Stock Price Prediction By Applying Machine Learning Techniques
- Resource Type
- Conference
- Authors
- Ahuja, Rakesh; Kumar, Yash; Goyal, Sumit; Kaur, Sarakshi; Sachdeva, Ravi Kumar; Solanki, Vikas
- Source
- 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) Emerging Smart Computing and Informatics (ESCI), 2023 International Conference on. :1-5 Mar, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Machine learning algorithms
Linear regression
Support vector machine classification
Companies
Forestry
Probability
Prediction algorithms
Linear Regression
Support Vector Regressor
Random Forest Regressor
Mean Absolute error
Stock Market
Root Mean Squared Error
Knowledge Engineering
- Language
Stock Market Prediction is affordable access to find the future scope of company stock or any financial exchange. The successful prediction of the stock will maximize the profit of the investors that are associated with the company. This research paper proposed algorithms based on knowledge engineering to envisage the stock price of a brand's dataset. Three most prominent regression techniques namely Support Vector(SVR), Random Forest(RFR) and Linear Regression have been used for predicting the stock price. The model proposed in this paper is based on the historical data of the company. These machine-learning algorithms are very popular and efficient for finding accurate results. This model does the prediction and compares its accuracy through the mean squared error(MSE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) to classify the better result.