├── nlp-1.py
├── 1.py
├── 2.py
└── 3.py


/nlp-1.py:
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1 | import urllib.request
2 | 
3 | response = urllib.request.urlopen('http://somiljain.me/')
4 | 
5 | html = response.read()
6 | 
7 | print (html)
8 | 


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/1.py:
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1 | import tensorflow as tf 
2 | from tensorflow.keras import Sequential 
3 | from tensorflow.keras.layers import Dense
4 | 
5 | model = Sequential()
6 | model.add(Dense(3, input_dim=2 ,activation='relu'))
7 | model.add(Dense(1, activation='softmax'))
8 |  
9 | 


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/2.py:
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 1 | import tensorflow as tf 
 2 | from tensorflow.keras import Model
 3 | from tensorflow.keras.layers import Dense
 4 | 
 5 | class SimpleNeuralNetwork(Model):
 6 |   def __init__(self):
 7 |     super(SimpleNeuralNetwork , self).__init__()
 8 |     self.layer1 = Dense(2,activation='relu')
 9 |     self.layer2 = Dense(3,activation='relu')
10 |     self.outputLayer = Dense(1,activation='softmax')
11 |   def call(self, x):
12 |     x = self.layer1(x)
13 |     x = self.layer2(x)
14 |     return self.outputLayer(x)
15 | 
16 | Model = SimpleNeuralNetwork()
17 |      
18 |  
19 | 


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/3.py:
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 1 | import quandl
 2 | import numpy as np 
 3 | from sklearn.linear_model import LinearRegression
 4 | from sklearn.svm import SVR
 5 | from sklearn.model_selection import train_test_split
 6 | # Get the stock data
 7 | df = quandl.get("WIKI/AMZN")
 8 | # Take a look at the data
 9 | # Get the Adjusted Close Price 
10 | df = df[['Adj. Close']] 
11 | # A variable for predicting 'n' days out into the future
12 | forecast_out = 30 #'n=30' days
13 | #Create another column (the target ) shifted 'n' units up
14 | df['Prediction'] = df[['Adj. Close']].shift(-forecast_out)
15 | #print the new data set
16 | print(df.tail())
17 | X = np.array(df.drop(['Prediction'],1))
18 | 
19 | #Remove the last '30' rows
20 | X = X[:-forecast_out]
21 | print(X)
22 | ### Create the dependent data set (y)  #####
23 | # Convert the dataframe to a numpy array 
24 | y = np.array(df['Prediction'])
25 | # Get all of the y values except the last '30' rows
26 | y = y[:-forecast_out]
27 | print(y)
28 | # Split the data into 80% training and 20% testing
29 | x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
30 | # Create and train the Support Vector Machine (Regressor) 
31 | svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) 
32 | svr_rbf.fit(x_train, y_train)
33 | 
34 | 


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