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README.md

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-# stock-market-analysis-and-forecasting
-Stock Market Analysis and Forecasting
+# 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](#project-overview)
+- [Datasets](#datasets)
+- [Methodology](#methodology)
+- [Modules](#modules)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Results](#results)
+- [Contributing](#contributing)
+- [License](#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:
+   ```bash
+   git clone https://github.com/yourusername/stock-market-analysis.git