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Stock Forecasting Application

This project focuses on predicting stock prices using machine learning. Built with Python, it utilizes time series data to train models for forecasting future stock prices. The project explores multiple models, including Linear Regression and LSTM (Long Short-Term Memory), and provides an interactive interface using Streamlit.

Table of Contents

Project Overview

The Stock Forecasting Application leverages historical stock data to predict future stock prices, providing insights into potential market trends. Using data processing techniques and machine learning algorithms, this project explores stock price movement prediction and helps in visualizing these trends.

The primary objectives of this project are to:

  • Preprocess and visualize historical stock data.
  • Build and evaluate forecasting models.
  • Deploy an interactive Streamlit application for stock predictions.

Features

  • Data Ingestion and Preprocessing: Fetches stock data and cleans it for model training.
  • Data Visualization: Plots historical stock prices and trends.
  • Forecasting Models: Includes both Linear Regression and LSTM models for price prediction.
  • Interactive UI: Uses Streamlit to display model predictions interactively.

Installation

Prerequisites

  • Python 3.7+
  • Jupyter Notebook (for .ipynb file)
  • Streamlit

Steps

  1. Clone the repository to your local machine:

    git clone https://github.com/yourusername/stock-forecasting.git
cd stock-forecasting

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Run the application using Streamlit:

    streamlit run The\ stock\ forecast\ App_Project.ipynb

Usage

1.Launch the Streamlit App: Run the application command, which will open a local server. 2.Select Stock and Date Range: Use the interface to select a specific stock symbol and the date range. 3.Choose Model: Select between Linear Regression and LSTM. 4.View Predictions: The app will display visualizations for historical data and forecasted values.

Running the Jupyter Notebook

For in-depth analysis and customizations, open the Jupyter Notebook:

jupyter notebook The\ stock\ forecast\ App_Project.ipynb

Technical Details

This project uses two main forecasting models:

  1. Linear Regression: A simple model for stock forecasting based on historical trends.
  2. LSTM: A deep learning model specialized in handling sequential data, ideal for time series predictions.

Data is preprocessed, including scaling and splitting into training and testing datasets. Model training is based on historical closing prices, and predictions are visualized for easy analysis.

Model Selection and Evaluation

  • Linear Regression: Suitable for detecting linear trends but limited in handling complex time dependencies.
  • LSTM: A more advanced approach that can capture time-dependent patterns but requires extensive data and processing.

Performance metrics such as Mean Squared Error (MSE) are used to evaluate models.

File Structure

|-- stock-forecasting/
|   |-- The stock forecast 2 _project .py          # Main Python script
|   |-- The stock forecast App_Project.ipynb       # Jupyter Notebook with analysis and code
|   |-- README.md                                  # Documentation
|   |-- requirements.txt                           # Dependencies list

Requirements

  • numpy
  • pandas
  • scikit-learn
  • tensorflow (for LSTM)
  • streamlit
  • matplotlib
  • yfinance

To install all dependencies, use:

pip install -r requirements.txt

Future Improvements

  • Additional Models: Integrate more advanced models such as ARIMA or Prophet.
  • Enhanced UI: Add more interactive elements, including more detailed visualization options.
  • Real-Time Data: Incorporate real-time stock data fetching for live predictions.

Acknowledgments

Special thanks to the online communities and resources that provided insights into time series forecasting and stock market analysis.