saranshrajput

import pandas as pd
import scipy.sparse as sparse
import numpy as np
import random
import implicit
from sklearn.preprocessing import MinMaxScaler

articles_df = pd.read_csv('shared_articles.csv')
interactions_df = pd.read_csv('users_interactions.csv')
articles_df.drop(['authorUserAgent', 'authorRegion', 'authorCountry'], axis=1, inplace=True)

saranshrajput

grouped_df['title'] = grouped_df['title'].astype("category")
grouped_df['personId'] = grouped_df['personId'].astype("category")
grouped_df['contentId'] = grouped_df['contentId'].astype("category")
grouped_df['person_id'] = grouped_df['personId'].cat.codes
grouped_df['content_id'] = grouped_df['contentId'].cat.codes

sparse_content_person = sparse.csr_matrix((grouped_df['eventStrength'].astype(float), (grouped_df['content_id'], grouped_df['person_id'])))
sparse_person_content = sparse.csr_matrix((grouped_df['eventStrength'].astype(float), (grouped_df['person_id'], grouped_df['content_id'])))

model = implicit.als.AlternatingLeastSquares(factors=20, regularization=0.1, iterations=50)

saranshrajput

content_id = 450
n_similar = 10

person_vecs = model.user_factors
content_vecs = model.item_factors

content_norms = np.sqrt((content_vecs * content_vecs).sum(axis=1))

scores = content_vecs.dot(content_vecs[content_id]) / content_norms
top_idx = np.argpartition(scores, -n_similar)[-n_similar:]

saranshrajput

def recommend(person_id, sparse_person_content, person_vecs, content_vecs, num_contents=10):
    # Get the interactions scores from the sparse person content matrix
    person_interactions = sparse_person_content[person_id,:].toarray()
    # Add 1 to everything, so that articles with no interaction yet become equal to 1
    person_interactions = person_interactions.reshape(-1) + 1
    # Make articles already interacted zero
    person_interactions[person_interactions > 1] = 0
    # Get dot product of person vector and all content vectors
    rec_vector = person_vecs[person_id,:].dot(content_vecs.T).toarray()
    

saranshrajput

import math
import pandas_datareader as web
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

saranshrajput

plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Date',fontsize=18)
plt.ylabel('Close Price USD ($)',fontsize=18)
plt.show()

saranshrajput

plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Date',fontsize=18)
plt.ylabel('Close Price USD ($)',fontsize=18)
plt.show()

saranshrajput

#Create a new dataframe with only the 'Close' column
data = df.filter(['Close'])#Converting the dataframe to a numpy array
dataset = data.values#Get /Compute the number of rows to train the model on
training_data_len = math.ceil( len(dataset) *.8) 

#Scale the all of the data to be values between 0 and 1 
scaler = MinMaxScaler(feature_range=(0, 1)) 
scaled_data = scaler.fit_transform(dataset)

saranshrajput

model = Sequential()
model.add(LSTM(units=50, return_sequences=True,input_shape=(x_train.shape[1],1)))
model.add(LSTM(units=50, return_sequences=False))
model.add(Dense(units=25))
model.add(Dense(units=1))

#Compiling the Model
model.compile(optimizer='adam', loss='mean_squared_error')

#Training the Modle

saranshrajput

#Test data set
test_data = scaled_data[training_data_len - 60: , : ]#Create the x_test and y_test data sets
x_test = []
y_test =  dataset[training_data_len : , : ] #Get all of the rows from index 1603 to the rest and all of the columns (in this case it's only column 'Close'), so 2003 - 1603 = 400 rows of data
for i in range(60,len(test_data)):
    x_test.append(test_data[i-60:i,0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))