Top Class Stock Price Prediction Project through Machine Learning Algorithms for Google. Easy Understanding and Implementation.

Stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called "shares." A stock is a general term used to describe the ownership certificates of any company. Stock prices change everyday by market forces. By this we mean that share prices change because of supply and demand. If more people want to buy a stock (demand) than sell it (supply), then the price moves up. Conversely, if more people wanted to sell a stock than buy it, there would be greater supply than demand, and the price would fall. Understanding supply and demand is easy. So, why do stock prices change? The best answer is that nobody really knows for sure. Some believe that it isn't possible to predict how stocks will change in price while others think that by drawing charts and looking at past price movements, you can determine when to buy and sell. The only thing we do know as a certainty is that stocks are volatile and can change in price extremely rapidly.

We’ll dive into the implementation part of this Project soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis. Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. As you might have guessed, our focus will be on the technical analysis and visualization part. We’ll be using a dataset from Google stock Price test and train.

1.Using Sckiit Learning( Machine Learning model)

2.Data Preprocessing using dataset

3.Visualization of Dataset

4.Feature Scaling

5.Preparing the Datasets for training

6.Reshaping the datasets

7.Model development

8.Implementation of sequential.

9.Preprocessing the Data

10.Predicting the Output

11.Result visualization