Stock Prediction

This repository contains code for predicting stock prices using various machine learning models. The models implemented include Linear Regression, SVM Regression, KNN Regression, Kernel Ridge Regression, and Ridge Regression. The root mean square error (RMSE) values for each model are as follows:

• Linear Regression: 7.70
• SVM Regression: 7.86
• KNN Regression: 10.666
• Kernel Ridge Regression: 7.701
• Ridge Regression: 7.708

Dataset

The dataset used for training and testing the models consists of historical stock price data. It contains the following columns:

• Date: The date of the stock price data.
• Open: The opening price of the stock.
• High: The highest price of the stock during the day.
• Low: The lowest price of the stock during the day.
• Close: The closing price of the stock.
• Volume: The trading volume of the stock.

Libraries Used

The following machine learning libraries were used in this project:

• scikit-learn (sklearn): Used for implementing the machine learning models.
• matplotlib: Used for visualizing the data and model predictions.
• numpy: Used for numerical operations and array manipulation.
• seaborn: Used for creating statistical visualizations.