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The economy of a country is directly connected to the stock market. A country’s growth can also be determined through the stock market. Generally, the best prediction of the stock market will always help and guide investors to gain maximum profits. The prediction of the stock market can be made successful by carefully gathering and analyzing the previous stock market data history, which is previous year data. A large volume of stock market price data changes every minute. As we know very well that there are a lot of risks in the present stock market system where loss or gain is unexpected. Predicting stock is not usually an easy task, it's a close way of analyzing the behavior of stock market data series. Stock market prediction plays a vital role in determining the future value of company value.
Machine learning is considered as one of the branches in Artificial Intelligence to work automatically or to predict information or to give instructions to a system to perform an action. The aim of Machine Learning is to explain the structure of the data and fit that data into models that can be utilized by the researchers. This project work involves the analysis of various machine algorithms that are applied to stock market prediction. Applying Machine learning is a well-organized way to handle such situations. By applying Machine learning, one will be able to predict a market value close to the real value, which results in increased accuracy. Introducing Machine learning in the stock market prediction system has attracted many investors and researchers because of its accurate estimation.
Why: Problem statement
When the company’s growth in the share market is at peak the demand for jobs would be high and there will be a money flow between people and thus forming a chain forming dependency one over the other.This project helps the user to predict the stock price in future and also helps the user to decide in which company it would be feasible to invest.
How: Solution description
The three years' stock data has been collected using different internet sources. The factors considered are open, close, low, high and volume.
The aim is to build a predictive model and find out the stock value for the future year using LSTM.
Long Short Term Memory networks called “LSTMs” are a unique type of RNN, the ability to learn long-term dependencies. They work tremendously well on an enormous kind of problems and are now widely used.LSTMs are explicitly designed to avoid the long-term dependency problem. Saving and remembering information for a prolonged period of time is practically their default behaviour, not something they struggle with.
By employing machine learning algorithms, we classified the value of the stock market.
The proposed system uses three algorithms namely:
K- nearest neighbours is defined as a simple algorithm that stores all available cases and classifies new cases supported by a similarity measure (e.g., distance functions). KNN has been widely used in statistical predictions or estimation and also in pattern recognition. KNN is used as a non-parametric technique.
Decision Tree Algorithm
Decision Tree algorithm is from the family of supervised learning algorithms. Decision tree builds classification or regression models in the form of a tree structure. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too. The goal of using a Decision Tree is to create a training model that can be used to predict the class or value of target variables by learning simple decision rules inferred from prior data(training data). The topmost decision node which corresponds to the simplest predictor called the root node. Both categorical and numerical data can be handled by Decision trees.
Random forest is from the family of supervised learning algorithms which is utilized for both classifications including regression analysis. It is mainly used for classification problems. As we know, a forest is made up of trees and more trees means a more robust forest. Similarly, a random forest algorithm creates decision trees on data samples then gets the prediction from each of them and eventually selects the simplest solution by means of voting.
We deployed a predicted Machine learning model on the web through Flask.
How is it different from competition
The existing stock market price prediction uses decision tree to predict the high trend and low trend . Stock market is dynamic in nature and it is prone to change according to situations like political uncertainty, money flow, Natural Calamity, Job Market. etc.Since decision trees alone cannot predict these many factors it is impossible to predict the correct trend.
But the user interface has not been done. In the proposed system, we have used three algorithms. We also used the LSTM model to predict the value of the stock market. We deployed a Machine learning model on the web through Flask.