📈 Stock Price Trend Predictor App (ML)

The Stock Price Trend Predictor App is a machine learning project designed to forecast stock price trends using historical data. Leveraging Long Short-Term Memory (LSTM) networks, the app provides users with insightful predictions and visualizations of stock performance.

🛠 Core Features

• Data Collection & Preprocessing:

  • The app fetches historical stock data using the yfinance API, ensuring up-to-date and reliable financial information.
  • Extensive data cleaning and manipulation are performed using Pandas and NumPy, including handling missing values, calculating moving averages, and normalizing the data for model input.

• LSTM Model for Prediction:

  • A robust LSTM model built with TensorFlow and Keras is used to predict stock price trends. The model is trained on 10 years of stock data, providing high accuracy with an average MAPE of less than 5%.
  • Data is scaled using MinMaxScaler from scikit-learn to optimize the LSTM network's performance.

• Visualizations:

  • Historical stock prices and moving averages are visualized using Matplotlib, allowing users to easily understand past performance and trends.
  • The app provides interactive plots that display both historical data and predicted trends, offering a comprehensive view of stock behavior.

• User Interface:

  • The model is deployed via Streamlit, providing a clean and intuitive user interface where users can input stock ticker symbols and view predictions.
  • The app displays the next day's estimated closing price, offering a practical tool for investors and analysts.

🛠 Tech Stack

• Machine Learning: TensorFlow, Keras
• Data Manipulation & Analysis: Pandas, NumPy
• Data Visualization: Matplotlib
• Data Scaling: scikit-learn
• Data Collection: yfinance API
• Deployment: Streamlit

📋 How to Run the Project Locally

Prerequisites

• Python 3.6 or higher
• Pip package manager

Installation and Initialization of the app

1. Clone the repository:

   git clone https://github.com/HetP1742431/Stock-Price-Trend-Predictor-App.git
   cd Stock-Price-Trend-Predictor-App

2. Install the required Python packages:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

4. Open your web browser and go to http://localhost:8501 to view the app.

Usage:

• Enter a Ticker Symbol: Start by entering the stock ticker symbol (e.g., AAPL, MSFT) in the provided input field.
• View Historical Data: The app will display the historical data and various financial indicators like moving averages.
• Predict Future Prices: The LSTM model predicts the next day's closing price, which is displayed along with historical trends.

💡 What I Learned

• Gained deep expertise in developing and optimizing LSTM networks for time series forecasting, particularly in the context of financial data.
• Enhanced skills in data preprocessing, including cleaning, normalization, and feature engineering, using powerful libraries like Pandas and NumPy.
• Developed proficiency in using TensorFlow and Keras for building and training deep learning models, and in evaluating model performance with metrics like MAPE.
• Improved problem-solving abilities through troubleshooting and fine-tuning the model, ensuring accuracy and reliability.
• Learned how to deploy a machine learning model using Streamlit, creating an interactive user interface that makes complex predictions accessible to users.

This project was a valuable experience that not only strengthened my technical skills in machine learning and data science but also enhanced my ability to deploy practical applications. I invite you to explore the project, check out the code, and see how machine learning can be applied to financial forecasting.

• Live Demo Video: Demo video.