Rice Yield Prediction Using Machine Learning

This project focuses on building a prediction model for rice yield in Bangladesh using the scikit-learn package in Python. The project demonstrates the process of creating a machine learning model, processing the dataset, training multiple regression models, and using them for prediction and evaluation.

Project Overview

Machine learning models are increasingly used in agriculture to improve crop management and yield predictions. In this project, we explore the process of building multiple machine learning regression models to predict rice yield based on various input features such as seasonal patterns, regional differences, and agro-environmental variables.

Getting Started

To run this project, you will need Python and the scikit-learn library installed on your system. You can run the project either online using a platform like Google Colab or on your local machine.

Prerequisites

Python: Version 3.6 or above

scikit-learn: Install via pip

Pandas: For data manipulation

Matplotlib: For visualization

Building a Model with Real-World Data

1. Processing the Data
2. Input Data: The program processes the data provided in a .csv file. The dataset includes various features like "Season", "State", and "Yield". Preprocessing: The data undergoes preprocessing, including one-hot encoding of categorical variables and standardization of numerical features.
3. Reading and Splitting the Data Data Loading: The .csv file is read, and the data is split into input features (X) and output target (y). Train-Test Split: The data is then split into training and test sets using an 80-20 ratio.
4. Training the Models The training input (X_train) and output (y_train) sets are used to train multiple machine learning models, including: Linear Regression Decision Tree Regressor K-Nearest Neighbors (KNN) Random Forest Regressor
5. Using the Models for Prediction Prediction: The test input set (X_test) is used to predict rice yield using each trained model. Evaluation: The predictions are compared to the actual values in the test output set (y_test). Evaluation metrics such as Mean Squared Error (MSE) and R-squared (R²) score are calculated.

Conclusion This project showcases how machine learning models can be applied to agricultural data for predicting crop yields. By using multiple regression models, we can identify the most effective approach for this specific problem, providing valuable insights for agricultural planning in Bangladesh.