GitHub repo for Crop Yield Prediction using Decision Trees, SVR, KNN, and ensemble models (XGBoost, AdaBoost, Gradient Boosting). Features data preprocessing, model training, …

Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute …

Mar 23, 2025 · Crop Yield Prediction using various ML approaches - Random-Forest Regressor, Gradient-Boosting Regressor, Decision-Tree Regressor, Support-Vector Regressor

Robust crop yield prediction and climate impact assessment using machine learning. Models include Random Forest, Linear Regression, Decision Trees, Neural Networks, and Stacked …

In our we propose a model which focuses on predicting the crop yield in advance by analyzing factors like district (assuming same weather and soil parameters in a particular district), state, …

forest algorithm creates decision trees on different data samples and then predict the data from each subset and then by voting gives better the answer for the system.

decision trees are created in sequential form. Weights play an important role in XGBoost. Weights are assigned to all the independent variables which are then fed into the decision tree which …

Accurate crop prediction is essential to maximize yield and optimize resource usage, ensuring food security and economic stability. This paper presents an advanced model that integrates …

In this Machine Learning project, we develop a crop yield prediction using the Gradient Boosting algorithm with Python

Crop yield prediction is done by Random Forest regression and fertilizer prediction is done Decision Tree algorithm. Random Forest model was experimented with different types of …