Stock Prediction using Machine Learning and Python | Machine Learning Training

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of stock's future price could yield significant profit.

Long Short Term Memory (LSTM) : LSTM (Long Short Term Memory ) are a variation of the RNN architecture. With these memory cells, networks can effectively associate memories and input remote in time, hence, suit to grasp the structure of data dynamically over time with high predication capacity.

 Stock Predication Model :

 RAW DATA :-  The historical stock data is collected from the google stock price and this historical data is used for the prediction of future stock prices.

Finally we are printing out the growth of the price and stock from 2012 to 2017 because up like this as you can see it has risen quite a lot over five years . 

And why would not it be we are talking about google and if i am not wrong google's parent company saw its stock price rise by almost 85 percent between 2014 to 2017  going from about eight hundred twenty  dollars to fifteen hundred nineteen in three years.

2. Data Preprocessing :-

The pre-processing stage involves Data discretization , Data transformation, Data cleaning, Data integration. After the dataset is transformed into a clean dataset, the dataset  is divided into training and testing sets to evaluate. 

3. Feature Extraction :- In this layer , only the features which are to be fed  to the neural network are chosen. 

4. Training Neural Network :-

In this stage, the data is fed to the neural network and trained for prediction assigning random biases and weights.

The stochastic descent update for ADAgrad is given by :

4.1 :- The type of optimizer used can greatly affect how fast the algorithm converges to the minimum value. Here we have chosen to use Adam optimizer. The Adam optimizer combines the perks of two other optimizers ADAgrad and RMSprop.