chore(integration): submit Sales Prediction App using uAgents for Fetch.ai hackathon

[Shivaraj-Kolekar]

Sales Prediction App

Overview

This repository contains a Sales Prediction Application that utilizes Flask for the backend, uAgents for agent-based operations, and a custom-developed machine learning model for predicting future sales based on historical data. The application is designed to provide businesses with actionable insights into future sales trends, helping them make informed decisions.

Features

• Custom Sales Prediction Model: Developed using historical sales data, this model predicts future sales with high accuracy.
• Flask Backend: Manages API requests, handles data processing, and serves the model predictions.
• uAgents Integration: Uses uAgents for decentralized and scalable processing of sales data.
• CSV Upload: Users can upload their sales data in CSV format for analysis and prediction.

Installation

Prerequisites

Ensure you have the following installed on your system:

• Python 3.8 or higher
• pip (Python package manager)
• Flask
• uAgents
• (Any other dependencies, such as pandas, scikit-learn, etc.)

Setup Instructions

1. Clone the repository:

   git clone https://github.com/your-username/sales-prediction-app.git
   cd sales-prediction-app

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   
   

4. Run the Flask application:
   python app.py

uAgents Integration

This application uses uAgents for distributed processing, allowing it to handle large datasets and complex operations efficiently. The uAgents are configured to:

Receive and Process Data: Agents receive sales data from the Flask backend and preprocess it for prediction.
Model Execution: uAgents handle the execution of the custom prediction model, ensuring scalability and robustness.
Return Predictions: Processed predictions are sent back to the Flask backend for user access.

Usage

1. Uploading Data:

Navigate to the /upload endpoint to upload your CSV file containing historical sales data.
The CSV file should have columns like Date, Sales, Product_ID, etc., depending on the model's requirements.

2. Making Predictions:

After uploading the data, navigate to the /predict endpoint.
The model will process the data and return predictions for future sales.

3. Viewing Results:

Predictions can be viewed directly on the web interface or downloaded as a CSV file for further analysis.

Future Enhancements:

• Real-time Prediction: Implementing real-time sales prediction using streaming data.
• Advanced Visualization: Adding advanced data visualization features to the web interface.
• User Authentication: Securing the app with user authentication and role management.