  This paper presents price prediction models using Machine Learning algorithms
augmented with Superforecasters predictions, aimed at enhancing investment
decisions. Five Machine Learning models are built, including Bidirectional
LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM
and GRU algorithms. The models are evaluated using the Mean Absolute Error to
determine their predictive accuracy. Additionally, the paper suggests
incorporating human intelligence by identifying Superforecasters and tracking
their predictions to anticipate unpredictable shifts or changes in stock prices
. The predictions made by these users can further enhance the accuracy of stock
price predictions when combined with Machine Learning and Natural Language
Processing techniques. Predicting the price of any commodity can be a
significant task but predicting the price of a stock in the stock market deals
with much more uncertainty. Recognising the limited knowledge and exposure to
stocks among certain investors, this paper proposes price prediction models
using Machine Learning algorithms. In this work, five Machine learning models
are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU
and the last one is built using LSTM and GRU algorithms. Later these models are
assessed using MAE scores to find which model is predicting with the highest
accuracy. In addition to this, this paper also suggests the use of human
intelligence to closely predict the shift in price patterns in the stock market
The main goal is to identify Superforecasters and track their predictions to
anticipate unpredictable shifts or changes in stock prices. By leveraging the
combined power of Machine Learning and the Human Intelligence, predictive
accuracy can be significantly increased.
