Stock Prediction Project Overview This repository contains a Python script (train.py) designed to process stock data and train a machine learning model for stock price prediction. The goal of this script is to individually handle CSV files for different companies, train a Long Short-Term Memory (LSTM) model on each company's data, and produce a graph of the predictions.

Script Details train.py The train.py script performs the following tasks:

Data Preparation:

Loads stock data from CSV files. Computes moving averages (MA50 and MA200). Scales the data using Min-Max Scaling. Model Training:

Creates an LSTM model to predict stock prices. Trains the model using a portion of the data and evaluates its performance. Model Selection:

Tries different hyperparameters (LSTM units, dense layer units, epochs) to find the best model based on RMSE (Root Mean Squared Error). Model Saving:

Saves the best-performing model to the models directory. Prediction Visualization:

Generates a graph of the predicted vs. actual stock prices. Dataset The dataset used in this project consists of CSV files where each file contains historical stock data for a company. The columns typically include:

Open: Opening price of the stock High: Highest price of the stock Low: Lowest price of the stock Close: Closing price of the stock Volume: Trading volume