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https://github.com/abhishekpandeyIT/StockMarketAnalysis.git
Stock market prediction is the act of trying to determine the future value of company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield a significant profit.
Stock Market prediction is a desirable task for the investors for their Good and successful investments in the Stock Market. Many investors are keen to know the future situation of the Stock Market.
In this Machine Learning Model I am using LSTM(Long Short Term Memory) approach, Recurrent Neural Networks(RNN) and using Python Standard Libraries such as Pandas, Numpy, Matplotlib, Keras and Scikit-learn.
In this project, I am using Reliance Industries India Data Set and Analyses its Stock Market status by using 2020 Dataset as its Test_Data. I found this data from "Kaggle.com".
The RNN is proved to be the most successful technique to predict sequential Data.
LSTM is one of the most successful RNN Architecture. LSTM is a variation of the RNN architecture. It works like a collection of memory cells(Flip Flops), with these cells, the network can effective associative memories and input remote on time. Hence, suit to grasp the structure of data dynamically over time with the prediction capacity.
a. 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.
b. Data Preprocessing: The pre-processing stage involves Data discretization, Data Transformation, Data CLeaning and Data Integration. After the Dataset is transformed into a clean dataset, the dataset is divided into a clean dataset. The dataset is divided into Training and Testing data sets to evaluate.
c. In this layer, only the feature which is to be fed to the neural network are chosen.
d. Training Neural Network: In this stage, the Data is fed to the neural network and trained for prediction assigning random biases and weights. 1. Optimizer: The type of optimizer used can greatly affect how fast the algorithm converges to the min. value. Here we have chosen to use Adam optimizer combines the perks of two other optimizers ADAgrad and RMSprop.
2. Regularization: Another important aspect of Training the model is making sure the weights do not get too large, hence overfit. For this purpose, we have chosen to use Tikhonov regularization.
3. Dropouts: Dropouts are used in making the neurons more robust and hence allowing them to predict the trend without focusing on any one neuron.
e. Output Generation: In this layer, the output value generated by the output layer of the RNN is compared with the tard=get value. The error or the difference between the target and the obtained output value is minimized by using a backpropagation algorithm.
f. Visualization: A rolling analysis of a time series model is often used to assess the model's stability over time. When analyzing financial time series data using a statistical model, a key assumption is that the parameters of the model are constant over time.
- The popularity of stock market prediction is growing rapidly as peoples are getting stocks and stock market. Therefore we need a highly accurate model for this prediction that's why we used RNN and LSTM approach
- This model will help many investors, businessmen by prediction future trends of Stock Market based on huge datasets.