Stock Price Prediction using LSTM

This repository contains Python code for predicting stock prices using Long Short-Term Memory (LSTM) neural networks. The code imports financial data, preprocesses it, builds an LSTM model, and makes predictions on the stock's closing prices.

Table of Contents

1. Prerequisites

2. Concepts and Techniques

Prerequisites

Before running the code, make sure you have the following prerequisites installed:

Python 3.x Libraries: yfinance pandas numpy matplotlib scikit-learn keras

Concepts and Techniques

This code demonstrates the following concepts and techniques:

Data Download:

Using the yfinance library to download historical stock price data for a specified company within a given date range.

Data Preprocessing:

Cleaning the data by resetting the index, handling missing values, and calculating daily returns.

Exploratory Data Analysis (EDA):

Visualizing the stock price, moving averages, volume, and daily returns using Matplotlib.

Feature Scaling:

Normalizing the stock price data using Min-Max scaling to make it suitable for training the LSTM model.

LSTM Model:

Building a Sequential Keras model with LSTM layers for time-series prediction. The architecture includes two LSTM layers with dropout and two dense layers.

Training and Validation: Splitting the data into training and validation sets, training the LSTM model, and making predictions.

Model Evaluation: Calculating the Root Mean Squared Error (RMSE) to assess the model's performance.

Visualization: Plotting the actual stock prices, model predictions, and cumulative returns.