Predictive Analytics for Price Stabilization of Essential Commodities using AI/ML

This repository contains a predictive analytics solution aimed at stabilizing the prices of essential commodities using advanced AI/ML techniques. The project leverages time series forecasting models like PMD ARIMA and Prophet for accurate commodity price predictions and a Streamlit-based web application for user interaction.

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Features

• Machine Learning Models:
  • PMD ARIMA: Automatically optimizes ARIMA hyperparameters for accurate time series forecasting.
  • Prophet: A robust forecasting model designed for seasonal trends and long-term predictions.
• Interactive Web App: User-friendly interface powered by Streamlit (app2.py).
• Price Trends Analysis: Insights and visualizations to aid in stabilizing commodity prices.

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Folder Structure

• app2.py: Streamlit application for interaction with the predictive models.
• trained_model.zip: Contains the pretrained models and associated files.

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Requirements

• Python 3.7 or later
• Required libraries:
  • streamlit
  • pandas
  • numpy
  • pmdarima
  • prophet (formerly fbprophet)
  • Any other dependencies specified in your project

Getting Started

1. Clone the Repository

git clone https://github.com/YourUsername/Predictive-Analytics-for-Price-Stabilization.git
cd Predictive-Analytics-for-Price-Stabilization

2. Set Up the Environment

Install the necessary dependencies:

3. Extract the Model

Unzip the trained_model.zip into the project directory. Ensure the models are accessible in the same folder as app2.py.

4. Run the Application

Start the Streamlit app:

streamlit run app2.py