Stock market prediction is the act of trying to determine the future values of a company stock or other financial instrument traded on an exchange. This project gives the estimation of the price of a company’s stock based on the history and helps the stakeholders to either invest on the stock or to take away their stock from the company. In this project, only Open price is considered for processing.
I have used the machine learning algorithms of regression in this project namely:
- Simple Linear Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression.
The above mentioned algorithms are used to predict stock prices using historical data. I have visualized the actual close price v/s predicted close price of the stocks for each of the model.
The dataset is taken from Kaggle. The dataset consists of 5306 rows and 15 columns.
Link - Dataset
This project uses the following software and Python libraries: