Shagun-25

Array

• Two Sum
• Best Time to Buy and Sell Stock
• Contains Duplicate
• Product of Array Except Self
• Maximum Subarray
• Maximum Product Subarray
• Find Minimum in Rotated Sorted Array

Shagun-25

# Visualising the anomalies

plt.figure(figsize=(16, 6))
plt.plot(test_data[61:]['date'], test_data[61:]['price'].values, color = 'orange', label = 'Test Data')
plt.scatter(anamolies['date'], anamolies['price'], marker='*', s = 200, color='black', label='Anomaly')
plt.title('Anamoly Detection in Stock Prices')
plt.xlabel('Years')
plt.ylabel('NIFTY Index Prices')
plt.legend()
plt.show()

Shagun-25

plt.figure(figsize=(20, 5))
plt.plot(tweet_news['score'])
plt.xlabel('Years')
plt.ylabel('Sentiment Scores')

Shagun-25

from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
stopwords = set(STOPWORDS)

def show_wordcloud(data, title = None):
    wordcloud = WordCloud(
        background_color='white',
        stopwords=stopwords,
        max_words=200000,
        max_font_size=40, 

Shagun-25

#Function to Predict Price for Random 60 Consecutive Days

def prediction_multiple_days_quantized():

    #Loading Data
    data = pd.read_csv('data_processed_final.csv')
    with open('min_max.pickle', 'rb') as i:
        minmax = pickle.load(i)

    #Predicting

Shagun-25

#Function to Predict Next Day Index Price

def prediction_single_day_quantized(date):  #date: Enter date for which you want next day's price prediction.
    
    #Loading Data
    data = pd.read_csv('data_processed_final.csv')
    with open('min_max.pickle', 'rb') as i:
        minmax = pickle.load(i)

    #Predicting

Shagun-25

#Function to Predict Price for Random 60 Consecutive Days

def prediction_multiple_days():

    #Loading Data
    data = pd.read_csv('data_processed_final.csv')
    with open('min_max.pickle', 'rb') as i:
        minmax = pickle.load(i)

    #Predicting

Shagun-25

#Function to Predict Next Day Index Price

def prediction_single_day(date):  #date: Enter date for which you want next day's price prediction.
    
    #Loading Data
    data = pd.read_csv('data_processed_final.csv')
    with open('min_max.pickle', 'rb') as i:
        minmax = pickle.load(i)

    #Predicting

Shagun-25

run_model = tf.function(lambda x: model(x))

BATCH_SIZE = 64
STEPS = None
INPUT_SIZE = 1
concrete_func = run_model.get_concrete_function(tf.TensorSpec([BATCH_SIZE, STEPS, INPUT_SIZE], model.inputs[0].dtype))
MODEL_DIR = "./saved_model"

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]

Shagun-25

!rm -rf ./logs/
keras.backend.clear_session()
%load_ext tensorboard

model = Sequential()

# Adding the input layer
model.add(LSTM(units=128, activation='tanh', kernel_initializer=tf.keras.initializers.glorot_uniform(seed=26), input_shape = (trainX.shape[1], 1), unroll = True))

# Adding the output layer