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Stock Price Prediction Model

A powerful and efficient Stock Price Prediction Model that forecasts future stock prices using Machine Learning / LSTM techniques.

PyTorchlstm
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Stock Price Prediction with LSTM

A deep learning-based forecasting system using Long Short-Term Memory networks to predict stock prices from historical data.

1,557
Training Days
80/20
Train/Test Split
200
Training Epochs
30
Sequence Length

Overview

This project implements a stock price prediction model using LSTM (Long Short-Term Memory) networks, specifically designed to capture long-term dependencies in sequential financial data. The model is trained on historical closing prices and uses a 30-day window to predict the next day's price.

~5.49
Training RMSE
~10.10
Testing RMSE

Tech Stack

PythonPyTorchPandasNumPyscikit-learnyfinanceMatplotlib

Model Details

Sequence Length: 30 Days
Hidden Dimension: 32
LSTM Layers: 2
Learning Rate: 0.01
Optimizer: Adam
Loss Function: MSELoss
Epochs: 200

Features

  • →LSTM Neural Network Architecture
  • →30-day Sequence Learning
  • →Real-time Data from Yahoo Finance
  • →Standard Scaler Preprocessing
  • →RMSE Performance Metrics
  • →80/20 Train-Test Split