Google Stock Price Prediction

This project predicts Google stock prices using a Random Forest classifier with data sourced from yfinance. It includes backtested predictors to enhance accuracy.

Overview

The goal of this project is to build a model that can predict the future movement of Google stock prices based on historical data.

Installation

To run this project, you need the following Python libraries:

• yfinance
• pandas
• scikit-learn

Install the necessary packages using:

pip install yfinance pandas scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/B1tW1z/basic-stock-price-predictor-model.git

2. Navigate to the project directory:

   cd <project-directory>

3. Run the prediction script:

Backtesting

The model includes backtesting to evaluate its performance. It simulates trades based on model predictions and compares the results to a simple buy-and-hold strategy.

Note

This is a very basic implementation of ml model created using yfinance and sklearn. If you have certain issues or want any changes

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