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Logistic Regression Project

This repository contains the implementation of a Logistic Regression model to solve a classification problem.

Table of Contents

  1. Overview
  2. Installation
  3. Usage
  4. Deployment
  5. Contributing
  6. License

Overview

The goal of this project is to build a Logistic Regression model to predict the outcome of a given dataset. Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

Installation

  1. Clone the repository: git clone https://github.com/ctrl-Ravi/LogisticRegressionProject.git

  2. Install the required packages: pip install -r requirements.txt

Usage

  1. Run the Jupyter Notebook: jupyter notebook LogisticRegression.ipynb

  2. Execute the code cells in the notebook to train and evaluate the Logistic model.

Deployment

The model can be deployed using various cloud platforms like Heroku, AWS, or Google Cloud Platform.

Contributing

If you want to contribute to this project, feel free to fork the repository and submit a pull request with your proposed changes.

License

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