ABSTRAT:

Stock market or Share market is one of the most complicated and sophisticated way to do business. Small ownerships, brokerage corporations, banking sector, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and pre-existing algorithms to help make this unpredictable format of business a little more predictable. We shall see how this simple implementation will bring acceptable results. The outcome is completely based on numbers and assumes a lot of axioms that may or may not follow in the real world so as the time of prediction

EXISTING SYSTEM :

You must select the appropriate model in which you will implement your math to produce results. The model selected or designed must be in conjunction with the input data. A wrong model designed or selected for an inappropriate data or vice-versa, will result in a garbage model which is completely useless. You must see for compatible SVM or some other available methods to process your data. Trying out different models simultaneously to check which works the most effectively is also a good practice. Furthermore, implementation is the simplest step of them all and should take the least amount of time so as to save us some time from the total time cost which could be utilized in some other important steps

EXISTING SYSTEM DISADVANTAGES:

1.LESS ACCURACY

2. LOW EFFICIENCY

PROPOSED SYSTEM :

Now the data is ready for us to input in a classifier. We will be using the simplest classifier i.e. Linear Regression, which is defined in Sk learn library of the Scikit-learn package. We choose this classifier because of its simplicity and because it serves our purpose just right. Linear regression is a very commonly used technique for data analysis and forecasting. It essentially uses the key features to predict relations between variables based on their dependencies on other features. [9] This form of prediction is known as Supervised Machine learning. Supervised learning is a method where we input labelled data i.e. the features are paired with their labels. Here we train the classifier such that it learns the patterns of which 199 combination of features result in which label. Here in our case, the classifier sees the features and simply looks at its label and remembers it. It remembers the combination of features and its respective label which in our case is the stock price a few days later. Then it moves on and learns what pattern is being followed by the features to produce their respective label. This is how supervised machine learning works

PROPOSED SYSTEM ADVANTAGES:

1.HIGH ACCURACY

2.HIGH EFFICIENCY

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10