Dash Analytics: Consumer Data Analysis And Visualization Project

A comprehensive data analytics and visualization platform built with Django and MongoDB, featuring real-time dashboards, predictive analytics, and customer behavior insights.

Features

• 📊 Interactive Dashboards
• 📈 Sales Trend Analysis
• 👥 Customer Behavior Analytics
• 🌍 Geographical Insights
• 📱 Responsive Design
• 🌓 Dark/Light Theme
• 📊 Data Upload & Management
• 🤖 ML-Powered Predictions

Tech Stack

• Backend: Django 4.2, Django REST Framework
• Database: MongoDB 8.0+ with MongoEngine
• Frontend: TailwindCSS 3.6+, Chart.js
• Data Analysis: Pandas 2.0+, NumPy 1.24+, Scikit-learn 1.3+
• ML Models: Prophet 1.1+

Prerequisites

• Python 3.12+
• MongoDB 8.0+
• Node.js 18+ (for TailwindCSS)
• Git

Installation

1. Clone the repository:

git clone <repository-url>
cd DashAnalytics

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r dash_analytics/requirements.txt

4. Setup MongoDB:

   a. MongoDB Atlas (Cloud):

   • Create a free MongoDB Atlas account at https://www.mongodb.com/cloud/atlas
   • Create a new cluster
   • In the "Network Access" tab, add your IP address
   • In the "Database Access" tab, create a new database user
   • Click "Connect" on your cluster, then "Connect your application"
   • Copy the connection string and update it in dash_analytics/dash_analytics/settings.py:

   MONGODB_ATLAS_URI = 'mongodb+srv://<username>:<password>@<cluster>.mongodb.net/<dbname>'

   b. MongoDB Compass (Local):

   • Install MongoDB locally from https://www.mongodb.com/try/download/community
   • Install MongoDB Compass from https://www.mongodb.com/try/download/compass
   • Open MongoDB Compass and connect to your local MongoDB instance
   • Add the connection string in URI in new connection

5. Run migrations:

cd dash_analytics
python manage.py makemigrations
python manage.py migrate

6. Create a superuser (admin):

python manage.py createsuperuser

7. Start the development server:

python manage.py runserver

The application will be available at:

• Main application: http://localhost:8000
• Admin interface: http://localhost:8000/admin
• API endpoints: http://localhost:8000/api/

Project Structure

• analytics/ - Data analysis and ML model endpoints
• api/ - REST API implementation
• core/ - Main application logic
• dataset/ - Sample data and datasets
• data_analysis_ml_code/ - Jupyter notebooks and ML scripts
• templates/ - Frontend templates
• static/ - Static assets

Team Members

• Nusrat Jahan Sumaiya - 22234103100
• Sadikul Amin Sadman - 22234103128
• Md. Faiyazur Rahman - 22234103093
• Md. Khairul Bashar Hasib - 22234103102
• Rakibul Hasan Rakib - 22234103096

Features in Detail

Data Analytics

• Sales performance tracking
• Customer segmentation
• Product performance analysis
• Geographical distribution analysis

Predictive Analytics

• Sales forecasting
• Customer churn prediction
• Product demand prediction
• Trend analysis

Visualization

• Interactive charts and graphs
• Real-time data updates
• Custom date range filters
• Export capabilities

Data Management

• CSV data import through web interface
• Automatic data validation and cleaning
• Historical data tracking with MongoDB
• Automated backup and restore features
• Sample dataset included in /dataset directory

API Documentation

The REST API is available at /api/ with the following endpoints:

• /api/customers/ - Customer data management
• /api/products/ - Product information
• /api/orders/ - Order processing and history
• /api/analysis/ - Analytics results
• /api/predictions/ - ML model predictions

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

This project is licensed under the MIT License.