Prediction is a complicated and challenging process that plays the most important role in the stock market. The main idea of Stock market prediction is to gain significant profits. Stock Market Prediction involves the process of predicting the future value of the financial stock of the company. The successful prediction of a stock future price will maximize investor gains. The nature of the stock market is volatile, dynamic, and non-linear. It is necessary to predict the stock accurately. Machine learning algorithms make predictions based on the values of current stock market indications by training on their previous values. Linear regression, a powerful supervised machine learning algorithm used for predictions with a numeric target variable. In the stock market prediction process, one date variable and one closing price variable. The closing price variable is our independent variable, also be referred to as the target variable.
The linear regression method will predict based on the actual price and predicted stock price. Supervised classification algorithms such as Support Vector Machine(SVM), Decision Tree(DT), K-nearest neighbor (KNN), Random Forest (RF), and Regression algorithms such as Linear Regression and Logistic Regression (LR)are the most commonly used machine learning algorithms for stock market prediction. Recent development in stock market prediction is Stock closing price prediction using machine learning techniques for accurate predictions, stock market prediction using text mining, stock market growth and contraction prediction, stock market prediction for risk environment or financial crisis, and so on.
• Machine learning models make a stock market prediction easier and more authentic based on determining and predicting the future value of the financial stocks of a company with greater accuracy and reliability using learning algorithms.
• Machine learning techniques determine the future prices of the stocks of a company, and predictions depend on large amounts of data and are generally dependent on the long-term history of the market.
• Owing to the dynamic, unpredictable, and non-linear nature of the stock market and consistently changing stock values, improving the accuracy of the stock market prediction system by deploying a small dataset becomes a challenging task.
• Sentiment analysis through Machine Learning on stock market prediction becomes necessary, which affects the stock prices of a company significantly.