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Stock Price Prediction Model

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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: pandaspandas, scikit-learnscikit-learn, matplotlibmatplotlib, yfinanceyfinance

Before you Start

Install Python and the required libraries:

Install dependencies
pip install pandas scikit-learn matplotlib yfinance
Install dependencies
pip install pandas scikit-learn matplotlib yfinance

Getting Started

Create a Project

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

Write the Code

⚙️ Stock Price Prediction Model
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.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()
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.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
Run stock price prediction
python stock_price_prediction_model.py

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. Import Libraries and Setup Data
stock_price_prediction_model.py
import pandas as pd
import yfinance as yf
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
stock_price_prediction_model.py
import pandas as pd
import yfinance as yf
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
  1. Data Preprocessing and Model Training Functions
stock_price_prediction_model.py
def get_stock_data(ticker):
    data = yf.download(ticker, period='1y')
    return data
 
def preprocess_data(df):
    return df.dropna()
 
def train_model(X, y):
    model = RandomForestRegressor()
    model.fit(X, y)
    return model
stock_price_prediction_model.py
def get_stock_data(ticker):
    data = yf.download(ticker, period='1y')
    return data
 
def preprocess_data(df):
    return df.dropna()
 
def train_model(X, y):
    model = RandomForestRegressor()
    model.fit(X, y)
    return model
  1. Evaluation and Error Handling
stock_price_prediction_model.py
def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    print(f"MSE: {mean_squared_error(y_test, y_pred)}")
 
def main():
    print("Stock Price Prediction Model")
    # data = get_stock_data('AAPL')
    # df = preprocess_data(data)
    # X, y = df.drop('Close', axis=1), df['Close']
    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    # model = train_model(X_train, y_train)
    # evaluate_model(model, X_test, y_test)
    print("[Demo] Prediction logic here.")
 
if __name__ == "__main__":
    main()
stock_price_prediction_model.py
def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    print(f"MSE: {mean_squared_error(y_test, y_pred)}")
 
def main():
    print("Stock Price Prediction Model")
    # data = get_stock_data('AAPL')
    # df = preprocess_data(data)
    # X, y = df.drop('Close', axis=1), df['Close']
    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    # model = train_model(X_train, y_train)
    # evaluate_model(model, X_test, y_test)
    print("[Demo] Prediction logic here.")
 
if __name__ == "__main__":
    main()

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

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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