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Stock Price Prediction Using Linear Regression

Overview

This project demonstrates how to use Linear Regression to predict stock market Close Prices based on Open Prices using real-time stock data. The model is built using the scikit-learn library and evaluates its performance using common metrics like Mean Squared Error (MSE) and R-squared. A visualization of the regression line along with actual data points is also provided.

Table of Contents

• Overview
• Dataset
• Technologies Used
• Installation
• Usage
• Results
• Visualization

Dataset

The dataset is fetched in real-time from stock data APIs (like Yahoo Finance/Alpha Vantage API/IEX Cloud API) and contains the following columns:

• Datetime: Timestamp of the stock price
• Open: Opening price of the stock
• High: Highest price during the minute
• Low: Lowest price during the minute
• Close: Closing price of the stock at the end of the minute
• Adj Close: Adjusted closing price
• Volume: Volume of trades during the minute

We focus on predicting the Close price using the Open price.

Technologies Used

• Python (v3.8 or later)
• Pandas (for data manipulation)
• Matplotlib (for data visualization)
• Scikit-learn (for building the Linear Regression model)
• YahooFinance API (for fetching real-time stock data)

Installation

1. Clone the Repository

git clone https://github.com/SupremeEvilGod/ML3-Stock-Price-Prediction-Using-Linear-Regression.git
cd ML3-Stock-Price-Prediction-Using-Linear-Regression

2. Install Dependencies

Make sure you have Python installed. Then, install the required libraries using pip.

pip install -r requirements.txt

The requirements.txt file includes:

numpy
pandas
scikit-learn
matplotlib
yfinance

3. Fetch Real-Time Stock Data

You can modify the script to fetch data for your preferred stock ticker using the yfinance library.

import yfinance as yf

# Fetch data for a specific stock (e.g., MSFT)
data = yf.download(tickers="MSFT", period="1d", interval="1m")

Usage

1. Run the Model:

   You can execute the Python script or Jupyter notebook to fetch the stock data, build the Linear Regression model, and evaluate its performance.

2. Model Outputs:

   • Model Coefficient (Slope): How much the Close Price changes with respect to Open Price.
   • Model Intercept: Where the regression line intercepts the y-axis.
   • Mean Squared Error (MSE): A measure of the model's error (lower is better).
   • R-squared Value: Indicates how well the model explains the variance in the data.

Results

• Model Coefficients:

  • Coefficient (Slope): 0.971
  • Intercept: 6.313

• Performance Metrics:

  • Mean Squared Error (MSE): 0.0148
  • R-squared: 0.977

These metrics show that the model performs very well, with 97.7% of the variance in the Close Price explained by the Open Price.

Visualization

The scatter plot below shows the actual data points (in blue) and the predicted regression line (in red).