stock_market_prediction
- 0 Collaborators
Our project is to predict the stock market. It will collect the data sets from the previous stocks and shares. We will train the data and test it. After testing it will give a predicted output. ...learn more
Project status: Concept
Intel Technologies
DevCloud,
oneAPI,
Intel Python
Overview / Usage
Stock price analysis has been a critical area of research and is one of the top applications of machine Here, we used an LSTM network to train our model with Google stocks data and It will solve the pressure faced by the investors. In this mainly we implemented the Python libraries to execute the model by training the Google Stock Data sets using modules - sklearn, TensorFlow, Keras models. This model is concentrated on the single product eg:- Google Stocks. Using this product we can predict other different product as well.
Methodology / Approach
LONG SHORT TERM MEMORY NETWORK: LTSMs are a type of Recurrent Neural Network for learning long-term dependencies. It is commonly used for processing and predicting time-series data.
Three steps in LSTM:
a)The first step in LSTM is to decide which information to be omitted from the cell in that particular time step. It is decided with the help of a sigmoid function. It looks at the previous state (ht-1) and the current input xt and computes the function
b)There are two functions in the second layer. The first is the sigmoid function, and the second is the tanh function The sigmoid function decides which values to let through (0 or 1). The tanh function gives the weight-age to the values passed, deciding their level of importance from -1 to 1.
c)The third step is to decide what will be the final output. First, you need to run a sigmoid layer which determines what parts of the cell state make it to the output Then, you must put the cell state through the tanh function to push the values between -1 and 1 and multiply it by the output of the sigmoid gate.
The model can predict the trend of the actual stock prices very closely. The accuracy of the model can be enhanced by training with more data and increasing the LSTM layers.
**I am using the Intel OneAPI Extension for Sci-kit learn to pre-process the datasets used in the model to predict the Google stocks, it is used as an AI accelerator. Also, ** Focused on traditional machine learning tasks such as classification and regression.
Technologies Used
Machine Learning (ML) and Artificial Intelligence (AI): Machine learning algorithms, such as regression, random forests, support vector machines, and neural networks, are often used to analyze historical stock data and identify patterns and trends. Some of the python libraries - Keras layers(LSTM, Densa, Dropout), Regressor, scikit learn extension from Intel oneAPI.
For presentation I imported matplotlib.pyplot module, To make a clearful and a neat presentation.