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.
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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent