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Machine Learning Techniques for Detecting Phishing Websites

This project focuses on detecting phishing websites, a major cause of cybersecurity threats, using advanced machine learning techniques implemented in Python. The aim is to enhance web safety by accurately identifying and mitigating phishing risks.

Here's a step-by-step guide to set up and run a Django project:

1. Set Up Your Environment

  • Make sure you have Python installed (version 3.6 or later is recommended). You can download it from python.org.
pip install pip
  • Install Dependencies Install the required packages listed in the requirements.txt file. This file is typically found in the root of the project and contains all necessary packages.
pip install -r requirements.txt

2. Install Django

Once your virtual environment is activated, install Django using pip:

pip install django

3. Set Up Database (if applicable)

python manage.py migrate

4. Create a Superuser (Optional)

  • Default is a username and password is admin and admin
  • or
  • Follow the prompts to create the superuser account.
python manage.py createsuperuser

5. Run the Development Server Start the Django development server with:

python manage.py runserver

Unsplash API Setup Guide

This guide will help you set up and use the Unsplash API in your application.

Step-by-Step Guide

1. Create an Unsplash Account

  • Visit Unsplash and sign up for a new account if you don't have one.

2. Create an Application

  • After logging in, navigate to the Unsplash Developer page.
  • Click on the Your Apps tab.
  • Click on the New Application button.
  • Fill out the application form:
    • Application Name: Name your app.
    • Description: Describe what your app does.
  • Click Create Application.

3. Obtain Access Key and Secret Key

  • After creating your application, you will see your Access Key and Secret Key. Use the Access Key to authenticate your requests.
  • Keep your Secret Key confidential.

4. Read the API Documentation

5. Make API Requests

  • Use the Access Key to make API requests. Here’s an example in Python using the requests library( Use in features24.html , features25.html ):
In HTML(features25,features24)

const apiKey = 'use your api key';

Screenshot

Fig.1: Home

Fig.2: Indox

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A project that utilizes machine learning in Python to detect phishing websites, addressing a key issue in cybersecurity.

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