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Stock Price Prediction Model

A Python application for predicting stock prices using machine learning, featuring data preprocessing, model training, and evaluation.

2 min readupdated 6 Oct 2026

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:

Shell
pip install pandas scikit-learn matplotlib yfinance

Getting Started

Create a Project

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

Write the Code

Stock Price Prediction ModelSource
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

Shell
python stock_price_prediction_model.py

What it produces

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

Text
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

Explanation

Key Features

  • Data Preprocessing: Cleans and prepares stock data.
  • Model Training: Trains a ML model for prediction.
  • Evaluation: Assesses model performance.
  • Error Handling: Validates inputs and manages exceptions.

Code Breakdown

  1. What it imports (lines 6–12)
Python
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
  1. load_data — the function (lines 14–19)
Python
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
  1. train_model — the function (lines 21–30)
Python
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
  1. plot_predictions — the function (lines 32–43)
Python
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()
  1. main — the function (lines 48–72)
Python
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()

The file defines 5 top-level symbols in all; the whole thing is above under Write the Code.

Features

  • Stock Prediction: Data preprocessing, model training, and evaluation
  • Modular Design: Separate functions for each task
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Integrating with more financial APIs
  • Supporting advanced ML models
  • Creating a GUI for prediction
  • Adding real-time analytics
  • Unit testing for reliability

Educational Value

This project teaches:

  • Financial Analytics: Stock prediction and ML
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Trading Platforms
  • Financial Analytics
  • Forecasting Tools

Checkpoint exercise

This checkpoint practises turning a price series into sliding-window features and fitting a linear model.

exerciseYour turn

Tab indents. Press Escape, then Tab, to leave the editor. Control plus Enter runs the code; Control plus Shift plus Enter submits it.

Output
Press Run to see what your code prints.

Conclusion

Stock Price Prediction Model demonstrates how to build a scalable and accurate stock prediction tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in finance, analytics, and more. For more advanced projects, visit Python Central Hub.

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