https://github.com/ebadshabbir/logistic_regression-binomial-

Logistic Regression on Social Network Ads Dataset This project applies Logistic Regression to predict whether a user will purchase a product based on their age and estimated salary, using the Social Network Ads dataset. The data is split into training and test sets, with feature scaling applied for normalization.
https://github.com/ebadshabbir/logistic_regression-binomial-

classification jupyter-notebook logistic-regression machine-learning matplotlib-pyplot numpy pandas python sklearn

Last synced: 20 days ago
JSON representation

Logistic Regression on Social Network Ads Dataset This project applies Logistic Regression to predict whether a user will purchase a product based on their age and estimated salary, using the Social Network Ads dataset. The data is split into training and test sets, with feature scaling applied for normalization.

• Host: GitHub
• URL: https://github.com/ebadshabbir/logistic_regression-binomial-
• Owner: EbadShabbir
• Created: 2024-10-17T09:23:55.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-31T08:05:55.000Z (2 months ago)
• Last Synced: 2024-12-19T02:23:56.765Z (20 days ago)
• Topics: classification, jupyter-notebook, logistic-regression, machine-learning, matplotlib-pyplot, numpy, pandas, python, sklearn
• Language: Jupyter Notebook
• Homepage: https://kaggle.com/code/ebadshabbir/logistic-regression-binomial
• Size: 69.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Logistic Regression on Social Network Ads Dataset

This project implements a **Logistic Regression** model using the Social Network Ads dataset to predict whether a user will purchase a product based on their age and estimated salary. The model is trained and tested on a split of the dataset, and its performance is visualized with confusion matrices and decision boundary plots for both the training and test sets.

## Table of Contents

- [Overview](#overview)

- [Dataset](#dataset)

- [Installation](#installation)

- [Usage](#usage)

- [Results](#results)

- [Contributing](#contributing)

- [License](#license)

## Overview

This project demonstrates:

- Loading and preprocessing the data.

- Splitting the dataset into training and test sets.

- Applying **feature scaling** to normalize the features.

- Training a **Logistic Regression** model.

- Visualizing decision boundaries for both the training and test sets.

## Dataset

The dataset is the Social Network Ads dataset, which includes the following columns:

- **User ID**: Unique identifier for each user.

- **Gender**: Gender of the user.

- **Age**: Age of the user.

- **Estimated Salary**: Estimated salary of the user.

- **Purchased**: Whether the user purchased the product (0 or 1).

The dataset can be found on Kaggle: [Social Network Ads](https://www.kaggle.com).

## Installation

To run this code, follow these steps:

1. Clone the repository:

   ```bash

    https://github.com/EbadShabbir/Logistic_Regression-Binomial-

   

   

pip install -r requirements.txt

python logistic_regression.py

### Notes:

- Update the dataset download URL and any additional details if required.

- Add a `requirements.txt` with the necessary Python packages:

  ```text

  pandas

  matplotlib

  numpy

  scikit-learn