Stock Price Prediction using Linear Regression

This project demonstrates how to predict stock prices using linear regression. The dataset used is for PAYTM stocks from 18th November 2022 to 18th November 2023.

Project Structure

• stock-price-prediction-using-linear-regression.ipynb: Jupyter notebook with the code for data analysis and model training.
• Quote-Equity-PAYTM-EQ-18-11-2022-to-18-11-2023.csv: The dataset used for this project.
• LICENSE.txt: License information.
• stock_price_prediction_using_linear_regression.py: Python script for stock price prediction.

Installation

1. Clone the repository:

   git clone https://github.com/devdattatalele/Stock-price-prediction.git

2. Navigate to the project directory:

   cd Stock-price-prediction

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Open the Jupyter notebook stock-price-prediction-using-linear-regression.ipynb to explore the data and model training process.

2. Alternatively, you can run the Python script stock_price_prediction_using_linear_regression.py:

   python stock_price_prediction_using_linear_regression.py

Data

The dataset Quote-Equity-PAYTM-EQ-18-11-2022-to-18-11-2023.csv contains the following columns:

• Date
• Series
• Open
• High
• Low
• Previous Close
• Last Traded Price (LTP)
• Close
• VWAP
• 52 Week High
• 52 Week Low
• Volume
• Value
• Number of Trades

Results

The project includes:

• Data analysis and visualization
• Training a linear regression model to predict stock prices
• Evaluation of the model's performance

License

This project is licensed under the MIT License. See the LICENSE.txt file for more details.

Contact

Created by Devdatta Talele.

• Email: taleledevdatta@gmail.com
• LinkedIn: linkedin.com/devdatta-talele

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