Stock Market Analysis and Forecasting

This project focuses on analyzing and forecasting stock market trends using both fundamental and technical analysis techniques. Stock prediction is a challenging task with significant potential in guiding investment decisions. By exploring historical stock data and leveraging analysis techniques, this project aims to provide insights into stock price movements and trends.

Table of Contents

• Project Overview
• Datasets
• Methodology
• Modules
• Installation
• Usage
• Results
• Contributing
• License

Project Overview

The stock market is a collection of buyers and sellers trading ownership of companies through stocks (shares). This project explores methods of stock market prediction by using data from several major companies: Google, Microsoft, IBM, and Amazon.

Two primary approaches to stock market prediction are used:

1. Fundamental Analysis: Focuses on a company’s intrinsic value, analyzing factors such as market position, expenses, and growth rates.
2. Technical Analysis: Relies on historical stock data to identify patterns and predict future price movements.

In this project:

• Module I focuses on stock data analysis.
• Module II forecasts stock prices using machine learning models.

Datasets

The project uses the stock data of four major companies:

1. Google
2. Microsoft
3. IBM
4. Amazon

The datasets include various stock attributes, such as:

• Open
• High
• Low
• Close
• Volume

Methodology

The project is divided into two key parts:

1. Data Analysis:

   • Visualizing stock data trends (open, high, low, close, volume).
   • Identifying correlations between different attributes (e.g., close and open prices).
   • Exploring patterns, seasonality, and trends in stock prices.

2. Stock Forecasting:

   • Using machine learning models to forecast future stock prices based on historical data.
   • Models used for forecasting include:
     • ARIMA
     • LSTM (Long Short-Term Memory)

Modules

Module I - Analysis

• Objective: To analyze stock data from the provided datasets.
• Tasks:
  1. Visualize the distribution of opening and closing prices.
  2. Analyze the correlation between different attributes.
  3. Compare "High" and "Close" prices for each dataset.
  4. Explore trends and seasonality.

Module II - Forecasting

• Objective: To predict future stock prices using machine learning models.
• Tasks:
  1. Train and test forecasting models (e.g., ARIMA, LSTM) on stock data.
  2. Evaluate model performance and accuracy.

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/stock-market-analysis.git