Assur'Aimant: - Insurance Premium Prediction Web App

A professional Django application for predicting insurance premiums using machine learning, featuring user authentication, profile management, and interactive prediction capabilities.

Here is a snip of the general home page:

Here is a snip of the client home page:

Table of Contents

• Features
• Installation
• Directory Structure
• Database Schema
• Configuration
• Main Features
• Contributing
• License

Features 🚀

Core Functionality:

• User authentication with secure password hashing
• Profile management system
• Insurance premium prediction using lasso regression
• Responsive UI with Tailwind CSS
• SQLite database integration
• Dynamic notifications and form validation

Bonus Features:

• Prediction history tracking
• Advisor appointment system
• Interactive prediction preview
• Mobile-responsive design

Installation ⚙️

Prerequisites

• Python 3.9+
• pip
• Node.js (for Tailwind CSS)

Setup

# Clone repository
git clone https://github.com/MichAdebayo/brief_django_application.git
cd brief_django_application

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/MacOS
venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Set up Tailwind CSS
npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init -p

# Run migrations
python manage.py migrate

# Create admin user
python manage.py createsuperuser

# Run development server
python manage.py runserver

Directory Structure 📂

.
├── brief_app
│   ├── __init__.py
│   ├── asgi.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
├── insurance_app
│   ├── model
│   │   ├── model.pkl
│   │   └── model_1.pickle
│   ├── static
│   │   ├── css
│   │   ├── flatpickr
│   │   ├── images
│   │   └── js
│   ├── templates
│   │   └── insurance_app
│   ├── templatetags
│   │   ├── __init__.py
│   │   └── custom_filters.py
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── forms.py
│   ├── models.py
│   ├── tests.py
│   ├── urls.py
│   └── views.py
├── node_modules
│   ├── @alloc
│   ├── @isaacs
│   ├── @jridgewell
│   ├── @nodelib
│   ├── @pkgjs
│   ├── @tailwindcss
│   ├── autoprefixer
│   │   ├── bin
│   │   └── lib
│   ├── caniuse-lite
│   │   └── dist
│   ├── nanoid
│   │   └── bin
│   ├── postcss
│   │   └── lib
│   ├── postcss-value-parser
│   │   └── lib
│   └── source-map-js
│       └── lib
├── resumes
│   └── test.pdf
├── templates
│   └── base.html
├── theme
│   ├── static_src
│   │   ├── src
│   │   ├── package-lock.json
│   │   ├── postcss.config.js
│   │   └── tailwind.config.js
│   ├── __init__.py
│   └── apps.py
├── manage.py
├── package-lock.json
├── package.json
├── requirements.txt
├── test.py
└── test_numpy.py

Database Schema 🗄️

Main Features 🔍

Prediction Flow

• User logs in

• Navigates to the prediction page

• Form pre-filled with profile data

• Interactive preview updates in real-time

• Submit to get final prediction

• Results saved to history (bonus), which can be viewed in the user's home page through the "view estimate history" link

Security Measures

• Password hashing with PBKDF2

• CSRF protection

• Input validation

• Session-based authentication

Contributing 🤝

Create feature branch:

1. Copy

git checkout -b feature/your-feature

2. Commit changes

git commit -m "Add your feature"

3. Push to branch:

git push origin feature/your-feature

4. Open Pull Request (PR)

License 📄

This project is licensed under the MIT License.

Project Team:

Michael Adebayo | Eliandy Rymer | Dorothée Catry