Agriculture plays a vital role in Pakistan’s economy, yet farmers often lack reliable data to make profitable crop-selling decisions. This project analyzes daily price and quantity data (2021–2025) for eight major crops across ten cities in Punjab, sourced from AMIS. We applied data analytics and developed a Random Forest model to predict future prices and suggest the best cities for selling crops. Despite data limitations, the project demonstrates how machine learning can empower farmers and improve decision-making in agricultural markets.
Keywords: Price prediction,Supply and Demand Analysis ,Time Series Forecasting,Random Forest,Location Prediction,Data Visualization
Tools: Python ,Google Colab,Scikit-Learn,Streamlit,Canva,Capcut,AMIS
Department: Department of Business Studies
Tools: Python ,Google Colab,Scikit-Learn,Streamlit,Canva,Capcut,AMIS
Department: Department of Business Studies
Project Poster
