Stock Market Prediction system is used to predict stock price using Machine Learning technique called LSTM(long short-term memory), a time-series forecasting model which is based on Recurrent Neural Networks (RNN) and Python, where we have a dataset contain a lot of Google stock price from years 2012 to 2017.
The dataset used here is dowloaded from Kaggle
In this part we will see the project code divided to sections as follows:
Section 1 | Data Preprocessing : In this section we aim to do some operations on the dataset before training the model on it, processes like :
Load dataset Check for duplicates and remove them Check for missing data for each column Creating rolling mean values for 7 days
Section 2 | Model Creation : The dataset is ready for training, so we create a LSTM(long short-term memory), a time-series forecasting model which is based on Recurrent Neural Networks (RNN)is created
Section 3 | Model Evaluation : Finally we evaluate the model by getting accuracy, classification report and confusion matrix.