An AI-powered Crop Minimum Support Price (MSP) Prediction Web App using XGBoost and Streamlit, with SMS alert and OTP verification via Twilio.
🌾 Crop MSP Prediction Web App This project predicts the Minimum Support Price (MSP) of agricultural crops based on historical data using machine learning (XGBoost). It includes a user-friendly Streamlit interface and SMS notifications (with OTP verification) for farmers using Twilio.
Features:- 1.Crop MSP prediction using XGBoost Regressor 2.Intuitive UI built with Streamlit 3.OTP-based phone verification using Twilio Verify API 4.SMS alerts to farmers with predicted MSP
Technologies Used:- 1.Python 2.Pandas, NumPy, Matplotlib, Seaborn 3.XGBoost, scikit-learn 4.Streamlit 5.Twilio SMS & OTP verification API
Model Training:- Model trained using: 1.GridSearchCV for hyperparameter tuning 2.R² Score for accuracy evaluation 3.Actual vs Predicted MSP visualization Files:- 1.crop_price.xlsx – Dataset 2.xgb_crop_model1.pkl – Trained model 3.crop_app.py – Streamlit app with Twilio integration 4.best_xgb_params.xlsx – Best parameters from GridSearchCV 5.msp_predictions1.xlsx – Actual vs predicted values
How to Run Clone the repo:
git clone https://github.com/your-username/crop-msp-prediction-app.git cd crop-msp-prediction-app
How to Run in Web Site:- streamlit run crop_app.py
Twilio Setup:- 1.Set your Twilio credentials and Verify SID in the script. 2.Use send_otp() to send OTP and check_otp() to verify. 3.After verification, the user receives the predicted MSP via SMS.