Stock Price Prediction Model

Abstract

Stock Price Prediction Model is a Python project that uses machine learning to predict stock prices. The application features data preprocessing, model training, and evaluation, demonstrating best practices in financial analytics and ML.

Prerequisites

Python 3.8 or above
A code editor or IDE
Basic understanding of ML and finance
Required libraries: pandas, scikit-learn, matplotlib, yfinance

Before you Start

Install Python and the required libraries:

Install dependencies

pip install pandas scikit-learn matplotlib yfinance

Getting Started

Create a Project

Create a folder named stock-price-prediction-model.
Open the folder in your code editor or IDE.
Create a file named stock_price_prediction_model.py.
Copy the code below into your file.

Write the Code

Stock Price Prediction Model

pch.viewSource

Stock Price Prediction Model

"""
Stock Price Prediction Model

This project builds a stock price prediction model using historical data and machine learning (scikit-learn). It demonstrates data loading, feature engineering, model training, prediction, and visualization. Includes CLI for training and prediction.
"""
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import argparse
import joblib
import os

def load_data(csv_path):
    if not os.path.exists(csv_path):
        print(f"Error: File {csv_path} not found.")
        return None
    df = pd.read_csv(csv_path)
    return df

def train_model(df, model_path=None):
    X = df[['Open', 'High', 'Low', 'Volume']]
    y = df['Close']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    if model_path:
        joblib.dump(model, model_path)
        print(f"Model saved to {model_path}")
    return model, X_test, y_test

def plot_predictions(model, X_test, y_test):
    predictions = model.predict(X_test)
    plt.figure(figsize=(10,5))
    plt.plot(y_test.values, label='Actual')
    plt.plot(predictions, label='Predicted')
    plt.xlabel('Sample')
    plt.ylabel('Stock Price')
    plt.title('Stock Price Prediction')
    plt.legend()
    plt.savefig("stock_price_prediction_model.png", dpi=120, bbox_inches="tight")
    print("saved stock_price_prediction_model.png")
    plt.show()

def predict(model, X):
    return model.predict(X)

def main():
    parser = argparse.ArgumentParser(description="Stock Price Prediction Model")
    parser.add_argument('--data', type=str, help='Path to CSV data file')
    parser.add_argument('--train', action='store_true', help='Train model')
    parser.add_argument('--model', type=str, default='stock_model.pkl', help='Path to save/load model')
    parser.add_argument('--predict', type=str, help='Path to CSV file for prediction')
    args = parser.parse_args()

    if args.train and args.data:
        df = load_data(args.data)
        if df is not None:
            model, X_test, y_test = train_model(df, args.model)
            plot_predictions(model, X_test, y_test)
    elif args.predict:
        if not os.path.exists(args.model):
            print(f"Model file {args.model} not found. Train the model first.")
            return
        model = joblib.load(args.model)
        df = load_data(args.predict)
        if df is not None:
            X = df[['Open', 'High', 'Low', 'Volume']]
            preds = predict(model, X)
            print("Predictions:", preds)
    else:
        parser.print_help()

if __name__ == "__main__":
    main()

Example Usage

Run stock price prediction

python stock_price_prediction_model.py

What it produces

Running the file exactly as it ships takes 3.5 s and prints:

python stock_price_prediction_model.py

usage: stock_price_prediction_model.py [-h] [--data DATA] [--train]
                                       [--model MODEL] [--predict PREDICT]
 
Stock Price Prediction Model
 
options:
  -h, --help         show this help message and exit
  --data DATA        Path to CSV data file
  --train            Train model
  --model MODEL      Path to save/load model
  --predict PREDICT  Path to CSV file for prediction

How it fits together

Read from the top: this is what runs when you execute the file, and which function calls which. It is generated from the code, so it cannot drift from it.

diagram Diagram mermaid

  flowchart TD
  RUN(["python stock_price_prediction_model.py"])
  load_data("load_data")
  train_model("train_model")
  plot_predictions("plot_predictions")
  predict("predict")
  main("main")
  RUN --> main
  main --> load_data
  main --> plot_predictions
  main --> predict
  main --> train_model
  plot_predictions --> predict

Explanation