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https://github.com/gauranshgoel123/predictive-demand-analysis
Demand Forecasting Project A web application for predicting future demand for part numbers based on historical data. Built with React for the frontend and FastAPI with Python for the backend, this application visualizes demand trends and allows users to input additional data for improved accuracy. In render analyzer is frontend analysis is backend
https://github.com/gauranshgoel123/predictive-demand-analysis
chartjs data-analysis data-science data-visualization dataset deployment full-stack machine-learning numpy pandas predictive-analysis prophet-model python reactjs render
Last synced: 3 days ago
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Demand Forecasting Project A web application for predicting future demand for part numbers based on historical data. Built with React for the frontend and FastAPI with Python for the backend, this application visualizes demand trends and allows users to input additional data for improved accuracy. In render analyzer is frontend analysis is backend
- Host: GitHub
- URL: https://github.com/gauranshgoel123/predictive-demand-analysis
- Owner: Gauranshgoel123
- Created: 2024-10-27T13:50:34.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-27T19:08:39.000Z (3 months ago)
- Last Synced: 2024-11-13T10:49:23.623Z (2 months ago)
- Topics: chartjs, data-analysis, data-science, data-visualization, dataset, deployment, full-stack, machine-learning, numpy, pandas, predictive-analysis, prophet-model, python, reactjs, render
- Language: Python
- Homepage: https://predictive-demand-analyzer.onrender.com/
- Size: 72.2 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predictive Demand Analysis
## Demand Forecasting Project
## Check out the live site at:
https://predictive-demand-analyzer.onrender.com/
## Overview
The Demand Forecasting Project is a web application designed to predict the future demand for part numbers based on historical data. It provides visualizations of predicted demand trends and allows users to input additional data for more accurate forecasts.
## Problem Statement
**Problem Statement 5: Predictive Analysis for Part Numbers**
Create a web application that predicts the future demand for part numbers based on historical data. The historical data might be sparse, so the prediction algorithm should be capable of working effectively with limited training data. The system should visualize the predicted demand trends and allow users to input additional data for more accurate predictions. The frontend is developed using React, while the backend is developed using Python.
## Tech Stack
- **Frontend**: React.js
- **Backend**: FastAPI (Python)
- **Data Handling**: Pandas
- **Deployment**: Render (Backend), Render (Frontend)
## Features
- Predict future demand based on historical data.
- Visualize demand trends with interactive plots.
- Allow users to add new data for improved prediction accuracy.
- User-friendly interface for seamless interaction.
## Installation
### Prerequisites
- Node.js and npm (for the frontend)
- Python 3.x (for the backend)
- A code editor (e.g., Visual Studio Code)
### Frontend Setup
1. Navigate to the frontend directory:
```bash
cd frontend
```
2. Install dependencies:
```bash
npm install
```
3. Start the development server:
```bash
npm start
```
### Backend Setup
1. Navigate to the backend directory:
```bash
cd backend
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
```
3. Activate the virtual environment:
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS/Linux:
```bash
source venv/bin/activate
```
4. Install dependencies:
```bash
pip install -r requirements.txt
```
5. Run the FastAPI server:
```bash
python main.py
```
## Deployment
- The backend is deployed on **Render**.
- The frontend is deployed on **Render**.
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue if you have suggestions or improvements.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Thank you to everyone who contributed to this project.
- Special thanks to the resources that helped in the development of this application.