Stock Price Predictor using Linear Regression Model

This project implements a stock price predictor using the Linear Regression model from the scikit-learn (sklearn) library in Python. It demonstrates a basic application of machine learning to financial data. Important: Please be aware that stock price prediction is inherently complex and influenced by many factors. This model is a simplified example and should not be used for actual investment decisions without further research and understanding. Past performance is not indicative of future results.

Table of Contents

1. Introduction
2. Features
3. Dependencies
4. Installation
5. Usage
6. Data Source
7. Model Explanation
8. Limitations
9. Contributing
10. License

Introduction

This project aims to provide a simple and understandable example of using linear regression to predict stock prices. Linear regression attempts to model the relationship between a dependent variable (stock price) and one or more independent variables (e.g., previous day's price, volume, etc.) by fitting a linear equation to observed data.

Features

• Linear Regression Model: Uses sklearn.linear_model.LinearRegression for prediction.
• Data Loading: Reads historical stock data from a CSV file.
• Feature Engineering (Basic): Demonstrates basic feature creation (e.g., using lagged values).
• Prediction: Predicts future stock prices based on the trained model.
• Evaluation: Provides basic evaluation metrics (e.g., Mean Squared Error). (Note: Implement evaluation if you have validation data and corresponding evaluation code)
• Visualization: Offers basic visualization of the actual vs. predicted prices. (Note: Implement visualization if you have visualization code)

Dependencies

• Python 3.6 or higher
• scikit-learn (sklearn)
• pandas
• numpy
• matplotlib (Optional, for visualization)

You can install these dependencies using pip:

Installation

1. Clone the repository:

   git clone https://github.com/Vishwathma2004/stock_price_predictor.git
   cd stock-price-predictor
   

2. (Optional) Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Linux/macOS
   venv\Scripts\activate.bat # On Windows
   

3. Install the dependencies (if you haven't already):

   pip install -r requirements.txt  #If you have a requirements.txt file
   # OR
   pip install scikit-learn pandas numpy matplotlib # If you don't have a requirements.txt
   

Usage

1. Prepare your data: Place your historical stock data in a CSV file (e.g., stock_data.csv). The CSV should at least contain a column representing the date and a column representing the closing price.

2. Modify the script:

   • Update the data_file variable in the Python script (main.py or similar) to point to your CSV file.
   • Adjust the feature engineering section to create features relevant to your data. The example likely uses lagged prices, but you can add other indicators.
   • Modify the target_column variable to specify the name of the column containing the closing price.

3. Run the script:

   python main.py  # Or the name of your Python script
   

4. Interpret the output: The script will output the predicted stock prices. (If you have implemented it, it will show also visualization and evaluation of the model.)

Data Source

Specify the source of the data used for training the model. Examples:

• Yahoo Finance
• Alpha Vantage
• IEX Cloud
• Your own custom data source

Include specific details on how to access the data and any required API keys or registration. For example: "Historical stock data was downloaded from Yahoo Finance using the yfinance library."

Model Explanation

This project uses Linear Regression to model the relationship between features (e.g., previous day's closing price) and the target variable (current day's closing price). The model learns a linear equation that best fits the training data. The equation takes the form:

Price(t) = b0 + b1 * Price(t-1) + b2 * Volume(t-1) + ...

where:

• Price(t) is the predicted price at time t
• b0 is the intercept
• b1, b2, ... are the coefficients for each feature
• Price(t-1) is the price at the previous time step
• Volume(t-1) is the volume at the previous time step

Limitations

• Linearity Assumption: Linear regression assumes a linear relationship between the features and the target variable. This assumption may not hold true for stock prices, which can be influenced by complex non-linear factors.
• Limited Features: The example likely uses a limited set of features. Real-world stock price prediction requires considering a much wider range of factors, including economic indicators, news sentiment, and company-specific information.
• Overfitting: The model may overfit the training data, leading to poor performance on unseen data. Techniques like regularization and cross-validation can help mitigate overfitting. These are not necessarily implemented in the basic example.
• Market Volatility: Stock markets are inherently volatile and unpredictable. No model can perfectly predict future prices.
• Simplified Model: This project is a simplified demonstration and should not be used for actual investment decisions.

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IMPORTANT DISCLAIMER: This project is for educational purposes only. It is not intended to provide financial advice. Stock price prediction is a complex and uncertain task. Do not use this model for making investment decisions without consulting a qualified financial advisor and conducting thorough research. The authors and contributors of this project are not responsible for any financial losses incurred as a result of using this model.