Project 1 - Stock Market Prediction

This project was completed as part of an internship program at Nexus Info. The internship was conducted remotely. The objective of this project is to analyze and predict stock market trends using machine learning techniques. The dataset and associated files are used to build and evaluate models that can accurately predict stock prices based on various financial parameters.

Table of Contents

1. Introduction
2. Dataset
3. Requirements
4. Installation
5. Results

Introduction

Stock market prediction involves forecasting the future price of a company's stock or other financial instruments traded on an exchange. Accurate predictions can help investors make informed decisions and maximize their returns. This project leverages machine learning techniques to predict stock market trends based on historical data and various financial indicators.

Dataset

The dataset used in this project includes historical stock prices and financial indicators. It typically contains the following columns:

• Date: The date of the stock prices.
• Open: The opening price of the stock on a given day.
• High: The highest price of the stock on a given day.
• Low: The lowest price of the stock on a given day.
• Close: The closing price of the stock on a given day.
• Volume: The number of shares traded on a given day.
• Adj Close: The adjusted closing price of the stock on a given day.

The dataset can be sourced from various financial data providers such as Yahoo Finance, Alpha Vantage, etc.

Requirements

To run this project, you will need the following software and libraries:

• Python 3.x
• Jupyter Notebook
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

You can install the required libraries using the following command:

pip install pandas
pip install numpy
pip install scikit-learn
pip install matplotlib
pip install seaborn

Installation

1. Clone the repository:

https://github.com/himankgupta1/Project-1-Stock-Market-Prediction.git

2. Navigate to the project directory:

cd Project-1-Stock-Market-Prediction

3. Install the required libraries

Results

The results of the model evaluation are documented in the Jupyter Notebook. The model's performance is assessed using various metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Additionally, visualizations are provided to help understand the model's behavior and predictions.