tf
	from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Flatten, Dropout
	import pandas as pd

	# prepare data for models
	def pepper_data(df):
	df.rename(columns={'v2a1':'monthly_rent',
	'hacdor':'overcrowd_bedroom',
	'hacapo':'overcrowd_rooms',
	'v14a':'has_bathroom',
	'refrig':'has_refrigerator',
	'r4t1':'persons_younger_12',
	'''
	Тестовое задание: необходимо составить программу на python,
	рисующую графики ошибок 1го и 2го рода для двух статистических гипотез (H0 и H1).
	Параметры распределения гипотез выбрать самостоятельно.
	'''

	import numpy as np
	import matplotlib.pyplot as plt

	def gauss(x,mu=0,sigma=1):
	import pandas as pd
	import numpy as np
	import seaborn as sns

	# loading data
	training_data=pd.read_csv('train.csv')
	test_data=pd.read_csv('test.csv')

	# preprocessing data
	y_train=training_data['SalePrice']