Stock-Market-Live-Dataset

This project uses an LSTM (Long Short-Term Memory) neural network to predict the stock price of Tata Motors based on historical data. The data is fetched from Yahoo Finance, and the model is trained on the Close prices over a period from 2010 to 2024.

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Table of Contents

1. Project Overview
2. Technologies Used
3. Data Source
4. Model Architecture
5. How to Run the Project
6. Results
7. License

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Project Overview

The goal of this project is to predict future stock prices using a deep learning model (LSTM). The steps include:

• Fetching stock data using the Yahoo Finance API.
• Preprocessing and normalizing the data.
• Training an LSTM model.
• Predicting and visualizing future stock prices.

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Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Yahoo Finance API (yfinance)
• TensorFlow/Keras
• Scikit-Learn

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Data Source

The historical stock data for Tata Motors is retrieved using Yahoo Finance (yfinance). The data spans from January 1, 2010 to November 28, 2024.

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Model Architecture

The LSTM model consists of:

• 4 LSTM layers with varying units (50, 60, 80, 120)
• Dropout layers for regularization (0.2, 0.3, 0.4, 0.5)
• 1 Dense layer for output

Model Summary

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 100, 50)           10400     
_________________________________________________________________
dropout (Dropout)            (None, 100, 50)           0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 100, 60)           26640     
_________________________________________________________________
dropout_1 (Dropout)          (None, 100, 60)           0         
_________________________________________________________________
lstm_2 (LSTM)                (None, 100, 80)           45120     
_________________________________________________________________
dropout_2 (Dropout)          (None, 100, 80)           0         
_________________________________________________________________
lstm_3 (LSTM)                (None, 120)               96480     
_________________________________________________________________
dropout_3 (Dropout)          (None, 120)               0         
_________________________________________________________________
dense (Dense)                (None, 1)                 121       
=================================================================
Total params: 178,761
Trainable params: 178,761
Non-trainable params: 0
_________________________________________________________________

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How to Run the Project

Prerequisites

Ensure you have the following libraries installed:

pip install numpy pandas matplotlib yfinance scikit-learn tensorflow

Steps to Run

1. Clone the repository:

   git clone https://github.com/PranavKarwa2004/Stock-Market-Live-Dataset.git
   cd Stock-Market-Live-Dataset

2. Run the Python script:

   python stock_prediction.py

3. Output:

   The script will download the data, train the model, and produce a plot comparing the original and predicted stock prices.

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Results

The model predicts future Close prices based on historical data. Below is a sample output visualization:

• Blue Line: Original Prices
• Red Line: Predicted Prices

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License

This project is licensed under the MIT License. See the LICENSE file for details.