Determining sex of penguin with Logistic Regression

This repository contains a Jupyter Notebook implementation of logistic regression from scratch for determining the sex of penguins. Logistic regression is a fundamental machine learning algorithm used for binary classification tasks, and in this project, we'll be applying it to predict the sex of penguins based on certain features.

Project Overview The goal of this project is to build a logistic regression model to predict the sex of penguins using a set of input features. We'll be implementing the logistic regression algorithm from scratch using Python and Jupyter Notebook. The dataset used for this project contains information about various penguins along with their sex and several features such as bill length, bill depth, flipper length, and body mass.

Files and Folders Logistic_Regression_From_Scratch.ipynb: This is the main Jupyter Notebook containing the code implementation of logistic regression from scratch. It includes step-by-step explanations of the algorithm, data preprocessing, model training, and evaluation.

data: This folder contains the dataset files required for the project.

penguins.csv: The CSV file containing the penguin dataset with features and sex labels. images: This folder stores any images used in the Jupyter Notebook for visualization purposes.

Dependencies To run the Jupyter Notebook and reproduce the results, you will need the following dependencies: 1. Python 2. Jupyter Notebook 3. NumPy 4. Pandas 5. Matplotlib 6. Seaborn 7. Scikit-learn

You can install the required packages using the following command:

pip install jupyter numpy pandas matplotlib seaborn scikit-learn

Usage Clone this repository to your local machine using Git or download the file. Navigate to the project directory in your terminal.

License This project is licensed under the MIT License.

Feel free to use, modify, and distribute the code as needed.

Feel free to customize the README to include any additional information or instructions specific to your project. Make sure to provide clear explanations and details so that users can easily understand and replicate your work.